The Harpreet Podcast - Episodes Tagged with “Data Science”

Harnessing AI Agents with Abi Aryan

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

Abi Aryan, a self-taught computer scientist and machine learning engineer, joins us for an enlightening Ask-Me-Anything session on "The Deep Learning Podcast by Deci."

Key Highlights:

Guest Introduction: Meet Abi Aryan, a self-taught computer scientist, and machine learning engineer, as she shares her extensive experience in leveraging AI for smarter software systems development.

Challenges in MLOps: The discussion kicks off with a deep dive into the challenges of MLOps, exploring computational resources, industry distribution, and nuances of data collection and labeling.

Market Landscape: Aryan provides insights into the market landscape, highlighting the transformative role of large language models (LLMs) in diverse industries such as e-commerce, insurance, media, and entertainment.

Transition to MLOps and LLMops: Explore the transition from MLOps to LLMops, understanding the unique challenges and future prospects in the development and deployment of large language models.

Q&A Session: Engage in a dynamic Q&A session where Aryan addresses audience questions, covering topics such as challenges in LLM development, incorporating AI agents into software services, evaluating models, and the balance between fine-tuning and prompt engineering.

Applications in Legal Research: Uncover the applications of LLMs in legal research and document analysis, showcasing their potential impact on enhancing efficiency and accuracy in the legal domain.

Choosing the Right Framework: Aryan shares insights into the considerations for choosing the right framework for LLM deployment, offering practical tips for ensuring seamless integration and performance.

Future of Libraries and Computer Vision Models: Gain a glimpse into the future with discussions on libraries like LangChain, the potential emergence of computer vision-focused models, and considerations for running LLM applications on low-level hardware.

Cost Considerations and Career Trajectories: The session concludes with considerations on cost in training models, developing Minimum Viable Products (MVPs), discussions on different roles in the AI space, and insights into potential career trajectories.

Join us in this enlightening conversation with Abi Aryan as she demystifies large language models, offering profound insights into their challenges, applications, and the exciting future they hold in the ever-evolving landscape of artificial intelligence.

Building a World Where Machines Can See with Kausthub Krishnamurthy

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

Join us in this insightful podcast-style interview with Kausthub Krishnamurthy, a Senior Manager and Machine Learning Engineer at Nearmap, as we explore the fascinating world of robotic vision within deep learning. Kausthub shares his journey from modular cube flow pipelines to developing data pipelines and training models for computer vision at Nearmap, highlighting the multidisciplinary nature of robotics that intertwines machine learning, computer vision, software engineering, and robotics.

Key Highlights:

Robotic Vision and Machine Learning: Delve into the complexities of robotic vision, comparing classical computer vision techniques with deep learning methods, and discussing their applications in automation, field robotics, and cloud machine learning.

Design Considerations: Understand the design considerations for integrating machine learning into robotics, addressing challenges related to real-time data processing, connectivity, hardware-software ecosystem, and the evolving roles within robotic vision and sensing.

Simulation-Driven Development: Explore the importance of simulation-driven development in robotics, leveraging tools like ROS and Moose, and the role of agile development approaches in shaping the future of robotics.

Career Paths and Continuous Learning: Gain insights into career paths in robotics beyond engineering, the vital role of simulation in robotics training, and tips for continuous learning and career advancement in the field.

Project Ideas and Internship Tips: Discover project suggestions and internship tips for aspiring robotics professionals, and considerations regarding data privacy and safety in the context of consumer-direct robotics use.

Embark on this enlightening conversation with Kausthub Krishnamurthy as he unravels the intricacies of robotic vision, machine learning, and the future of robotics in the dynamic landscape of technology and innovation.

Music Generation Using AI with Dr. Tristan Behrens

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

In this episode we get into the captivating realm of AI-driven creativity with Dr. Tristan Behrens, an AI advisor, musician, and freelance researcher. Join us as we explore the transformative power of artificial intelligence in unlocking creativity, focusing on Dr. Behrens' expertise in using AI to generate music through his machine, Hexagon.

Key Points:

Guest Introduction: Dr. Tristan Behrens, an AI advisor and researcher, shares his unique journey from software development to a Ph.D. in computer science and AI.

Computation and Creativity: The episode begins by unraveling the intricate relationship between computation and creativity, highlighting the fusion of technology and artistic expression.

AI in Music Composition: Dr. Behrens discusses the process of training AI models on diverse music genres using MIDI data, employing the Transformer architecture and a complex token vocabulary for music track generation.

Credit in AI-Augmented Creativity: The discussion touches upon the evolving role of AI in augmenting human creativity, acknowledging the importance of giving credit to both AI and human contributors.

Transformers in AI: Understanding the role of Transformers in AI, particularly in converting text to music, showcases the complexity and versatility of modern AI architectures.

Data Pipeline and Modeling: Dr. Behrens provides insights into building the AI model, emphasizing the significance of a robust data pipeline and thoughtful modeling.

AI Music Creation Process: Explore the intricacies of converting text to sound, accompanied by Dr. Behrens' firsthand experiences with neural network outputs.

Challenges and Role of Symbolic AI: Delve into the challenges of AI in music generation and the potential influence of Symbolic AI in shaping the future of creative AI applications.

Future Architectures: A glimpse into the future unfolds as Dr. Behrens discusses the evolving landscape of AI architectures and their impact on creative endeavors.

Deep Reinforcement Learning: Uncover the potential role of deep reinforcement learning in pushing the boundaries of AI music generation.

Challenges of Deep Learning in Creativity: The episode concludes by addressing the challenges inherent in integrating deep learning into the augmentation of human creativity.

Join us in this enlightening conversation with Dr. Tristan Behrens as we navigate the fascinating intersection of artificial intelligence and creativity, unlocking new possibilities in the realm of AI-generated music.

Graph Neural Networks with Kyle Kranen

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

Kyle Kranen, a senior deep learning algorithm engineer at Nvidia, despite graduating from UC Berkeley in 2020, has nearly a decade of experience in Deep Learning. It shines through as he demystifies the intricacies of graph neural networks, providing a unique perspective shaped by technical internships and a current focus on implementing and optimizing state-of-the-art deep learning models.

Key Highlights:

Guest Introduction: Meet Kyle Kranen, a senior deep learning algorithm engineer at Nvidia, as he shares his wealth of experience and insights into the world of graph neural networks.

Power of Graphs in Data Representation: Explore the significance of proper data structures in machine learning and delve into how graph neural networks have overcome challenges in handling complex relationships within data.

Graph Anatomy: Uncover the intricacies of graphs, examining their role as a powerful tool for data representation and understanding their ubiquitous presence in various domains.

Local Aggregation in Graphs: Kyle introduces the concept of local aggregation in graphs, shedding light on its importance and its role in enhancing the capabilities of graph neural networks.

Message Passing: Gain a deeper understanding of the importance of message passing in graph neural networks, a fundamental mechanism for information exchange and aggregation.

Graph Neural Network Architecture: Navigate the anatomy of a graph neural network, exploring its basic building blocks and the significance of learnable parameters in capturing complex relationships.

Predictive Power: Discover the predictive power of graphs, exploring graph-level, node-level, and edge-level predictions, along with insights into representing the 'blobbiness' or unstructured nature of a graph.

Edge Classification and Graph Isomorphism: Kyle delves into specific challenges such as edge classification and the graph isomorphism test problem, providing nuanced perspectives on tackling these issues.

Popular Architectures: Explore the landscape of popular architectures for graph neural networks, understanding the diversity of approaches that cater to different applications.

Production Pipelines: Gain insights into the production pipelines for graph neural networks, unraveling the practical aspects of deploying these models in real-world scenarios.

Advantages of Graph Learning: The episode concludes with an exploration of the advantages of graph learning, highlighting the transformative potential of leveraging graph neural networks in diverse domains.

Join us in this comprehensive discussion as Kyle Kranen demystifies the realm of Graph Neural Networks, offering profound insights into their applications, challenges, and the immense potential they hold in reshaping the landscape of deep learning.

Production Machine Learning and MLOps with Josh Tobin

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

Josh Tobin, co-founder and CEO of Gantry, shares from his extensive experience, including a PhD in Computer Science at UC Berkeley and his role as a research scientist at OpenAI, Tobin provides valuable insights into the transition of ML from academic research to real-world applications.

Key Highlights:

Guest Introduction: Meet Josh Tobin, as he shares his journey from academia to entrepreneurship, highlighting his expertise in MLOps and the practical aspects of deploying ML models in production.

ML in Production: Explore the significant differences between ML in a research setting and ML in production, emphasizing the importance of integrating ML models within broader product systems.

Emerging Trends: Tobin discusses the emerging field of MLOps, the impact of foundational models like GPT-3 on ML operations, and the nuanced challenges of deploying AI systems in real-world scenarios.

Practical Considerations: Gain insights into practical aspects of ML in industry, including experiment management, feature stores, and the complexities of integrating state-of-the-art models into production systems.

Future Outlook: Tobin offers advice for practitioners and businesses navigating the AI transformation, stressing the collaborative potential between humans and AI and underlining the critical role of prompt engineering in the next generation of AI applications.

Join us in this engaging conversation with Josh Tobin, as we explore the dynamic landscape of ML research, production, and the future trends shaping the field of artificial intelligence and machine learning.

Vision AI, AGI and YOLOv5 with Glenn Jocher

Harpreet Sahota — Wed, 12 Jun 2024 00:00:00 -0400

Glenn Jocher, the founder of Ultralytics, unveils the journey behind YOLOv8 and discusses the future of object detection. As a pioneer in AI and the mastermind behind the renowned YOLO (You Only Look Once) object detection algorithms, Jocher shares invaluable insights and experiences in this insightful AMA session.

Key Highlights:

Origins of YOLOv8: Explore the evolution of YOLO models, from YOLOv3 to YOLOv8, as Jocher reveals the technical advancements and innovations driving the development of these groundbreaking object detection algorithms.

Community Contributions: Learn about the pivotal role of open-source contributions and community collaboration in the success of YOLOv8, showcasing the power of collective intelligence in pushing the boundaries of AI vision systems.

Technical Insights: Delve into the technical intricacies of YOLOv8, including architecture changes, loss functions, and the transition from anchor-based to anchor-free systems, offering a deeper understanding of the underlying mechanisms driving object detection.

Wide Applications: Discover the diverse range of applications of YOLO models, from flaw detection in manufacturing to aiding visually impaired individuals, highlighting the versatility and real-world impact of these cutting-edge AI technologies.

Future Directions: Gain insights into the future of YOLOv8 and beyond, including plans for mobile deployment, architectural improvements, convergence with NLP, and optimization strategies for custom datasets, paving the way for advancements in AI-driven object detection and computer vision.

Embark on this enlightening journey with Glenn Jocher as he unravels the intricacies of YOLOv8 and shares his vision for the future of object detection in the ever-evolving landscape of artificial intelligence.

The Final Data Science Happy Hour 02DEC2022

Harpreet Sahota — Sun, 04 Dec 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=-NZbXGoj2bQ

Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark

The Artists of Data Science Social links:
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YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
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Facebook https://facebook.com/TheArtistsOfDataScience
Twitter: https://twitter.com/datascienceharp

Data Science Happy Hour 103 | 18NOV2022

Harpreet Sahota — Sun, 20 Nov 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/eHIlY01n5LI

Data Science Happy Hour 102 | 11NOV2022

Harpreet Sahota — Sun, 13 Nov 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=xWugtCTnWbw

Data Science Happy Hour 101 | 04NOV2022

Harpreet Sahota — Sun, 06 Nov 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=D5QCcfi7acc

Data Science Happy Hour 100 | 14OCT2022

Harpreet Sahota — Sun, 16 Oct 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/6YN5p6T7vys

Data Science Happy Hour 99 | 07OCT2022

Harpreet Sahota — Sun, 09 Oct 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=C_DBrPdh4Jw&ab_channel=HarpreetSahota%7CTheArtistsofDataScience

Data Science Happy Hour 98 | 30SEP2022

Harpreet Sahota — Sat, 01 Oct 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=bFy64aMh_ho

Data Science Happy Hour 97 | 16SEP2022

Harpreet Sahota — Sun, 18 Sep 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/NDVD28raDqw

Data Science Happy Hour 96 | 09SEP2022

Harpreet Sahota — Sun, 11 Sep 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=G78EE7P-EV8

Data Science Happy Hour 95 | 02SEP2022

Harpreet Sahota — Sun, 04 Sep 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=KE_i6puujLo

Data Science Happy Hour 94 | 26AUG2022

Harpreet Sahota — Sun, 28 Aug 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/zn0XHbXrhjs

Data Science Happy Hour 93 | 19AUG2022

Harpreet Sahota — Sun, 21 Aug 2022 00:15:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/wVsszZLrl6g

Data Science Happy Hour 92 | 13AUG2022

Harpreet Sahota — Sat, 13 Aug 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/uYWKVCdNjPk

Data Science Happy Hour 91 | 07AUG2022

Harpreet Sahota — Sun, 07 Aug 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=Wt0w92QYIPg

Data Science Happy Hour 90 | 29JUL2022

Harpreet Sahota — Sun, 31 Jul 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/idD5TyW45y8

Data Science Happy Hour 89 | 15JUL2022

Harpreet Sahota — Sun, 17 Jul 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=jwi9WH7588E

Data Science Happy Hour 88 | 08JUL2022

Harpreet Sahota — Fri, 08 Jul 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=9nSil9d9f7w&ab_channel=HarpreetSahota%7CTheArtistsofDataScience

Data Science Happy Hour 87 | 24JUN2022

Harpreet Sahota — Mon, 27 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=KUEpn6uiapM

The Data Scientist Show | Daliana Liu

Harpreet Sahota — Fri, 24 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Daliana onine: https://www.linkedin.com/today/author/dalianaliu
Watch the video of this episode: https://youtu.be/ldXGeOjGkx4

Memorable Quotes from the Episode:

[00:37:10] "Whatever we can do to actually, like, connect to our fellow man and woman is really, really important. And exercise and activity is a big way to do that. And the community serves our mental health. Whether you're an introvert or an extrovert, it doesn't really matter. Being around people serves you and allows you to feel like you're part of something like a belonging, right? So I think in all those ways, activity and exercise is huge for individuals who are just really trying to establish a baseline of mental health."

Highlights of the show:

[00:00:09] Guest Introduction

[00:02:05] Where you grew up and what it was like there?

[00:04:19] When you were in high school, what did you think your future would look like?

[00:07:41] How did you gravitate towards math having this interest that seemed a bit like honestly, like artsy kind of interest. Did you see any kind of art in mathematics? Is that what drew you to it? What was that pool?

[00:12:05] How is writing been an important element of success in your career?

[00:15:58 How do you suggest people get better at writing? Is it just going through, like a business writing class, one of those free business writing classes? How did you get developed that skill?

[00:20:49] What are some other critical elements to success for someone's career as a data scientist that don't get taught in school?

[00:24:34] How did you learn different skills?

[00:27:10] I'm wondering if a little bit in there is that feeling of imposter syndrome, a feeling of not wanting to ask a question because you don't want to be perceived as not knowing something like, oh, you're supposed to be a data scientist. Don't you know how to do this? Do you notice this happening a lot with with data scientist of any career level?

[00:30:53] Let's talk about how your day to day work as a data scientist is. How is this different from what you expected it to be when you were an aspiring data scientist?

[00:35:23] What what do most data scientists do wrong when it comes to their career development?

[00:38:35] Where would you draw the line between a data analyst and a data scientist? Can you point to one skill and be like, oh, right there, that's it. If only you knew this one thing, you'd be a data scientist. Does it work like that? What are your thoughts on that?

[00:44:09] What are your thoughts on why people are giving you so much pushback around that particular thing?

[00:52:24] How do you try to ensure that you're providing as fresh a perspective as possible with the content that you create?

[00:53:33] What are your thoughts on what it means to be a data science influencer?

[00:55:19] Let get into your podcast "the data scientist show". Talk to us about that. How did that idea come into your mind that you want to start a podcast? Who should listen to this podcast? Do you have to be experienced in the game to listen to it? Or is this a broad spectrum of data scientists.

[01:00:07-01:00:15] Let's talk about your experience being a woman in tech and a woman in data.If you have any advice or words of encouragement for the women in our audience who are breaking into or currently in our data world?

[01:06:05] What can we do to foster the inclusion of women in data science and AI?

[01:05:07] It is 100 years in the future. What do you want to be remembered for?

Random Round

[01:07:12] In your opinion, what do most people think within the first few seconds of meeting you for the first time?

[01:07:41] You do like journaling in the morning or anything like that?

[01:07:57] What are you currently reading?

[01:08:42] Can you share just a couple of tips on how not to feel bad not finishing a book?

[01:10:21] Pirates are ninjas?

[01:10:31] Mountains or ocean?

[01:10:38] If you were a vegetable, what vegetable would you be?

[01:10:48] If you could live in a book, TV show or movie, what would it be?

Data Science Happy Hour 86 | 17JUN22

Harpreet Sahota — Sun, 19 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=BltSMpwSBWw

Human Behavior Course, Interesting Challenging Work Podcast | Lara Pence

Harpreet Sahota — Fri, 17 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Lara online: https://www.drlarapence.com/
Watch the video of this episode: https://www.youtube.com/watch?v=jKwGLkMvzis

Memorable Quotes from the Episode:

[00:38:09] "Whatever we can do to actually, like, connect to our fellow man and woman is really, really important. And exercise and activity is a big way to do that. And the community serves our mental health. Whether you're an introvert or an extrovert, it doesn't really matter. Being around people serves you and allows you to feel like you're part of something like a belonging, right? So I think in all those ways, activity and exercise is huge for individuals who are just really trying to establish a baseline of mental health."

Highlights of the show:

[00:01:31] Guest Introduction

[00:03:03] Where you grew up and what it was like there?

[00:04:56] Did you think you're going to be into when you're in high school?

[00:06:36] It's fascinating that you love to study humans because we are interesting, interesting creatures.We always like to compare ourselves to others. So what is it? What is it about humans that make us always go through this, this comparison thing?

[00:09:13] If somebody else is talking the same topics that I'm talking about and they've got a bigger audience, if anything, they're attracting more people to it. So it's just this little mindset shift. Can we work through my comparison issues on the air? Is that something you want to explore with a couple of questions?

[00:13:13] Speaking to my audience, a lot of them are are definitely future leaders, if not already current leaders. It may include senior level management type of level, things like that. As we move up the chain in responsibility it can get tempting for us to take on more and more responsibilities, right?

[00:13:31] At some point we need to start saying no, but how? How do we go about saying no? Why is it important that we are able to say no?

[00:17:17] "Busy calendar and a busy mind will destroy your ability to do great things in the world."

[00:19:02] Decision making is definitely an important aspect of data science, especially at the leadership level. You've got to make decisions, you've got to make them well because the consequences could cost in many different ways. I wonder if you can share some ways for us to improve our decision making process.

[00:22:58] Let's talk about self-awareness as it relates to coming up with our values. First, how do we describe self-awareness in this context? How can we use that to help us identify our values?

[00:26:37] Is there something that we can attest that we can give ourselves to determine just how self-aware we actually are?

[00:29:33] What is this concept of of a personal true north? Talk to us about this this concept and how do we define that for ourselves?

[00:31:27] What are some surefire ways that that we can use to make sure that we can avoid distraction and stay productive?

[00:35:55] There's an interesting connection between movement and mental health, if you just talk to us a little bit about that.

[00:39:49] How do we fight that urge and force ourselves to get that movement in because it's going to help us in the long term, right?

[00:44:01] Talking about your obsession with curiosity. What do you find so curious about curiosity?

[00:46:31] "I don't need anyone's permission to be curious either. It's free."

[00:46:44] What can I do to ensure that I don't do anything that would cause him (Harpreet's son) to lose that curiosity?

[00:49:21] How do we cultivate that sense of curiosity as adults?

[00:51:24] It is 100 years in the future. What do you want to be remembered for?

Random Round

[00:52:11] What are you most active with in terms of podcasting?

[00:53:06] What is the the life box substance about this?

[00:54:46] What in your opinion, what do you think people think within the first few seconds of meeting you for the first time?

[00:55:11] What are you currently reading?

[00:55:31] What song do you have on repeat?

[00:55:54] What accomplishment are you most proud of?

[00:56:26] What sport are you playing?

[00:56:31] What makes you cry?

[00:57:14] What is your favorite city?

[00:57:50] What is something you can never seem to finish?

Data Science Happy Hour 85 | 10JUN22

Harpreet Sahota — Sun, 12 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=NstXQM0M5JI&t=5s

Top Tech Companies of Data Science, Motivation And Tangible Tips

Harpreet Sahota — Fri, 10 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Jonathan online: https://www.linkedin.com/in/jonathan-wonsulting/
Find Jerry Lee online: https://www.linkedin.com/in/jehakjerrylee/
Watch the video of this episode: https://www.youtube.com/watch?v=KdfFyY_-XT8&t=89s

Memorable Quotes from the Episode:

[00:26:43] " I think one of the best things about the content is that we sort of have seen on LinkedIn, Austin, Balzac as an example, he posts very, very actionable content and that's very much what we sort of like to strive for as well when we talk about job search, making it extremely tangible so that someone who is reading it can take action that very second. So for you, if just content that you really like and enjoy with and enjoyed and you're like, Listen, I'm going to try it. If it takes you less than 5 minutes, do it. Try it. Best case scenario is that you get a job out of it. The worst case scenario, you use 5 minutes."

Highlights of the show:

[00:00:41] Guests Introduction

[00:03:41] Jerry, talk to us about where you grew up and what it was like there?

[00:05:18] Jonathan Mann, tell us a little bit about yourself. Where did you grow up and what it was like there?

[00:06:49] How do you guys know each other? These guys grow up together. You guys go to high school together. You know, what's what's the back story there?

[00:07:55] Talk to us first about the genesis of the company. How did this idea start? How did this idea come about? What were you seeing in the world that was just like I just just couldn't take anymore. You had to do something about it. Like, what was that moment?

[00:10:49] So Jonathan talks about 'what is the definition of an underdog' . Who are the underdogs? And then maybe after that, Jerry, why is it that companies tend to overlook people just because of their "pedigree"?

[00:12:25] What is it about these companies overlooking people just because of their pedigree?

[00:14:50] What's like one of the first few things that you start to do with people? What are the first, I guess, myths you start to debunk or the mindset shift mindset shifts you help people go through or anything like that?

[00:16:11] Jonathan what is the first two steps to getting from that rejection to redirection path?

[00:17:20] When when you go to a LinkedIn profile, what's the immediate thing you go to? Let's start with that, Jerry, and then go to John.

[00:19:12] When it comes to the headlines, what is a common mistake you see people make repeatedly when it comes to their headlines?

[00:22:02] What are some do's and don'ts that you can share?

[00:23:32] What if we just don't feel like we're an expert enough to post content?

[00:25:01] There's the creating content, but then there's the consumption of content. How do you how do you ensure that you're consuming good stuff?

[00:26:12] There are a lot of good content out there as well, right? Once you have the good content filter down to get your feed full of stuff that you actually do want to see, then it becomes, Oh my God, there's so much good stuff and so many good tips, like, how the fuck do I apply this to my life? What am I supposed to do? Do you have like the tips or a framework on, on how you go about doing that.

[00:26:34] In terms of making use of all the wonderful tips that people are sharing because sometimes they just get so many tips, they might just get paralyzed like, oh my God, what do I do? What are your tips on that?

[00:31:31] Let's say you applied for a job. You're in the in the interview and you're showing up to an interview and you don't have much experience. Let's say it's an entry level job. So I just want to get your hot take on entry level jobs requiring experience. What are your thoughts around that? How can we break that need experience to get experience a cycle?

[00:34:27] Should we worry about looking a job hopper in 2022? What are your thoughts on that?

[00:36:52] Before we get to that phase again, job offers and all that stuff, we can't job help them see a job offers. How about those negotiations? That's the critical piece, I think, of the job process. Do you feel that people tend to be afraid to negotiate? And where do you think that fear stems from?

[00:38:44] How do you ask better questions during an interview to get to know more about the culture and environment?

[00:42:28] Is there a right or wrong way to answer to the "tell me about yourself" question. Jonathan, what do you think?

[00:43:25] How should we answer the "what's your biggest weakness" question? Should we actually just say weakness or what's your tips there?

[00:44:57] Talk about being an influencer, LinkedIn influencer, kind of the perils of being a LinkedIn influencers. What responsibility do you think it is? I don't know if I'm counted, I only got like 43,000 followers for whatever I'm influencer or not. But I feel like I have some responsibility towards people who consume my content. What are your views on that? What responsibility do we do we have towards towards those who are following us?

[00:46:59] Have you guys ever gone to any types of bouts of kind of creative burnout? What was that like? How did you overcome it? What were some early warning signs that you're starting to get burnt out?

[00:49:45] What's the right way to ask for a mentor? How do we identify who we want as a mentor?

[00:50:59] How do you go about finding this person might be a good candidate or that that vetting process or what have you?

[00:52:40] It is 100 years in the future. What do you want to be remembered for?

Random Round

[00:54:08] What song do you have on repeat?

[00:54:41] What talent would you like to show off in a talent show?

[00:54:59] What fictional place would you most like to go?

[00:55:18] If you lost all of your possessions but one, what would you want it to be?

Data Science Happy Hour 84 | 03JUN22

Harpreet Sahota — Sun, 05 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=DAkvvP6-TuQ&t=14s

How to Build and Lead Data Science Teams | Jeremy Adamson

Harpreet Sahota — Fri, 03 Jun 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Jeremy online: https://www.linkedin.com/in/rjeremyadamson
Watch the video of this episode: https://www.youtube.com/watch?v=UglmEt_CRQE

Memorable Quotes from the episode:

[00:31:19] "Design thinking is a great ideation framework for understanding based on the business outcome, how we can tackle that. It's five simple steps. The first one is to empathize with the stakeholder, and that's a word that I think we need to be saying a lot more in this practice is empathy."

Highlights of the show:

[00:01:22] Guest Introduction

[00:03:04] Talk to us a little bit about how you got interested in data science and what was your path into the field like?

[00:05:08] How much more hype has data science, A.I. and all that become since you first broke into the field?

[00:06:26] What do you see happening in 2022 in data science and analytics? What's the big thing that you're excited or hopeful about?

[00:13:34] What are some guiding principles that we should keep in mind to ensure that we're successfully building and leading those?

[00:15:07] What's the etiquette behind the kicking of the doors?

[00:16:48] We will get into 'design thinking' part of the book, but I want to double down on the 'process' aspect of the book. What is 'process' anyways and what is it all about?

[00:18:16] What are some some ways that we can ensure that our processes remain parsimonious? And if you got any examples that you want to share with us.

[00:19:50] Talk to us about comprehensive group of processes that that are required for for project success.

[00:23:48] Walk us through prioritization projects.

[00:25:25] Identifying things that are important, we talk about this with respect to a project scoping and planning that there's some questions that we should ask ourselves and ask our stakeholders. Two crucial ones. Can you share those questions with us? And what is it that we hope to get from from asking those questions?

[00:27:47] When it comes to dealing with stakeholders or let's say we've identified that this is a problem that we should be working on, but how do we make it? How do we frame it from the business problem to an analytics problem? What are some questions we should use to tease out what we need to, to properly frame it?

[00:31:06] There's something that you talk about called 'design thinking'. What is design thinking? What's it all about? And what does this have to do with 'process'? What does this have to do with data science?

[00:32:42] It seems like designing requires a skills that are underdeveloped in a lot of data science and analytic professionals. How do we cultivate those skills and make that process enjoyable for everyone who's involved?

[00:34:46] When it comes to executing a project, does Agile have a place in the data science world?

[00:35:32] Do you have a structured approach for generating demand within an organization, especially for new teams where all business functions are our customers?

[00:37:00] What is a SKU morph and how can we use this to our advantage in data science?

[00:39:20] Are there, if you know of any studies about how agile methods can be applied to teams in data analytics or finance.

[00:42:53] How can we start viewing ourselves as craftspeople? What do you mean by a 'bi craftsperson'? How can we start being ourselves as that?

[00:45:34] It's been extremely hard to hire and keep great data scientists. Do you have any tips that have worked for you? You've touched on a few of those, but have you got any additional tips for that?

[00:47:20] Apart from the technical skills, what is it that you look for in data science candidates?

[00:48:39] How can an individual contributor embody the characteristics of a good leader without necessarily having that title?

[00:50:11] It's 100 years in the future. What do you want to be remembered for?

Random Round:

[00:50:45] Let's just think about some interesting use cases for data science and machine learning in the aviation industries. What are a couple of ways that machine learning is being used there?

[00:52:37] If you were to write a fiction novel, what would it be about and what would you title it?

[00:53:00] What are you currently reading?

[00:53:14] What are you currently most excited about or currently exploring?

[00:53:51] What's something you learned in the last week?

[00:54:02] What have you created that you're most proud of?

[00:54:15] Have you ever saved someone's life?

[00:54:21] What's the best compliment you've ever received?

Data Science Happy Hour 83 | 27MAY2022

Harpreet Sahota — Sun, 29 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=qHjKd4van4o

How to Ace the Data Science Interview | Nick Singh

Harpreet Sahota — Fri, 27 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Nick online: https://www.nicksingh.com/
Watch the video of this episode: https://youtu.be/7fzOYBkTHDM

Memorable Quotes from the show:

[00:12:47] "The part of math that I was interested in wasn't that crazy, crazy theoretical math. It was just like, Oh, how can we use data to drive better decisions? Like how can simple statistics and computing metrics and just keeping track of shit using numbers? How can that help build better products or build better systems? And that's what I learned in systems engineering. Combine that with some of my CS classes, which got me into a little bit more machine learning, and then it started clicking in my head of like, Oh, this data thing is really cool."

Hightlights of the show:

[00:00:40] Guest Introduction

[00:03:26] Talk to us a little bit about where you grew up and what it was like there.

[00:07:57] What is it about us (of Indian heritage) and software and data science?

[00:09:11] Was there something you were always good at? Did you think you were ever going to be an author?

[00:11:03] Was data science something that you were exposed to when you're young?

[00:13:57] What is the business side of data? Please paint that picture for us.

[00:19:22] Is it better to have blank space on a resume than neutral information?

[00:23:34] LTalk to us about what this philosophy is for projects.

[00:31:57] How do we demonstrate business value with a project, especially if we don't have on the job experience and are doing a project to demonstrate our technical ability?

[00:39:20] You talk about cold emailing in your book. Is that just when someone messages somebody highly ranked on LinkedIn and leave it at that?

[00:40:50] Let's say somebody sees this awesome job on LinkedIn and then started looking for people in that company. Should they go and message an individual contributor, data scientist and have them look at their profile or send a message to the CEO? Like who on the spectrum do they reach out to?

[00:46:03] It is noticed that a lot of people that are new to the industry are new data scientists who are all up in their head thinking oh, man, like math and everything, thinking all about algorithms and their sleep. They think that these behavioral interview questions are just fluffy bullshit. Why do you think folks have this misconception?

[00:50:10] You talk about a framework in the book at a high level. Can you share a bit of that framework for how you would answer that question (where the star format doesn't apply)?

[00:52:34] Would you rather mention your knity gritty experiences from the past in an interview or do mention a little of a role that you played in math or astrophysics. Say that you're trying to get into a machine learning engineer role, can you share your response to that question with us here?

[00:55:12] Auditing the "tell me about yourself" question.

[01:04:50] What does product sense mean? What is it? Why are people afraid of it? Why does it seem like such a difficult skill?

[01:11:35] What's the number one product sense question that you see being asked?

[01:14:36] It is it's 100 years in the future. What do you want to be remembered for?

Random Round

[01:16:18] What do most people think? Within the first few seconds of meeting you for the first time.

[01:16:47] You have this awesome blog post about books that you always bring up in conversations. One of them is written by probably my absolute favorite authors and one of my favorite books. That's Antifragile by Nassim Taleb. Talk to us about the three main takeaways you've gotten from that book.

[01:21:19] What are you currently reading?

[01:24:23] First question what makes you cry?

[01:24:41] If you were a vegetable, what vegetable would you be?

[01:24:50] What have you created that you're most proud of?

[01:25:33] What's the best piece of advice you have ever received?

[01:26:54] If you lost all of your possessions but one, what would you want it to be?

[01:27:29] Do you ever sing When You're Alone?

[01:27:52] What's your favorite candy?

Data Science Happy Hour 82 | 20MAY2022

Harpreet Sahota — Sun, 22 May 2022 02:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=eYfHD1CkvRI

The Art of Statistics | David Spiegelhalter

Harpreet Sahota — Fri, 20 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find David online: https://twitter.com/d_spiegel
Read David's article "Will I live longer than my cat?": https://www.bbc.co.uk/news/magazine-19467491
Watch the video of this episode: https://youtu.be/pCWH97vBFmU

Memorable Quotes from the show:

[00:23:36] "...essentially what probability theory allows us to do is to make assumptions about how the world works, how the data is generated, and turn it and flip it around after we observe some data into statements about our uncertainty about underlying features of the world. We can do that, which of course is very explicit based on work indeed, where after processing data or uncertainty and it turns into uncertainty about the underlying quantities."

Hightlights of the show:

[00:01:29] Guest Introduction

[00:03:08] Talk to us about how you first got interested in statistics and what was it that drew you to this field?

[00:04:55] Why is it that it seems like mathematicians tend to dislike teaching statistics?

[00:08:27] What is statistical science and what is it all about?

[00:09:46] You talk about in your book, The Art of Statistics, how to handle problems and approach problems in statistics. You call the P, p, b, a C cycle. Tell us about that framework.

[00:15:03] You mentioned in the book that statistics is to blame for the reproducibility and replication crises in science. Why? Why is that?

[00:18:23] When we talk about induction and inductive inference, should the philosopher in us get worried at all about the problem of induction in statistics?

[00:19:40] Tell our audience about the 'normal distribution'.

[00:20:34] Do you have any examples of when inductive inference has failed in statistics that you could share with us?

[00:22:15] Why do we need probability theory when we're doing statistics?

[00:26:25] I think pouring into the Bayesian stuff is kind of taking a step back here, maybe first principles. But what is probability? How do we measure it? It seems like such a strange epistemological concept.

[00:28:27] Can we say there's a at least some type of difference between epistemic probability and some physical or I believe you say aleatory?

[00:30:03] Would there be a difference in the way that a philosopher or a statistician would interpret probability?

[00:38:32] What's the Bayesian approach all about and why is it that courts in the UK are banning it or have banned it?

[00:40:16] How is this (Bayesian approach) different from the frequentist approach to viewing probability? What's the central difference?

[00:44:55] It seems like the prior distribution is something that makes base them so controversial. Why is that?

[00:46:18] It seems like Bayes Theorem is the scientifically correct way to change your mind when you get new evidence, right?

[00:48:18] David Deutsch mentioned lately about the Bayesian-ism, and he's having some qualms with Bayesian ism. He says that Bayesian-ism becomes controversial when you try to use it as a way to generate new ideas or judge one explanation against another. How do we reconcile that when we're faced with some epistemic.

[00:49:51] About using it to help us in our everyday lives to make better decisions. How can we use Bayes in that context?

[00:53:15] It is 100 years in the future. What do you want to be remembered for?

Random Round

[00:54:17] What do you believe that other people think is crazy?

[00:55:02] What are you most curious about right now?

[00:55:55] What are you currently reading?

[00:58:33] What do you like most about your family?

[00:58:53] What was your best birthday?

Data Science Happy Hour 81 | 13MAY2022

Harpreet Sahota — Sun, 15 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=I6uLiz4lTrU&ab_channel=HarpreetSahota%7CTheArtistsofDataScience

Dave on Data | David Langer

Harpreet Sahota — Fri, 13 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/x26n7HmSYjw

Data Science Happy Hour 80 | 08MAY2022

Harpreet Sahota — Sun, 08 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=SYiQ1ncCGv8&ab_channel=HarpreetSahota%7CTheArtistsofDataScience

Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh

Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark

Shortcuts, Creativity, and Deep Learning | Marcus du Sautoy

Harpreet Sahota — Fri, 06 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Marcus online: https://twitter.com/MarcusduSautoy
Watch the video of this episode: https://youtu.be/efIBVILq6WI

Memorable Quotes from the show:

[00:34:25] "...one has to learn the power of the short cut in statistics, which, you know, I tell the story about the we had this advert when I was a kid which which stated eight out of ten cats prefer a particular type of cat food. And, and we had a cat and I never remember anybody asking our cat what cat food it likes. So it was very striking that when I got to university, I learned about the power of sampling and the fact that, you know, to be able to there are 7 million cats here in the UK. How many cats would you have to ask to be confident enough to make that statement about?"

Highlights of the show:

[00:00:40] Guest Introduction

[00:03:08] Talk to us about where you grew up and what it was like there.

[00:08:15] Math is kind of just the language we use to describe it. What are your thoughts?

[00:10:49] From your viewpoint, do you think math is an art? Is it a science? Is it a combination of art and science. How do you how do you view this?

[00:13:52] What was it about Gauss when we talk about Mathametics?

[00:19:02] Is there any virtue in human laziness?

[00:21:52] Aristotle, idleness and noble leisure. Discuss.

[00:21:59] Speaking of creativity and putting you out of a job, can you discuss a little more about what you talk about in your book about it?

[00:27:18] Speaking of creativity, you took time in this pandemic to write a play. How is that coming along?

[00:29:44] Fringe Festival in Winnipeg and London Fringe in London.

[00:30:07] You shared a story in the book about how we can use math to fight off of vampires. If you could recount that story.

[00:33:47] What are some dangers of using statistical shortcuts that we should be on high alert for?

[00:39:34] "...data science can be dangerous if it's not combined with a deep understanding of where the data comes from."

[00:40:33] Why is it that our that our brains aren't very good at assessing probabilities?

00:44:14] Why is it that some people find that shortcut that Reverend Bayes discovered so controversial?

[00:47:02] You talked about the philosophical view of probability. Is it frequentist approach, the Bayesian approach? How do you view probability? What's your take on that?

[00:49:41] What is the Lovelace test and in what ways is it different from the Turing test?

[00:56:25] You talk about a few different types of creativity in your book, please eloborate.

[01:06:17] What is it about a mathematician's mindset that is deterministic and foolproof and of engineers?

[01:09:34] It is 100 years in the future. What do you want to be remembered for?

Random Round

[01:10:46] What was your best birthday and how old were you at that birthday?

[01:11:42] What's the worst movie you've ever seen?

[01:12:06] What would you do on a free afternoon in the middle of the week?

Data Science Happy Hour 79 | 29APR2022

Harpreet Sahota — Sun, 01 May 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=30uugRuW5_E&ab_channel=HarpreetSahota%7CTheArtistsofDataScience

The People's Data Scientist | Danny Ma

Harpreet Sahota — Fri, 29 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Danny online: https://www.datawithdanny.com/
Watch the video of this episode: https://youtu.be/VgQ_Hhq4AlM

Memorable Quotes from the show:

[00:34:25] "I think in general, just we should really be out there to help others instead of trying to help ourselves in a way. Like I know of a few larger names who the social media presence is, their business, essentially. And I know that's really important. Like everyone has to make money, feed their families, buy all the things that they need in life and all that aspirational sort of things. But in a sense, like for me, like I don't know if this is this might be similar for you as well."

Hightlights of the show:

[00:00:40] Guest Introduction

[00:02:32] Where you grew up and what it was like there.

[00:03:57] What's life in Sydney been like for you? Have you come to North America? Have you done a compare and contrast that what's different and what's the same?

[00:06:20] What kind of kid were you during high school and what did you think your future would look like?

[00:10:35] You and I somehow came from a similar type of background, having a kind of walk that actuarial path we're entering into this data science kind of field. Tell us what was your experience like with those exams.

[00:12:52] What was it about kind of doing that actuarial work that made you want to leave it behind and move to this data thing?

[00:18:13] How did you figure out that what it was that you needed to figure out in order to make it in this field?

[00:22:43] How do you try to ensure that you've got as fresh a perspective as possible? Do you even need a fresh perspective as possible? What are your thoughts on that?

[00:29:14] We're just talking about what it means to be a data science influencer. What are your thoughts on what it means to be a a data science influencer?

[00:32:57] Do we have an influencer quality - What responsibility do we have to these people that are following us?

[00:36:51] What do you consider the difference to be between coaching and mentorship?

[00:39:27] How can somebody go and go about finding a mentor?

[00:43:07] What elements can you take and apply to this new thing that you want to do in the essence of creativity as well as finding different things that on the surface of it don't look like they belong together. But when you put them together, it actually gels quite nicely.

[00:44:40] Do you have any tips on on how I can be a better mentor?

[00:53:09] Talk to us abouth the love of SQL. How did this happen? Is this something that you've always just enjoyed? Has SQL always been your favorite part of the entire data science ecosystem? How this deep, deep love of SQL happened?

[00:57:23] Can we draw the line between a data analyst and a data scientist?

[01:08:32] What's your take on the importance of taking action on an idea you have in your mind there?

[01:12:00] It is 100 years in the future, what do you want to be remembered for?

[01:13:01] At what point did your meme game get so dank?

[01:15:21] What are you currently reading?

[01:17:05] What song do you currently have on repeat or stuck in your head?

Random Round

[01:18:00] Who inspires you to be better?

[01:18:09] What's the best piece of advice you've ever received?

[01:18:15] Who is one of your best friends?

[01:18:29] If you were a vegetable, what vegetable would you be?

Data Science Happy Hour 78 | 22APR2022

Harpreet Sahota — Sun, 24 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/LR81rcjuaFk

A Conversation with The Data Professor | Chanin Nantasenamat

Harpreet Sahota — Fri, 22 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Chanin online: https://th.linkedin.com/in/chanin-nantasenamat
Watch the video of this episode: https://youtu.be/pCHubISFBTI

Memorable Quotes from the show:

[00:31:42] "So I would believe that scientific method would be the science part of data science, and the data could be biology, chemistry, physics, business data, economic ecology. So I would believe that it's pretty much like a plug and play like data could come from many discipline. And then the analytic part, the machine learning part would be to take that data and make it into an interpretable model."

Highlights of the show:

[00:00:36] Guest Introduction

[00:03:12] Where you grew up and what it was like there?

[00:04:22] What brought you back to Thailand?

[00:05:15] How different is your life now than what you thought it would be growing up?

[00:07:03] When it comes to making YouTube videos, what is your most favorite part about making the YouTube videos and what is the part that you just liked the least?

[00:08:02] What part of it is the toughest? Is it just that the editing and the blogging and stuff like that? Or is there some parts of it where you're just like, Oh, man, I hate doing this?

[00:09:47] What is bioinformatics and how did you get into that?

[00:11:22] Was there any additional upskilling that you had to do in machine learning or data science topics? And if there was any additional upskilling, what was your process to acquire that knowledge?

[00:17:19] "How do I figure out what projectsI want to do, how to figure out what I want to research?" hat advice do you typically give to such questions?

[00:19:00] What is drug discovery? Where does data science enter into the mix here?

[00:22:28] Do you have any interesting use cases or studies you can share with us that talk about the involvement of machine learning and drug discovery, like a friendly, easy to read paper or maybe one of your YouTube videos if you got something like that?

[00:26:26] Do you know of anything that's been released on the market that has used this (drug discovery) approach? Is it widely used? Is it commonly used? Or is this kind of something that's right now just a theoretical idea?

[00:27:09] YouTubing, but where did that spark to help other data scientists come from?

[00:31:40] where is the science in data science?

[00:34:30] The methodology, a traditional machine learning problem or deep learning one. The process methodology is a little bit different. You worked with both of those, how would you say it's compare and contrast that if you would for us?

[00:36:50] Talk to us about a few of your blog posts.

[00:43:43] It is 100 years in the future, what do you want to be remembered for?

[00:44:45] When it comes to the future of of data science and machine learning, what applications are you most excited about in the field of drug discovery or bioinformatics? What gets you hyped up when you think about it?

[00:46:38] What are you currently reading?

[00:48:06] What song do you currently have on repeat?

[00:48:38] What are your pet peeves?

[00:49:02] Do you have any nicknames?

[00:49:22] What talent would you show off in a talent show?

[00:49:44] When was the last time you changed your opinion about something major?

[00:51:29] What's your favorite city?

Data Science Happy Hour 77 | 15APR2022

Harpreet Sahota — Sun, 17 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://www.youtube.com/watch?v=32znIxJoFRo

The International Woman of Data | Christina Stathopoulos

Harpreet Sahota — Fri, 15 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Christina online: https://www.linkedin.com/in/christinastathopoulos
Watch the video of this episode: https://youtu.be/FCQZfuhV6vY

Highlights of the Show:

[00:01:32] Guest Introduction

[00:03:24] Where did you grow up and what was it like there?

[00:04:18] You spent a decade abroad in Spain. How did that happen?

[00:06:28] How did you transition into analytics?

[00:08:31] How did you learn another language as an adult? Was that challenging? How did you figure that out?

[00:13:48] You and Kate are quite busy. How do you balance all of these activities, all of these speaking engagements and teaching, plus having a full time job?

[00:18:17] How did you develop this reading habit and how are you getting all these books? Are you getting them delivered to you or do you have a book exchange thing? How's this working?

[00:20:35] Do you do audiobooks or just strictly so?

[00:21:50] How can someone who's new to this space (analytics) decide which direction is right for them? And how did you figure out what direction you wanted to go into?

[00:24:53] What are some soft skills that you think have helped you really excel in your career?

[00:29:16] Russell defined your multilingual skills with spoken and written language. Do you find that they help you when translating between different coding languages?

[00:31:08] How can we simplify complex tasks?

[00:32:59] When you put yourself into their shoes, is there some kind of universal thing that most CEOs tend to care about or universal points that you've noticed through all these interactions that you've had, if such a thing could exist?

[00:38:18] How did you overcome technological challenges? If you face that challenge at all. What I'm trying to ask is learning new things as they come up in your career. How do you handle that? How do you manage that?

[00:42:51] Is there anything that you feel like you're just a natural at?

[00:43:31] In terms of the new methodologies and new technology that is coming out, is it mostly the academic research stuff? Is the cutting edge deep learning stuff? What do you find more fascinating?

[00:46:20] "How do you practice being present during tough times and tie back to your purpose with the work you do?"

[00:49:39] If you had any words of advice or encouragement for the women who are breaking into or that are currently in our world of data science?

[00:52:43] What can we do to foster inclusion of women in data science?

[00:57:04] It is 100 years in the future. What do you want to be remembered for?

Random Round

[00:57:54] You've done a lot of traveling 50 countries. What's the most beautiful place you've ever seen?

[01:02:39] What song do you have on repeat?

[01:05:19] What incredibly strong opinion do you have that is completely unimportant in the grand scheme of things?

[01:06:01] What was your best birthday?

[01:06:10] Do you have any nicknames?

[01:06:24] What's your worst habit?

Data Science Happy Hour 76 | 08APR2022

Harpreet Sahota — Sun, 10 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/6wNvlO8Jj7o

Leap into creativity | Natalie Nixon

Harpreet Sahota — Fri, 08 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Natalie online: https://www.figure8thinking.com/about/

Watch the video of this episode: https://youtu.be/3Dr7OcyQj1M

Highlights of the show:

[00:01:32] Guest Introduction

[00:03:34] Where did you grow up and what was it like there?

[00:08:04] Talk to us about the idea of “indoctrination to education”.

[00:12:35] Your interesting ‘creativity research’, when did that start? How did your interest in that get sparked?

[00:18:24] How can we make creativity more accessible and not just something that feels like it's in the domain of artsy people?

[00:23:15] What is your definition of creativity?

[00:27:27] Discuss the aspect of wonder and rigour.

[00:29:09] What's wrong with the way that we're currently asking questions?

[00:38:17] Walk us through what design thinking is and how does that help us be more creative?

[00:40:58] What is divergent and convergent thinking?

[00:50:47] Talk to us about the remix, the reframe and repurpose. How they help play a role in being creative?

[00:53:04] Talk to us about 'Fashion thinking'.

[00:56:20] It is 100 years in the future. What could it be remembered for?

Random Round

[00:57:13] What are you currently reading?

[00:57:37] What song do you currently have on repeat?

[00:58:22] What's the best thing you got from one of your parents?

[00:58:31] In your group of friends, what role do you play?

[00:58:41] What fictional place would you most like to go to?

[00:59:02] Pizza or tacos?

Data Science Happy Hour 75 | 01APR2022

Harpreet Sahota — Sun, 03 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/7l-pB7RkCJA

To Data Science Infinity and Beyond! | Andrew Jones

Harpreet Sahota — Fri, 01 Apr 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Andrew online: https://www.linkedin.com/in/andrew-jones-data-science-infinity
Watch the video of this episode: https://youtu.be/hiEEAIGP8Zo

Memorable Quotes from the Show:

[00:20:55] “You need somebody in there to be putting the nuance on that to make it actually work. I think for me as well, like Auto, where ML doesn't have any business sense necessarily, so it doesn't know what problems to solve or it doesn't know why it should solve them. So I think humans are still a huge part of that. I don't think that's going away anywhere soon. It's just an evolution and data scientists are going to start, you know, there's going to be bits where automation comes in and helps us do our jobs even better. But I don't think it's going to take away jobs necessarily. I don't have any particular fear about that.”

Highlights of the Show:

[00:01:18] Guest Introduction

[00:03:20] Where did you grow up and what was it like there?

[00:06:19] When you were in high school, what did you think your future would look like?

[00:07:12] At six foot seven. It's a shame that you did not get into basketball. Is that right?

[00:07:48] Did you start doing analytics in New Zealand or start in London? Walk us through that journey.

[00:10:39] What's the toughest part about transitioning from SAS into python?

[00:16:25] You've been in this field for over a decade. How far has it come since you first broke into it?

[00:19:13] Can you share a hot take with us on where you think the field of data science is headed?

[00:33:29] Talk about your mission to help develop data scientists.

[00:39:28] What makes an employable data scientist different from an unemployable one?

[00:42:07] Where do you think most data scientists go wrong in terms of their own career development?

[00:45:07] “How to choose the right model to train the data?”

[00:49:06 Is there a field within machine learning that focuses on incorporating human concerns through technology development?

[00:51:58] “What advice do you give social scientists that are learning data analytics? Any particular hints for psychologists trying to understand acceptable norms of behavior when creating data science projects?”

[00:54:04] Talks to us about the top five reasons that candidates get rejected.

[00:58:13] When it comes to career growth and development. What's the biggest lesson you learned the hard way that you want to make sure no one else makes?

[00:59:49] It's 100 years in the future. What do you want to be remembered?

Random Round

[01:01:18] What do you think will be the first video to hit 1 trillion views on YouTube? And what will that video be about?

[01:03:29] What are you currently reading?

[01:05:09] What song do you currently have on repeat?

[01:06:30 If you had to change your name, what would you change it to?

[01:07:26] What's on your bucket list this year?

[01:08:40] What's the story behind one of your scars?

[01:09:51] What languages do you speak?

Data Science Happy Hour 74 | 28MAR2022

Harpreet Sahota — Mon, 28 Mar 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/2kCDJbiTCk8

It's Bigger Than Leadership | Brittany Do

Harpreet Sahota — Fri, 25 Mar 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Read Brittany' book: Bigger Than Leadership https://www.amazon.com/Bigger-Than-Leadership-Influence-Inspiration-ebook/dp/B093PJGB6R

Watch the video of this episode: https://youtu.be/HP-_FJh8UGY

Memorable Quotes from the episode:

[00:33:19] “No matter who you are, no matter where you are, you do leave a visible,or like a physical, but also a more mental footprint everywhere you go. That is something that's really powerful.”

Highlights of the Show:

[00:01:18] Guest Introduction

[00:02:34] Where did you grow up and what was it like there?

[00:04:05] When you were in high school, what did you think your future would look like?

[00:07:44] Talk to us about your personal definition of what leadership is, what it means to you, and then why write about leadership?

[00:10:30] Who is your book for?

[00:12:36] Do you think a lot of people tend to feel that way, like they don't see themselves as leaders or they don't realize that they have this leadership ability. Would you agree with that? Why? Why not?

[00:14:35] What was the process like while writing your book? How did you manage your knowledge? How did you manage the notes? And then finally, how did all that come together in a book?

[00:16:51] Talk to us about the importance of stories and why they serve as such useful reminders for us.

[00:19:29] How do you balance all the activities - full time course load, writing a book in the middle of a pandemic? How did you manage all that?

[00:22:55] “Can you tell us more about the role of introspection in your writing work and the role of introspection in stories of leadership?”

[00:25:55] Do you have an introspection practice that you undertake? Is it just sitting and thinking, is it sitting in journaling? Is it going for a walk and thinking? How do you get your introspection on?

[00:28:58] How were you able to keep a narrow focus while exploring so much data in your writing?

[00:32:41] Talk to us about the “Three Eyes framework”. You touched a little bit on the intentionality aspect of it, but talk to us about how these three, I guess what these three eyes are and how they relate to leadership.

[00:43:02] Talk about the difference between leadership and mentorship.

[00:45:36] Can you share some tips with the audience for how we can go about finding a mentor for ourselves?

[00:49:17] What tips would you have for someone who finds themselves in a position similar to mine where all of a sudden people have started following them on LinkedIn and or other social media and have started to view them as mentors. What advice would you share?

[00:54:20] It is 100 years in the future. What do you want to be remembered for?

Random Round

[00:56:30] When do you think the first video to hit 1 trillion views on YouTube will happen? And what do you think that video will be about?

[00:57:11] What do most people think within the first few seconds of meeting you for the first time?

[00:58:48] Talk to us about what the book title “Bigger Than Leadership” means to you.

[01:00:53] What are you currently reading?

[01:02:12] What is your procedure for taking notes?

[01:03:11] What song do you currently have on repeat?

[01:04:22-01:04:28] When people come to you for help, what do they usually want help with?

[01:05:45] What is your theme song?

[01:06:26] What issue will you always speak your mind about?

[01:07:25] Who inspires you to be better?

Data Science Happy Hour 73 | 18MAR2022

Harpreet Sahota — Sun, 20 Mar 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/tzg4SkNo4g0

The Boss Mare of the Data Nerd Herd | Joe Reis

Harpreet Sahota — Fri, 18 Mar 2022 00:00:00 -0400

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Joe online: https://www.linkedin.com/in/josephreis

Joe is a business-minded data nerd who’s worked in the data industry for 20 years.

In his two decades as a practitioner he’s worked on the full gamut of data tasks from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else in between.

He’s taken all that experience and started his own venture and is currently the CEO of Ternary data.
Watch the video of this episode: https://youtu.be/6jGmXBaTJkI

Memorable Quotes from the episode:

[00:42:09] "...My other piece of advice, which is, do you lose money for the firm? I'll be understanding. If you lose a shred of reputation, I will be ruthless. Let's talk with me. Right. Reputation is everything. As he also says, it takes a lifetime to build a reputation. It takes 15 minutes to destroy it. So when we started our business, I thought it was interesting. We didn't really care about the money. We cared about reputation and cared about doing great work, meeting great people and just, I think developing good relationships. I always optimizing for reputation. I think we thought if we could build that pile of reputational capital, the money would follow. The reverse is rarely true, though. In the short term, you can build as much money as you can, but you can destroy your reputation. And then who's going to want to do business with you?"

Highlights of the show:

[00:01:11] Guest Introduction

[00:03:34] Joe, where did you grow up and what was it like there?

[00:05:22] What were you like as a high school kid? What did you think your future would look like?

[00:06:46] When you'd make the move over to Salt Lake City? Was that when you started working? Did you go to school there?

[00:09:08] What was it like kind of when you first started out and what drew you to this kind of field (data science)?

[00:14:02] Where is the science in data science? Is there any science in data science? Is it scientism?

[00:26:10] How did you guys link up and decide to start ternary data and can we even get the story behind the companies name as well?

[00:27:23] What are some problems that you just see as a consultant pop up over and over?

[00:34:06] Do engineers add value and how should we think about a return on investment for the work that they do?

[00:41:23] Talk to us about your blog post about the concept of reputational capital.

[00:43:04] Do you have any tips for people who are just early in their data science career. In their first job as a data scientist, how can they accrue some of this 'reputational capital'?

[00:45:56] How reading science fiction has made you a better technologist? What science fiction has done for you, has it made you a better technologist?

[00:47:44] What would you say is the one sci-fi work that's had the biggest influence on you as a technologist?

[00:51:04-00:51:17] You've got such a dope setup here. What's all this about? The keyboards? You got turntables, you got multiple keyboards. Are you making your music. Do you got any undercover Spotify?

[00:52:59] It's 100 years in the future. What do you want to be remembered for?

Random Round

[00:54:02] When do you think the first video to hit to 1 trillion views on YouTube will happen? When will that happen and what will that video be about?

[00:55:33] What song do you have on repeat?

[00:55:53] What are you currently reading?

[00:59:19] What's kind of your process when you're reading?

[01:03:12] What talent would you show off in a talent show?

[01:03:39] What do you mean by organizational behaviour?

Data Science Happy Hour 72 | 11MAR2022

Harpreet Sahota — Sun, 13 Mar 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/voMA7BvAHJ8

Become an Effective Data Storyteller | Brent Dykes

Harpreet Sahota — Fri, 11 Mar 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Find Brent online: https://www.linkedin.com/in/brentdykes
Watch the video of this episode: https://youtu.be/QcihRq9ieWE

Highlights of the show:

[00:01:21] Guest Introduction

[00:03:33] Talk to us a bit about where you grew up and what it was like there?

[00:04:58] You're still involved in the technology field and still involved with 'Data' in a sense. So is life really different than what you imagined it might be?

[00:08:36] ...the new indicating or talking or informing what is there,subtle differences is it glaring differences? Talk to us about that.

[00:14:05] I love philosophy, and Aristotle is definitely one of my favorites. So I'm wondering what can Aristotle teach us about persuasion and storytelling?

[00:15:18] "Telos"

[00:20:55] System one and System twos.

[00:21:00] If most of our decisions are very emotional, then how is it that we can make better decisions in spite of this emotional nature that we have?

[00:24:09 How do you define the term "motivated reasoning"?

[00:27:25] What are the differences in the ways that facts and stories activate our brains? Are some other differences in the ways that you know, facts and stories that activate our brain?

[00:31:48] What is a Data story like? Isn't it just the same as a dashboard with visuals or is it something else?

[00:37:19] What are the elements of an insight? How do we go from fact to insight?

[00:40:08] Is there a distinction between just the old fashioned insight and like an actionable insight? How do we distinguish the two?

[00:47:15] What is the "FOUR D" framework?

[00:52:32] We might have an audience member that's a key audience member, and they just want the facts. How do we how do we handle that situation?

[00:59:03] What's the difference between a Data story and a Data forgery?

[01:04:41] You talk about Cognitive Biases, Logical Fallacies in your book, what are these and why are they important to watch out for? Why should we keep an eye out for these things?

[01:10:45-01] It is this it's one hundred years in the future what do you want to be remembered for?

[01:12:23] What are you currently reading?

[01:12:43] What song do you currently have on repeat?

[01:13:26] If you lost all of your possessions, but one, what would you want it to be?

[01:13:48] What's your worst habit?

[01:13:55] What's your favourite, candy?

[01:14:12 What's one of your favorite comfort foods?

[01:14:24 What's something you learned in the last week?

Data Science Happy Hour 71 | 04MAR2022

Harpreet Sahota — Sun, 06 Mar 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/lDh5crPq_Yc

Free Diving into Data Science | Fabrice Mesidor

Harpreet Sahota — Fri, 04 Mar 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Find Fabrice online: https://twitter.com/fabricemesidor
Watch the video of this episode: https://youtu.be/HfPZkuU65OY

Memorable Quotes from the episode:
[00:42:14] "I find that coding and math. I mean, the entire machine learning models today are really complex. So pick something to have fun with. You don't have to be stuck with quickly coding enough to start working on something that you don't like. So you need to take a project that you will have fun. You just enjoy. And the second thing is, don't be scared of the challenge."

Highlights of the show:

[00:01:17] Guest Introduction

[00:02:57] Where you grew up and what it was like there?

[00:05:05] You grew up in Haiti. In high school, what did you think your future would look like? Do you think you'd end up in the middle of winter in York?

[00:07:19] Do you like microeconomics or macroeconomics better? Which one do you do you prefer?

[00:08:55] How did you end up in Papua New Guinea? What was it like working in Papua New Guinea like?

[00:16:48] Share some tips with us on how to remain focused.

[00:18:42] Breaking into data science, you had to really upskill in Python. How did you apply those (excel skills) techniques when you're learning Python?

[00:20:48] Share some tips for public speaking and giving talks about Data science?

[00:30:00] Talk to us about your project idea. How did you get the idea for this project and what was what was your big takeaway from it?

[00:31:54] How'd you get the Data for the movie scripts?

[00:33:23] Applying machine learning to hip hop lyrics, so I thought that was really cool. So walk us through how you came up with the idea for this project.

[00:34:59] While applying machine learning to hip hop lyrics. what was your problem statement? What methodology did you use? Did you did you grab just the lyrics or did you grab the audio? Or did you combine audio and lyrics? How did you piece that project together? What was the big question that you're trying to answer?

[00:37:48] Did you have any type of criteria for which songs to include and which song not to include?

[00:42:11] What can the audience take away from this so they can go create some creative stuff for themselves?

[00:43:55] Do you have some favorite places that you go, some websites or anything like that?

[00:46:45] How do you guys use data science to to to help people like meet their goals?

[00:48:42] It is one hundred years in the future. What do you want to be remembered for that?

Random Round

[00:49:35] If you were to write a fiction novel, what would it be about and what would you title it?

[00:50:21] When do you think the first video to hit one trillion views on YouTube will happen and what will it be about?

[00:51:49] What are you currently reading?

[00:52:29] What song do you have on repeat?

[00:53:47] What are you interested in that most people haven't heard of?

[00:54:42] How long were you able to hold your breath for?

[00:55:23] What would you do on a free afternoon in the middle of the week?

[00:55:32] What's the best thing you got from one of your parents?

[00:56:45] Pancakes or waffles?

Data Science Happy Hour 70 | 25FEB2022

Harpreet Sahota — Sun, 27 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/h2JVRSR22Ec

Resources:

https://conference.measureofmusic.com/

Declassifying the Cheat Codes to Success | Justin Nguyen

Harpreet Sahota — Fri, 25 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Find Justin online: https://www.linkedin.com/in/justingcgu
Watch the video of this episode: https://youtu.be/78sZh6iwoj0

Show Highlights:

[00:01:00] Guest Introduction

[00:01:44] Where you grew up and what was it like?

[00:04:41] It (was) assumed that there are only three possible career choices. Either you can be your doctor, an engineer, or it could be a failure. Was that the same kind of mentality that you had growing up with your parents?

[00:13:22] I'm guessing that person didn't grow up in the internet era to be able to come with these really interesting ideas that you have. What's your thoughts on that? How did you come up with some great ideas that you've discussed.

[00:17:14] Why is career services not the core piece of the college offering?

[00:33:00] Do you think there are some myths out there associated with the ATS applicant tracking system?

[00:43:05] Share some tips on how to make a good LinkedIn headline. Do you have any tips you can share with us for that?

[00:48:05] What cheat code can you share with us with respect to the 'About Me' section?

[00:52:48] it's one hundred years in the future. What do you want to be remembered for?

[00:54:36] What are you most inspired by right now?

[00:55:32] What do you believe that other people think is crazy?

[00:58:00] What song do you currently have on repeat?

[00:58:32] Which fictional place would you most like to go to?

[00:59:28] What is your theme song?

[01:00:14] Do you got any nicknames?

[01:01:03] Who is one of your best friends and what do you love about them?

Data Science Happy Hour 69 | 18FEB2022

Harpreet Sahota — Tue, 22 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp
Happy Hour 69 hosted by Antonio Ivanovski

Watch the video of this episode: https://youtu.be/c1Pd6hK4NoE

Resources:

https://coolhunting.com/style/puma-satori-lux/
https://onthemarkdata.medium.com/making-sense-of-ethereum-data-for-analytics-17655c4859d0
https://www.instagram.com/lizandmollie/?hl=en
https://www.sound.xyz/soulection/untitled-001

No Hard Feelings | Liz Fosslien

Harpreet Sahota — Fri, 18 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/yXCUm7au25U

Memorable Quotes from the episode:

[00:23:18] "Positivity paradox is that when you feel like you have to be positive, you feel worse because you're required to do what's called surface acting, and that's also I think it's very similar to emotional labor. This shows up a lot in customer service jobs. So if a customer is being really rude to you and you kind of put a smile on your face, pretend like they're being totally reasonable and take whatever vitriol they're spitting at you."

Highlights of the Show:

[00:00:46] Guest Introduction

[00:03:16] Talk to us about where you grew up and what it was like there?

[00:07:34] What did you think your future would look like when you grew up?

[00:12:37] Talk to us about distinction between emotional intelligence and being reasonably emotional. What's the difference between these two kind of ideas?

[00:17:40] How do you find space throughout the day to kind of just detach from some of these demands that you have of your time?

[00:23:10] Talk to us about the positivity paradox.

[00:29:07] Can you share some tips for newbies who are coming into an organization where maybe there's already these in-person relationships that have been developed and you're joining a team of colleagues kind of in this remote sense as a person on a screen like how can we develop meaningful work relationships if we're coming into a new environment in this virtual kind of world?

[00:32:11] Talk to us about the user manuals and how can they help with developing and building team cohesion?

[00:33:59] I really like that idea of the user manual but is this something that we can implement regardless of, you know, the depth or length of a work relationship?

[00:37:06] How can we start doing some implementing some of the stuff that we're learning books like yours?

[00:46:31] What are some other tips you might be able to share with that with our audience that find themselves in that situation where they've teammates now?

[00:51:07] How do we go about defining or cultivating a team culture?

[00:56:47] What about those people who just always seem to disagree and question everything that comes out of our mouth, right? How do we deal with with these people?

[01:00:37] How we can use our voices to support the women in Data science and just women in our organizations in general?

[01:04:40] Random round.

[01:04:41] It is one hundred years in the future. What do you want to be remembered for?

[01:07:16] When do you think the first video to hit one billion views on YouTube will happen?

[01:08:35] What are you currently reading?

[01:09:22] How do you effectively tell an accurate story about Data to an audience that might not be Data savvy?

[01:09:33] What song do you have on repeat?

[01:10:00] What languages do you speak?

[01:10:09] Who is one of your best friends and what do you love about them?

[01:11:42] What's the best thing you got from one of your parents Legos?

[01:12:43] What's your go to dance move?

Data Science Happy Hour 68 | 11FEB2022

Harpreet Sahota — Sun, 13 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/Y1xrax0G86c

Resources:
https://intel.wd1.myworkdayjobs.com/External/job/US-Oregon-Hillsboro/Enterprise-Master-Data-Architect---Technical_JR0206661

What AI is Like in the Real World | Alyssa Simpson Rochwerger

Harpreet Sahota — Fri, 11 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp
Find Alyssa online: https://www.linkedin.com/in/sophiaalyssasimpson

Watch the video of this episode: https://youtu.be/IqDE0kZOyag

Memorable Quotes from the Episode:

[00:24:42] "The best Data is is real data that is generated either by humans. Sometimes that's emails or whatever that use case is that you're solving. So I'll take a frequent use case, which is often like prioritization of support. Tickets is a classic model that teams want to build inside a lot of different types of organizations. You have zillions of support cases coming in for, and you want to just categorize them or you want to understand which ones are most severe that need to be answered first."

Highlights of the Show:

[00:01:27] Guest Intro

[00:03:05] You mentioned being an unlikely A.I. leader in your book, please talk to us about that.

[00:04:29] What could possibly go wrong if all we did was focus on creating accurate machine learning systems and just focus on that accuracy metric?

[00:08:08] Can you share some strategies with us for identifying the types of problems that A.I should solve?

[00:11:57] What is the Goldilocks problem? How do we define the Goldilocks problem?

[00:13:21] Can you share some tips with us to understand or tell, at least if a problem is going to be well suited to using machine learning?

[00:24:21] How do we make sure that it's the right data that we're using?

[00:28:34] If we have data that needs annotation, how do we check the quality of those annotations? How do we know where to go to get annotated? Do you have any tips around that?

[00:36:03] Talk to us about the importance of Data strategy.

[00:38:59] How do you deal with challenges like data governance in an organization if you face those?

[00:40:21] Being a woman in tech, if you might be able to just share some advice or words of encouragement for the women.

[00:45:03] Random Round.

[00:45:04] It is one hundred years in the future. What do you want to be remembered for?

[00:45:40] When do you think the first video to hit 1 trillion views on YouTube will happen and what will that video be about?

[00:46:38] In your opinion, what do most people think within the first few seconds of meeting you for the first time?

[00:46:56] What are you currently reading?

[00:47:34] What song do you have on repeat?

[00:48:06] What's your worst habit?

[00:48:30] What's your favorite candy?

Data Science Happy Hour 67 | 04FEB2022

Harpreet Sahota — Sun, 06 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/DnPVmB3vAEM

Resources:
https://docs.python.org/3/library/pdb.html
https://pythonexamples.org/python-breakpoint-example/
https://pythontutor.com/
https://www.bvp.com/atlas/roadmap-data-infrastructure

Decentralization for Data Scientists | Carlos Mercado

Harpreet Sahota — Fri, 04 Feb 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/jsNZCiTGWVM

Memorable Quotes from the show:

[00:04:37] "...When you open the book, the first thing you see is not financial advice. I am not a CFA. I'm on a talent. I have no mechanism to understand your personal financial situation. My goal, which I say in the book, is to transfer my way of thinking to like a book so that you can understand how I think about this stuff in a sort of economics and finance so that you can make an intelligent decision of like what percent of my investment portfolio should they allocate to this crazy bitcoin nonsense."

Highlights of the show:

[00:00:49] Guest Introduction

[00:01:40] How you got interested in blockchain in the first place?

[00:05:17] Who did you write this book for?

[00:06:33] You also talk about some 'stablecoin; What the heck does that even mean?

[00:07:55] What causes the prices of coins go super high and skyrocketing?

[00:10:23] When people talk about blockchain and crypto, can we conflate those two? When I say crypto, does that just mean a coin or does crypto also refer to blockchain?

[00:13:13] Why do we have blockchain when we do have PayPal?

[00:15:06] Talk to us about "Finan".

[00:16:20] What is money and why should inflation affect how we think about money?

[00:23:39] Ethereum.

[00:36:25] What do 'liquidity' and 'correlation' mean and can you help us out with an example?

[00:42:52] What 'loss aversion' is all about? Can you describe this concept? Is that why losing money hurts us because it takes so much more effort to get it back?

[00:44:52] What's the average person want from finance and how can decentralized finance be useful for them?

[00:50:15] Talk about some traits of a good portfolio?

[00:55:06] Concept of how to pick a 'protocol'. What do you look at when you're picking a protocol?

[01:05:06] It's one hundred years in the future. What do you want to be remembered for?

[01:06:21] Talk about some of your interviewing experience(s).

[01:18:43] What are you currently reading right now?

[01:19:38] What is one of your favorite smells?

[01:20:15] What's something you wish you figured out sooner?

Data Science Happy Hour 66 | 28JAN2022

Harpreet Sahota — Sun, 30 Jan 2022 00:00:00 -0500

Support the show: https://www.buymeacoffee.com/datascienceharp

Watch the video of this episode: https://youtu.be/Xeanz9yORZI

Resources:
https://en.wikipedia.org/wiki/5D_optical_data_storage
https://en.wikipedia.org/wiki/The_Feed_(British_TV_series)
https://en.wikipedia.org/wiki/Upload_(TV_series)
https://qr.ae/pGB0pB
https://studios.disneyresearch.com/category/robotics/
https://twitter.com/mxcl/status/608682016205344768?s=21
https://vinvashishta.substack.com/p/machine-learning-is-the-key-to-metaverse
https://www.starwars.com/news/the-mandalorian-stagecraft-feature
https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/
https://www.youtube.com/c/SQLBI

Understanding Data Foundations | Loris Marini

Harpreet Sahota — Fri, 28 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/wvkxi-Et29M
Find Loris Marini online: https://www.linkedin.com/in/lorismarini/
https://www.discoveringdata.com

Support the show: https://www.buymeacoffee.com/datascienceharp

Memorable Quotes from the show:

[00:33:01] "We all matter, we are all part of this and we need one another. I can't do that a science well, if the data is not reliable, if it's not trusted, if it's not connected to the business via metadata by a data management program that touches anyone. And so it's really a mind, a change of of mindset from. You can add value as a team in isolation to, well, not really. Data is the common denominator to everything we do, whether we like it or not. Everything we do generates Data."

Highlights of the Show:

[00:01:27] Guest Introduction.

[00:03:01] Where did you grow up and what was it like there?

[00:05:13] What the heck is quantum photonics and how did you get into that?

[00:08:51] How did you go from awesome, crazy physics stuff into Data science?

[00:12:41] Talk to us about your experience of hardcore physics and research and how did that experience lead you into the Data project?

[00:14:58] What did that look like when you were venturing out as the first data scientist?

[00:17:57] Data architecture. Talk to us about that transition. What was that transition like? What made you be like, "Oh my God, I need to put the Data science down and pick up the Data architect stuff"

[00:21:11] What is the difference between Data engineer and the data architect?

[00:24:47] What do you think a data scientist at a minimum should know about Data architecture and the role that Data architect plays?

[00:35:27] You're talking about the difference between data, information, knowledge and strategy. What's the difference between these? How does data, information or knowledge play into a strategy?

[00:40:44-00:40:46] What's the name of that podcast by Brian O'Neill?

[00:53:03] I love creating machine learning models and then you're trying to do stuff and then you realize that your hands are tied because there's no infrastructure in place; there's no desire or nobody cares about your fancy algorithms and anything like that. How can we start making a culture happen for success?

[00:58:04] Talk about this latter of Data needs that goes from data integration, data access and data transformation kind of walk us through that process and then talk to us about why transformation that part is so hard.

[01:02:03] It's one hundred years in the future. What do you want to be remembered for?

[01:04:00] What do you think is the most mysterious aspect of the universe, which you say that this uncertainty principle is that? Or is there a different thing that is more mysterious than that to you?

[01:05:02] When do you think the first video to hit one trillion views on YouTube will happen and what will that video be about?

[01:05:33] Who do people tell you that you look like?

[01:06:10] What are you currently reading?

[01:07:17] What song do you currently have on repeat?

[01:08:02] What's the last book you gave up on and stopped reading?

[01:08:45] What fictional place would you most like to go to?

[01:09:21] What languages do you speak?

[01:09:25] If you were a vegetable, what vegetable would you be?

The Artists of Data Science Social links:
YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience
Instagram: https://www.instagram.com/datascienceharp
Facebook https://facebook.com/TheArtistsOfDataScience
Twitter: https://twitter.com/datascienceharp

Data Science Happy Hour 65 | 21JAN2022

Harpreet Sahota — Sun, 23 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/NPIjuY_0HYU
Support the show: https://www.buymeacoffee.com/datascienceharp
Resources:

http://ecai2020.eu/papers/348_paper.pdf
https://arxiv.org/pdf/1912.10564.pdf
https://cds.nyu.edu/wp-content/uploads/2019/06/RDS_TentativeSyllabus.pdf
https://dagshub.com/
https://discord.gg/ngNdE5Tvzy
https://diversity.google/annual-report/
https://hal.inria.fr/hal-01522418/document
https://insights.stackoverflow.com/survey/2021#salary-comp-total
https://www.kaggle.com/discussion
https://www.linkedin.com/feed/update/urn:li:activity:6889576309601640448/
https://www.linkedin.com/in/reid-blackman-ph-d-0338a794/
https://www.microsoft.com/en-us/ai/responsible-ai-resources

How to Make Your Data Story ACTIONABLE! | Dr. Joe Perez

Harpreet Sahota — Fri, 21 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/JN7Anqiv2fU
Find Dr. Joe online: https://www.linkedin.com/in/jwperez
Support the show: https://www.buymeacoffee.com/datascienceharp

Memorable Quotes from the Episode:

Highlights of the Show:

Data Science Happy Hour 64 | 14JAN2022

Harpreet Sahota — Sun, 16 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/nLf0_0I6uvU

Resources:

https://abseil.io/resources/swe_at_google.2.pdf
https://register.gotowebinar.com/register/6783119648565141771
https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
https://theartistsofdatascience.fireside.fm/andy-hunt
https://www.benjerry.co.uk/flavours/flavour-graveyard/rainforest-crunch

Telling Your Data Story | Scott Taylor

Harpreet Sahota — Fri, 14 Jan 2022 00:00:00 -0500

Scott Taylor: https://www.linkedin.com/in/scottmztaylor/
https://www.metametaconsulting.com

Watch the video of this episode: https://youtu.be/LZHB6wcAUdg

Memorable Quotes from the Episode:

[00:26:52] "... if the Data science community worked more closely with the Data management community, I think we can, you know, let's stamp out wrangling or at least munching. Let's stamp out munching at least in our lifetime, since so many of those issues that people spend time on could be solved in the Data management side of the house. They may even have that data. I don't know how many times I learned at DB even at DB, where people were just like kind of starting over looking at something, it's like, you know, there's an existing list somewhere."

Highlights of the Show:

[00:01:29] Guest Intro

[00:03:42] Where'd you grow up? What was it like there?

[00:05:07] How did you get education in the United Nations School?

[00:07:06] How how did you get into Data?

[00:07:54] What was the first job you had?

[00:09:52] How did you end up learning about "Data"?

[00:12:56] What are the four Cs you talk about in your book?

[00:13:37] How have databases transformed from the time you started working on it?

[00:29:31] What would be the first thing you do to help your organization start on a path to creating a Data strategy?

[00:46:02] Random Round

[00:46:07] It's one hundred years in the future. What do you want to be remembered for?

[00:47:22] When do you think the first video to hit one trillion views on YouTube. Will happen and what will it be about?

[00:48:41] So what are you currently reading?

[00:48:57 What's something that you watch recently?

[00:50:36] What about music? What do you got on repeat?

[00:51:15] What makes you cry?

[00:56:10] What talent would you show off in a talent show?

[00:58:28] What are you interested in that most people haven't heard of?

[00:58:43] What's your earliest memory?

Data Science Happy Hour 63 | 07JAN2022

Harpreet Sahota — Sun, 09 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/2rKppfByI5c

Resources:

https://datascienceharp.medium.com/i-thought-failure-was-my-destiny-until-i-realized-it-made-me-who-i-am-today-1a8bd4ccb1e2
https://dev.to/arslan_ah/grokking-leetcode-a-smarter-way-to-prepare-for-coding-interviews-5d9d
https://fs.blog/mental-models/
https://github.com/jwasham/coding-interview-university
https://juniortosenior.io/
https://vinvashishta.substack.com/p/assessing-a-data-scientists-coding
https://www.jefflichronicles.com/mental-models
https://youtu.be/4Qta0MyEoYU

Blockchain and Crypto for Data Scientists | Jonathan Reichental

Harpreet Sahota — Fri, 07 Jan 2022 00:00:00 -0500

Watch the video of this episode: https://youtu.be/SButPV_V2-A

Memorable Quotes from the Episode:

[00:20:38] Most of the identifiers that associate a transaction with a person are just a series of letters and numbers, so it's not easy to trace back that Jonathan has sent Harpreet Sahota 1000 or 1000 bitcoin or something, but you can see all the transactions from the very, very beginning and you can export it. You could, you know, any number of data analytics products that you could run against it, just like any data store. One hundred percent, you can affect the data and get right to it, but you can obviously read and if you export it, you can do anything with it.

Highlights of the Show:

[00:00:40] Guest Introduction

[00:02:58] Talk to us a bit about where you grew up and what it was like there.

[00:06:14] How did this love of technology kick-off? How did you get interested in it?

[00:09:15] What is a blockchain and how is this different from what we're used to seeing in Data structures?

[00:15:05] Every time I hear about blockchain in the same sentence, almost they talk about cryptocurrency. So the exact same thing, or can we use them in place of each other? How does this work?

[00:21:35] What are the implications of blockchain technology for data governance data management?

[00:27:02] What is the difference between public permissioned and private blockchains.

[00:45:42] Randon Round.

[00:45:52] What's your favorite piece of clothing that you own?

[00:46:13] Who are some of your heroes?

The Year of Data - 2021 Rewind

Harpreet Sahota — Fri, 24 Dec 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/035XW6k-Kls

00:00:31 Harpreet's Year End Message

Regular Episodes Recap

The Most Internationally listened to episode: https://youtu.be/-5EBk43uWD4

00:04:30 The Smartest Person in the Room | Christian Espinosa
00:05:36 Clearer, Closer, Better | Emily Balcetis
00:06:30 Meditations on Power and Mastery | Robert Greene
00:08:09 Pulling the Grim Trigger | Kevin Zollman
00:08:27 Your Job Doesn't Define YOU | Eleanor Tweddell
00:08:45 Explainable Data Science | Denis Rothman
00:09:03 Choose Who You Become | Chase Caprio
00:09:24 Your Beliefs Aren't Reality | Dave Gray
00:09:58 How to build a Data Science Culture | John K Thompson
00:10:51 Data Science Thunder From Down Under | Steve Nouri
00:11:55 The Philosophy of Sentientism | Jamie Woodhouse
00:12:42 The Shape of Geometry | Jordan Ellenberg
00:13:09 Our Nearest Neighbour | Ken Jee
00:13:40 Learning How To Learn | Barbara Oakley
00:14:04 Skip the Line |James Altucher
00:14:20 How to think like a data science billionaire | John Sviokla
00:15:02 Do What You Love Doing | Lillian Pierson
00:15:46 The Tesstimony | Jonathan Tesser
00:16:33 The Fearless Factor | Jacqueline Wales
00:16:56 Simplify Complexity | David Benjamin
00:17:22 Cultivate Your Rest Ethic | Max Frenzel
00:17:51 The Complete Man | Purdeep Sangha
00:18:26 Tales of a Data Engineer | Dennis Will
00:18:47 Subliminal Motives | Eric Okon
00:19:12 Become a Pragmatic Data Scientist | Andy Hunt
00:20:10 Turn the Lights on Data | George Firican
00:20:47 Give Your Brain Some Space | Tiffany Shlain
00:21:52 Wellness for Data Professionals | Madison Schott
00:22:24 The Industrial Philosopher | Cristina Digiacomo
00:23:05 Turn Ideas into Gold | Steven Cardinale
00:23:37 NLP and Philosophy | Kourosh Alizedah
00:24:17 The Book of Why | Dana Mackenzie
00:24:49 The International Woman of Data - Christina Stathopoulos

The Happy Hours Recap

00:25:40 Question/Answer and Mentoring
00:26:26 What title should be written on the resume?
00:30:44 Sharing the hot seat with friends of the show!
00:31:38 What is the best way to break into research?
00:32:45 How to organize while working?
00:33:19 When will Linkedin content creators start making money?
00:33:47 What's up with the billion hours of YouTube video KPI?

Having Fun

00:36:07 Jeff Li's freestyle rap session!
00:36:56 Insights with Eric
00:37:45 What is the perspective of Data on social media?

00:38:58 Credits

Data Science Happy Hour 62 | 17Dec2021

Harpreet Sahota — Sun, 19 Dec 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/82m6j8ZJgPI

Resources:

https://www.moreintelligent.ai/10kcasts/

The Book of Why | Dana Mackenzie

Harpreet Sahota — Fri, 10 Dec 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/SWSLiGmnpao

Find Dana Mackenzie online:
https://danamackenzie.com
https://scholar.google.com/citations?user=sQhKQ5cAAAAJ&hl=en

Memorable Quotes from the Episode:

[00:20:28] "At one point he realized something very fundamental and remarkable, which is if you switch the fathers and sons and you plot the sons side as the independent variable and the other side is independent variable, you get the same thing, you get the same fuzzy thing and you get the same correlation. And so correlation is something that is completely independent of causation."

Highlights of the Show:

[00:01:22] Guest Introduction.

[00:03:02] Where you grew up and what it was like there?

[00:04:23] As a kid, you loved writing, but then you ended up studying math at like the highest levels. Was that something that you foresaw happening? Were you always into math? Was it like a choice between math and writing? How did this play out?

[00:10:13] if anybody who wants to develop and flex writing muscle, do you have any tips for them on how they can develop and cultivate this skill?

[00:14:18] In view of your book "The book of Why", what is this computational cognitive faculty that humans certainly acquired that our chimpanzee cousins did not?

[00:17:28] Concept of counterfactuals.

[00:24:48] "Every statistics book says correlation is not causation. And they forget to tell you what is causation."

[00:41:55] What is the ladder of causation?

[00:48:57] "Smoking causes cancer", discuss.

[01:01:11] What is the do operator all about? What makes it so revolutionary and special?

[01:16:00] It is one hundred years in the future. What do you want to be remembered for?

[01:17:58] What are you currently reading?

[01:21:13] What song do you have on repeat?

[01:25:29] What is one of your favorite comfort food comfort foods?

[01:25:53] What have you created that you are most proud of?

[01:26:03] Who inspires you to be better?

The Artists of Data Science Social links:
YouTube: https://www.youtube.com/c/TheArtistsofDataScience
Instagram: https://www.instagram.com/theartistsofdatascience/
Facebook https://facebook.com/TheArtistsOfDataScience
Twitter: https://twitter.com/ArtistsOfData

Data Science Happy Hour 60 | 03Dec2021

Harpreet Sahota — Sun, 05 Dec 2021 00:00:00 -0500

Watch the video of this episode: https://www.youtube.com/watch?v=wjueYMuS7kw

Resources:

https://calendly.com/harpreet-comet-ml/30min
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
https://cloud.google.com/architecture?doctype=concept%2Creferencearchitecture
https://craftinginterpreters.com/
https://fullstackdeeplearning.com/spring2021/lecture-11/
https://kubernetes.io/blog/2020/12/02/dockershim-faq/
https://kubernetes.io/blog/2020/12/02/dont-panic-kubernetes-and-docker/
https://missing.csail.mit.edu/2020/version-control/
https://theartistsofdatascience.fireside.fm/kurtis-pykes
https://www.amazon.ca/Software-Architecture-Trade-Off-Distributed-Architectures/dp/1492086894
https://www.youtube.com/watch?v=a6kqyqTNJM4&list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb
https://youtube.com/playlist?list=PLhr1KZpdzukdeX8mQ2qO73bg6UKQHYsHb

The Smartest Person in the Room | Christian Espinosa

Harpreet Sahota — Fri, 03 Dec 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/AAAV0wOLqQo

Find Christian Espinosa online:
https://christianespinosa.com/
https://www.linkedin.com/in/christianespinosa/

Memorable Quotes from the Episode:

[00:24:58] "...the final step is Kaizen. Kaizen is a is a Japanese word that means constant and never ending improvement with any of the six steps prior or the entire methodology. It's a journey, and you're not going to perfect it right out. The gate is taking this first step and the next step and the next step, and then making improvements as you move along. So that's the seven steps to the secure methodology."

Highlights of the Show:

[00:01:15] Guest Introduction.

[00:02:43] Where you grew up and what it was like there?

[00:05:43] Does Christian has the crazy interest to climb mountains?

[00:06:13] When you're growing up as a kid man, did you ever think that you'd be this crazy ultra marathon running Iron Man, mountain climbing cybercriminal fighting awesome individual?

[00:06:48] Where does that self rigor to be able to want to put yourself through these really challenging types of situation come from?

[00:09:19] What does it mean to be the smartest person in the room? What does that mean to you and when is it a bad thing?

[00:12:33] Is there a correlation or a relationship between the need to be the smartest person in the room and having like a fixed mindset?

[00:14:14] Who are these "paper tigers" and why are they so dangerous?

[00:19:20] How can you tell that somebody knows what their 'why' is? How do you assess for fit against a cultural fit?

[00:20:53] What is "secure methodology"? What are the seven steps involved in it?

[00:31:08] Do you think it's possible to identify whether we have a real growth mindset or a false one?

[00:33:02] Being congruent with your belief and the philosophy behind growth mindset.

[00:33:57] What are these NLP presuppositions in the context of your secure methodology?

[00:35:00] What are your top two favorite presuppositions for the communication part of the security framework?

[00:39:49] What is mono tasking?

[00:43:17] What are some of the NLP presuppositions that we can use to remind ourselves that it is time to get down to to multitasking?

[00:45:28] What are a couple of presuppositions that we should have in mind for the Kaisen?

[00:46:38-00:46:38] Talk to us about the four phases of kaizen.

[00:50:57] It is one hundred years in the future. What do you want to be remembered for?

[00:51:37] Random Round.

[00:51:37] When do you think the first video to hit one trillion views on YouTube will happen and what will it be about?

[00:52:11] What do most people think within the first few seconds of meeting you for the first time?

[00:52:41] What are you currently reading?

[00:53:35] What song do you currently have on repeat?

[00:53:59] What's your earliest memory?

[00:54:32] When was the last time you changed your opinion about something major?

[00:55:37] What's the best piece of advice you have ever received?

[00:56:29] What's the right way going about finding a mentor in your experience?

[00:59:01] Who was your favorite teacher and why?

NLP and Philosophy | Kourosh Alizadah

Harpreet Sahota — Fri, 26 Nov 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/3GG9snF8p7o

Find Kourosh Alizadah online:
https://www.linkedin.com/in/kcalizadeh/
https://philosophydata.com/

Memorable Quotes from the Episode:

[00:20:22] "...one word that's very commonly used in philosophy is the word substance and in everyday language. It just means like stuff. But in philosophy, it means like the substrate upon which all the properties change, right? So like what is the substance of a stone that stays the same even when it changes color or breaks or something like that."

Highlights of the Show:

[00:01:16] Guest Introduction.

[00:03:34] Where you grew up and what it was like there?

[00:04:41] How did you figure out who you want to be? - What did you think your feature is going to look like?

[00:06:13] Do we still have philosophers who study "philosophy and ideas"?

[00:07:59] The philosophy of Data science is if we had to kind of pin that, would there be a philosophy to Data science or of Data science?

[00:09:22] What is Data? How is it different from information or data and information? Are they the same thing?

[00:10:29] The concept of "philosophy data project".

[00:11:41] Transition from a capstone project to flat iron Data science boot camp.

[00:18:39] Did you actually read a lot of books?

[00:24:25] What are prediction probabilities?

[00:55:10] Random Rround

[00:55:12] When do you think the first video to hit $1 trillion views on YouTube will happen and what will it be about?

[00:56:07] What do most people think within the first few seconds of meeting you for the first time?

[00:56:24] What are you currently reading right now?

[00:57:18] What song do you currently have on repeat?

[00:58:29] Pet, peeves.

[00:58:37] Who are some of your heroes?

[00:59:30] When people come to you for help, what do they usually want help with?

[01:00:01] If you lost all of your possessions, but one, what would you want it to be?

[01:00:14] What fictional place would you most like to go to?

Data Science Happy Hour 59 | 19NOV2021

Harpreet Sahota — Sun, 21 Nov 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/SOW9wUY3FpA

Resources:

https://medium.com/@grepdennis/how-a-sql-database-engine-works-c67364e5cdfd
https://medium.com/building-the-metaverse/evolution-of-the-creator-economy-9e038e8411af
https://medium.com/data-driven-fiction
https://snap.stanford.edu/data/roadNet-CA.html
https://theartistsofdatascience.fireside.fm/guests/anderson-silver
https://theartistsofdatascience.fireside.fm/guests/donald-j-robertson
https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309
https://www.amazon.com/INSPIRED-Create-Tech-Products-Customers/dp/1119387507/ref=sr_1_1?keywords=inspired&qid=1637361494&s=books&sr=1-1
https://www.amazon.com/The-Feed-Season-1/dp/B086HVT7JH
https://www.hel.fi/uutiset/en/kaupunginkanslia/a-new-minecraft-city-model-introduces-helsinki-in-more-detail
https://www.linkedin.com/in/dkjapan/
https://www.tigergraph.com/resources/
https://www.youtube.com/watch?v=YT0CScFzp1o

Turn Ideas into Gold | Steven Cardinale

Harpreet Sahota — Fri, 19 Nov 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/cSOXStI5sjg

Find Steven Cardinale online:
https://twitter.com/scardinale
https://www.linkedin.com/in/stevencardinale/

Memorable Quotes from the Episode:

[00:53:20] "I was talking to somebody the other day and I said, What are you selling because I'm selling media coverage for football teams? I'm like, OK, great, you know, because all football teams need people to know where they're at. Nothing but what are you really selling? Well, I'm selling it to mostly the high school teams, and really what I'm selling is, you know that parents can see their kids and media coverage. Great. What are you selling? It took him a minute and goes, Well, I'm selling the fact that parents are spending money to be have their kids on a football team. They want to see their kids names in the newspaper. So now we're starting to understand something a little more interesting."

[00:11:21] "...if you think about a data scientist, you guys are alchemists, people who work with, you know, the big data lakes and the uncertainty of data and then convert it into a decision that is the essence of alchemy."

Highlights of the Show:

[00:01:24] Guest Introduction.

[00:04:43] Where you grew up and what it was like there?

[00:04:41] How did you figure out who you want to be?

[00:07:34] What are the two definitions of entrepreneurship as mentioned in your book?

[00:12:34] What are the terms Prima Materia and the Philosopher's Stone. How is it that they fit into this three step process?

[00:40:14] The "Albedo stage". What's so unique about this stage?

[00:43:03] The idea of pollination and how it helps us grow.

[00:48:40] "Ego is the enemy."

[00:49:51] When we're moving through these three stages, like, do they happen sequentially, concurrently, all over the place? How long should we be spending each?

[00:51:44] One part that I really enjoyed was just coming up with better questions because I feel like this is something that I've heard from my mentees. They really struggle with is like, they don't even know why questions are important, let alone how to even come up with better questions. So can you share some tips on how we can do that in our work?

[00:55:43] The rubato mindset. How is this different from the other parts that we've discussed?

[01:00:29] What are some tips you can share with us for how to use and implement these ideas that you talk about?

[01:02:18] It's one hundred years in the future. What do you want to be remembered for?

[01:03:00] When do you think the first video to hit one billion views on YouTube will happen? What's it going to be about?

[01:05:01] What are you currently reading?

[01:06:12] What songs do you currently have on repeat?

[01:07:15] What's your go to dance music?

[01:07:44] What is one of your favorite smells?

[01:07:58] In your group of friends. What role do you play?

[01:09:05] What's the best piece of advice you have ever received?

Data Science Happy Hour 58 | 12NOV2021

Harpreet Sahota — Sun, 14 Nov 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/IRkGuRMnZ6o

Resources:

https://fossa.com/blog/analyzing-legal-implications-github-copilot/
https://github.com/jupyter-naas/awesome-notebooks
https://hbr.org/2009/01/picking-the-right-transition-strategy
https://papermill.readthedocs.io/en/latest/

Turn the Lights on Data | George Firican

Harpreet Sahota — Fri, 12 Nov 2021 00:00:00 -0500

Watch the video of this episode: https://youtu.be/6UJED0scgy4

Find George Firican online:
https://twitter.com/georgefirican
https://www.linkedin.com/in/georgefirican

Memorable Quotes from the Episode:

[00:42:51] "So I think everything needs to start on the business side first, so ideally, that's very clear for everybody where the business with a five year plan, if you will, for the business is so that anything else is a strategy to support that plan, right? Otherwise, it's kind of just wishful thinking. If if you want to go to Mars from a Data perspective, how can you create models for the company to be able to do that? But then if the company doesn't want to get there, then it's pointless. So that's why it's you need a business to take that first step."

Highlights of the Show:

[00:01:29] Guest Introduction.

[00:02:53] Where you grew up and what it was like there?

[00:04:02] What did you think your future was going to look like at the age of 15?

[00:08:2] What was the nudge that got you into Data? What was the experience that you had that made you realize that Data was right for you as a great teacher?

[00:09:45] As data scientist, machine learning practitioners, we're end users of the data, right?

[00:12:22] What the heck is Data governance?

[00:14:26] Responsibilities of a data analyst.

[00:15:47-00:15:50] Can anybody be a data steward? What does a data steward mean?

[00:19:33] Metadata, master data, what are those? What do they have to do with data governance?

[00:22:19] Why should Data scientists care about these types of data?

[00:23:48] Discuss data governance in action in the workplace.

[00:27:28] When you say business driver, what does that mean?

[00:29:1] So what is the goal of the organization at a high level?

[00:30:02] What are your concerns around data governance or is there kind of a a business way to ask the question so that we can translate it into our own lingo?

[00:31:06] Why is it so painful to get to have the report or access them from a dashboard in a timely fashion?

[00:33:14] What would be the types of individuals that we would want to see on the council?

[00:35:11] What are the biggest challenges you foresee her facing when he's starting out a Data strategy at this massive organization?

[00:37:05] What can Stephen King teach us about Data governance?

[00:38:41] What are Data Management and other such principles? How do we identify these principles?

[00:41:18] What does Data strategy have to do with helping us get ahead in our Data careers?

[00:42:24] How can we help our organizations define a data strategy if we find ourselves in this position of having to to build a Data strategy?

[00:43:30] Are there any blueprints that exist to help create a Data strategy?

[00:44:24] What the heck are the maturity models like?

[00:45:48] Can we have the George tech and maturity model? Does that exist?

[00:50:37] What is the difference between data scientists and data analysts?

[00:53:33] Does data governance care about unstructured data or is it only about structured data; how's that?

[00:54:32] It's 100 years in the future. What do you want to be remembered for?

[00:54:59] When do you think the first video to hit one billion views on YouTube will happen, and what will it be about?

[00:55:55] What do most people think within the first few seconds of meeting you for the first time?

[00:56:46] What are you currently reading?

[00:58:13] Pet peeves?

[00:58:44] What's on your bucket list this year?

[01:00:35] Do you ever sing when you're alone?

Data Science Happy Hour 57 | 05NOV2021

Harpreet Sahota — Sun, 07 Nov 2021 00:00:00 -0400

Watch the video of this episode: https://youtu.be/t4HevyAyMbo

Resources:

https://www.amazon.com/Superminds-Surprising-Computers-Thinking-Together/dp/0316349135
https://www.forbes.com/sites/bernardmarr/2021/10/27/glenfiddich-sells-18000-super-rare-whisky-as-nfts--heres-what-that-means/

The Industrial Philosopher | Cristina Digiacomo

Harpreet Sahota — Fri, 05 Nov 2021 00:00:00 -0400

Watch the video of this episode: https://youtu.be/Zm2wrWgKn_g
Find Cristina Digiacomo online: https://www.linkedin.com/in/cristinadigiacomo

Memorable Quotes from the Episode:

[00:36:55-00:36:55] "... I know that's sort of like a pithy answer, but it's the truth. Our thoughts shape our reality. This is a very fundamental idea and concept from many, many, many, many philosophers across the millennia. We shape the circumstances in our lives just by the way that we look at them."

[00:23:14-00:23:16] "Philosophy s not just about thinking, it's about acting and acting appropriately. And so all those four things, you know, the perception of the truth and the truth. Managing your thoughts being deliberate and acting accordingly. Wisdom is the word for all of that."

Highlights of the Show:

[00:01:12] Guest Introduction

[00:02:54] Where did you grow up and what it was like there?

[00:07:08] How did you get into the DJ world?

[00:14:24] How did you get into into philosophy?

[00:15:41-00:15:41] Why is it that philosophy and wisdom [they] get lumped into these categories of being like "Woo Woo" out there? Why do you think that is?

[00:16:41] How do you define philosophy?

[00:19:46-00:19:52] Speaking of being wise, what is what is the difference between being wise and acting wise?

[00:24:23-00:24:25] How do we pause? How do we first of all, get to wisdom? How do we mitigate that knee jerk reaction?

[00:26:26] Talk to us about clarity as discussed in your book.

[00:28:52] Did you encounter any struggles when you're first trying to think in this way? I guess almost like metacognition, thinking about the way you're thinking and forcing yourself to answer these questions? Was that a bit of a challenge for you? And how did you overcome that?

[00:34:12] What are your thoughts on constantly being in thought?

[00:36:15] How can we help ourselves find out when we're having those detrimental thoughts and natural way back into something more productive, right?

[00:38:57-00:38:58] In your book you're talking about how people get really attached to their thoughts and their ideas. How can we avoid that?

[00:39:19] How do thought patterns affect our activities and what are some detriments of that?

[00:46:03] What is the real flow and how can we distinguish that from a fake flow?

[00:48:03] We talked about the importance of of inaction being just as important as as action. But if you were to just spelll it out clearly for us here, why is it that this inaction is just as important as as the action?

[00:49:48] What has philosophy taught you about being a better strategist?

[00:56:02] Is wisdom a trait that can be cultivated?

[00:56:27] Where can we cultivate this act of being wise everywhere that we are? Do we do it alone by ourselves as we interact with other people? How can we can we do that?

[00:57:20] What do you want to be remembered for?

[00:58:05] What do you think the first video to hit one billion views on YouTube will be about? And when will that happen?

[00:58:43] What do you think most people think within the first few seconds of meeting you?

[00:59:46] What are you currently reading?

[01:01:28] What languages do you speak?

[01:01:38] What's the story behind one of your scars?

[01:02:09] What's your favourite candy?

Data Science Happy Hour 56 | 28OCT2021

Harpreet Sahota — Sun, 31 Oct 2021 00:00:00 -0400

Watch the video of this episode: https://youtu.be/iHhULKL_iAU

Resources: 🔗

https://www.makeovermonday.co.uk/
https://github.com/amirziai/sklearnflask/
https://falconframework.org/
https://substack.com/profile/16324927-vin-vashishta
https://vinvashishta.substack.com/p/leadership-essentials-setting-clear
https://towardsdatascience.com/design-a-federated-learning-system-in-seven-steps-d0be641949c6#3c54-b11966999cd5-reply
https://github.com/phecy/SSL-FEW-SHOT

Become a Pragmatic Data Scientist | Andy Hunt

Harpreet Sahota — Fri, 29 Oct 2021 00:00:00 -0400

Watch the video of this episode: https://youtu.be/R4Xr5OiVuzo

Find Andy online:
https://twitter.com/PragmaticAndy
https://www.linkedin.com/pragmaticandy

Memorable Quotes from the episode:

[00:04:41-00:04:43] "I probably discovered computers around seventy six, seventy seven or so. In fact, the very first computer I ever experienced up close and personal not just read about was a teletype typewriter with an acoustic color. And you know, the kids in the audience are going, What's an acoustic coupler, grandpa? Right? If you remember the movie war games, you dial the phone with a handset and you stick a handset down in these foam cups and it goes. It makes the noises, and that's how you talk to the big giant mainframe, you know, and then literally in the next county over. So that was sort of my first, my first experience and I was hooked."

[00:35:54] "You are told in school that you're a dummy, that you can't ever learn anything that you're not good at Math. Oh my God. You know, I'm not a violent person, but I would shoot every teacher who told some girl, Oh, girls aren't good at math. You don't need to study that right? Bullshit. Hundred percent legit bullshit. The problem with that Is if you've been told by an authority figure, by a teacher that you can't learn something and then you believe it, then your brain wires itself so that you're not going to be able to learn it. It's a self modifying machine and it becomes a self-fulfilling prophecy. Which, to me, it's criminal. It's absolutely criminal."

Highlights from the show:

[00:01:32] Guest Introduction

[00:02:40] Talk to us a little bit about where you grew up and what was it like there.

[00:05:54] From the early age, did you just decide I'm going to go to school and I'm going to study computers and software?

[00:11:00] What is it with this pragmatic stuff? What does that mean to you?

[00:13:14] What if you're somebody who's just been constrained by your processes?

[00:20:16] Is it possible for me to be able to think like a software engineer, like an exceptional software engineer or software developer without necessarily being one?

[00:34:04] What is expertize and why is it so difficult to articulate?

[00:38:25] Can you talk to us about what Dreyfus model is and and why is it important that we understand this Dreyfus model?

[00:41:58] What's a good way for us to kind of accurately self assess where we would fall on this spectrum?

[00:54:18] How was it that you got interested in these type of books (pragmatic thinking) and how have they helped you and your journey?

[00:58:15] It's one hundred years in the future. What do you want to be remembered for?

[01:00:50] What is GROWS method?

[01:06:23] What are you listening to right now? What do you have on kind of repeat?

Data Science Happy Hour 55 | 22OCT2021

Harpreet Sahota — Sun, 24 Oct 2021 00:00:00 -0400

Resources: 🔗

https://missing.csail.mit.edu/
https://www.amazon.com/Pro-Git-Scott-Chacon-ebook/dp/B01ISNIKES/
https://progit2.s3.amazonaws.com/en/2016-03-22-f3531/progit-en.1084.pdf
https://www.udemy.com/course/git-and-github-bootcamp/
https://github.com/romkatv/powerlevel10k
https://github.com/ericgitonga/code-snippets
https://twitter.com/HBOMaxHelp/status/1405712235108917249
https://fossbytes.com/linus-torvaldss-famous-email-first-linux-announcement/
https://theartistsofdatascience.fireside.fm/greg-coquillo
https://hbr.org/2009/01/picking-the-right-transition-strategy
http://www.jefflichronicles.com/blog/2020/9/26/i-got-7-job-offers-during-the-worst-job-market-in-history-heres-the-data

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh
Register for Sunday Sessions here: http://bit.ly/comet-ml-oh
Listen to the latest episode of Emily Balcetis: http://theartistsofdatascience.fireside.fm/emily-balcetis

Links:

Clearer, Closer, Better | Emily Balcetis

Harpreet Sahota — Fri, 22 Oct 2021 00:00:00 -0400

Find Emily online: https://www.psychologytoday.com/us/contributors/emily-balcetis-phd
Video of this episode: https://youtu.be/5SrfAxUHLyw

Highlights of the show:

[00:00:20] Guest Introduction

[00:02:40] Where did you grow up and what was it like there?

[00:07:09] Is life different now than what you imagined it would be?

00:10:02] At a high level, how does this experience we call site work? Is it just like, seeing things? And that's it, like what's going on in my head when I'm looking at something?

[00:14:21] You talk about these four different ways that are our sight helps us achieve our goals. Can you talk about them at a high level?

[00:28:13] Once we do find that one thing we want to focus on, like, how do we push ourselves through to the finish line?

[00:38:36-00:38:41] Can you help us understand what narrow focus is as knowledge workers? How can we use narrow focus to help us achieve our goals?

[00:45:55] how do we concretely identify a definitive moment of success before starting our journey?

[01:01:39] Positive feedback can sometimes backfire, right when we're pursuing our goals. What what is it about that?

[01:09:30] It is one hundred years in the future. What do you want to be remembered for?

[01:10:33] Random Round

[01:10:37] What's on your bucket list this year?

[01:10:52] What makes you cry?

[01:11:09] What's one of your favorite comfort foods?

[01:11:27] What's the last book you gave up on and stopped reading?

[01:12:08] What are you currently reading?

[01:12:45] What song do you got on repeat nowadays?

*The Artists of Data Science Social links: *

YouTube: https://www.youtube.com/c/TheArtistsofDataScience
Instagram: https://www.instagram.com/theartistsofdatascience/
Facebook https://facebook.com/TheArtistsOfDataScience
Twitter: https://twitter.com/ArtistsOfData

Data Science Happy Hour 54 | 15OCT2021

Harpreet Sahota — Sun, 17 Oct 2021 00:00:00 -0400

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Listen to the latest episode: http://theartistsofdatascience.fireside.fm/john-vervaeke

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Subliminal Motives | Eric Okon

Harpreet Sahota — Fri, 15 Oct 2021 00:00:00 -0400

Visit Eric online: https://twitter.com/iamericokon?lang=en
Video of this episode: https://youtu.be/k6KOE4VDVpg

Highlights of the Show:

[00:00:53] Guest Introduction

[00:01:38] Where did you grow up and what was it like there?

[00:04:05] When you're coming up in high school, like, was this kind of expected of you to go into the family business? Was it that big at that time?

[00:05:53] What kind of jobs did you have coming up in the family business?

[00:11:48] How did you go from working in the family business to eventually getting into the podcast game? What was the most that transition?

Eric: [00:31:32] Are you a believer in the law of attraction?

Eric: [00:34:45] ...because you're a scientist, are you spiritual?

Eric: [00:36:53] Have you ever been to mediums or anything like that?

[00:41:16] Would you rather be stuck on a broken ski lift or a broken elevator?

[00:42:12] What are you interested in that most people haven't heard of?

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Data Science Happy Hour 53 | 08OCT2021

Harpreet Sahota — Sun, 10 Oct 2021 00:00:00 -0400

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Listen to the latest episode: http://theartistsofdatascience.fireside.fm/john-vervaeke

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Data Science Happy Hour 52 | 01OCT2021

Harpreet Sahota — Sun, 03 Oct 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 31 | 26SEP2021

Harpreet Sahota — Thu, 30 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout Purdeep Sangha's episode here: https://theartistsofdatascience.fireside.fm/purdeep-sangha
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

YouTube: https://www.youtube.com/c/TheArtistsofDataScience
Instagram: https://www.instagram.com/theartistsofdatascience/
Facebook: https://facebook.com/TheArtistsOfDataScience
Twitter: https://twitter.com/ArtistsOfData

Data Science Happy Hour 51 | 24SEP2021

Harpreet Sahota — Sun, 26 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 30 | 19SEP2021

Harpreet Sahota — Thu, 23 Sep 2021 00:00:00 -0400

Data Science Happy Hour 50 | 17SEP2021

Harpreet Sahota — Sun, 19 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 29 | 12SEP2021

Harpreet Sahota — Thu, 16 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout Max Frenzel's episode here: https://theartistsofdatascience.fireside.fm/max-frenzel
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Data Science Happy Hour 49 | 10SEP2021

Harpreet Sahota — Sun, 12 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Data Science Happy Hour 48 | 03SEP2021

Harpreet Sahota — Sun, 05 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 28 | 29AUG2021

Harpreet Sahota — Thu, 02 Sep 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout Jeffery Li's episode here: https://theartistsofdatascience.fireside.fm/jeff-li
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Data Science Happy Hour 47 | 28AUG2021

Harpreet Sahota — Sun, 29 Aug 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 27 | 22AUG2021

Harpreet Sahota — Fri, 27 Aug 2021 00:30:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout Jonathan Tesser's episode here: https://theartistsofdatascience.fireside.fm/jonathan-tesser
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Learn How to Use Publicly Available Web Data | Or Lenchner

Harpreet Sahota — Thu, 26 Aug 2021 00:00:00 -0400

Bright Data is a leading publicly available web data platform that enables organizations of all sizes to access data on the internet with complete transparency

Learn more about Bright Data here: https://brightdata.com/

Learn about the company DNA here: https://brightdata.com/dna

Follow Or on LinkedIn: https://www.linkedin.com/in/orlenchner/

Or on Twiter: https://twitter.com/orlench

Get FREE sample datasets here: https://brightdata.com/products/data-sets

[00:01:28] Guest Introduction

[00:03:39] Talk to us about some of the wave you had to surf you had on the ride to here.

[00:06:00] What's the importance that surfing has had in your life?

[00:12:19] "You don't need to reinvent everything from scratch"

[00:12:55] How do you develop this elusive skill of product?

[00:18:42] How do you define publicly available data?

[00:27:59] What are some some ways that Bright Data help get transparent view of the web?

[00:30:44] What is alternative data?

[00:33:58] How do you handle situations where people come and want to use data for some sketchy or shady things?

[00:38:41] Are there any major shifts or trends in data collection?

[00:41:07] Do you have any other success story just like with the HTI organization?

[00:42:17] Where can people go to apply for the roles in Bright Data?

[00:49:29] How can the benefits of open data be communicated to new audiences so that government data can be combined with Important privately owned data?

[00:53:31] Would you think it would it would ever happen would ever be the case that we have something similar to GDPR?

[00:54:00] Random Round

[00:54:55] What do you want to be remembered for?

[00:55:31] When do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?

[00:56:27] So what's your favorite question to ask a candidate during a job interview and why?

[00:57:42] In the data collection process and most of it is manually entered, the quality and consistency of this Data is poor. What are your thoughts? How can we improve this?

[01:00:05] Strategic thinking, the teacher planning, strategic learning, which is most important for you and why?

[01:01:36] What are you currently reading?

[01:03:43] What's the story behind one of your scars?

[01:04:04] What issue will you always speak your mind about?

[01:04:26] Best piece of advice you've ever received?

datascience #machinelearning #ai #data #analytics #dataanalytics #mlops #artificialintelligence

community #mindset #philosophy #success

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Data Science Happy Hour 46 | 20AUG2021

Harpreet Sahota — Sun, 22 Aug 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 26 | 15AUG2021

Harpreet Sahota — Thu, 19 Aug 2021 00:00:00 -0400

Data Science Happy Hour 45 | 13AUG2021

Harpreet Sahota — Sun, 15 Aug 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 25 | 08AUG2021

Harpreet Sahota — Thu, 12 Aug 2021 00:00:00 -0400

Data Science Happy Hour 44 | 06AUG2021

Harpreet Sahota — Sun, 08 Aug 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 24 | 01AUG2021

Harpreet Sahota — Thu, 05 Aug 2021 10:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout Lillian Pierson's episode here: https://theartistsofdatascience.fireside.fm/lillian-pierson
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Data Science Happy Hour 43 | 30JUL2021

Harpreet Sahota — Sun, 01 Aug 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Data Science Happy Hour 42 | 23JUL2021

Harpreet Sahota — Sun, 25 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 23 | 18JUL2021

Harpreet Sahota — Thu, 22 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards
Checkout James Altucher episode here: https://theartistsofdatascience.fireside.fm/james-altucher
Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Data Science Happy Hour 41 | 16JUL2021

Harpreet Sahota — Sun, 18 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

https://www.instagram.com/theartistsofdatascience/
https://facebook.com/TheArtistsOfDataScience
https://twitter.com/ArtistsOfData

Comet ML Office Hour 22 | 11JUL2021

Harpreet Sahota — Thu, 15 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

Data Science Happy Hour 40 | 09JUL2021

Harpreet Sahota — Sun, 11 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

Comet ML Office Hour 21 | 04JUL2021

Harpreet Sahota — Thu, 08 Jul 2021 00:00:00 -0400

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

Data Science Happy Hour 39 | 02JUL2021

Harpreet Sahota — Sun, 04 Jul 2021 00:00:00 -0400

Get 70% off of Data Science Dream Job today! http://dsdj.co/artists70

Vote in the data community content creators awards! http://bit.ly/data-creators-awards

Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

The Artists of Data Science YouTube: https://www.youtube.com/c/TheArtistsofDataScience

The Data Girl | Ashley M. Scott

Harpreet Sahota — Thu, 01 Oct 2020 00:00:00 -0400

Ashley's a Data Analyst collaborating with administrators and medical professionals to develop impactful analysis utilizing data mining, visualizations, and modeling to drive business solutions.

She’s a passionate advocate for educating women regarding data career opportunities and spreading awareness about the advancement of women in the tech industry.

And she's a Forbes Under 30 Scholar!

WHAT YOU'LL LEARN

[00:05:32] The importance of cultivating the right mindset

[00:17:04] Privacy, biometrics, and data

[00:25:03] How to become a Forbes under-30 scholar

[00:27:09] The unique experiences of a health care data analyst

[00:30:54] Bridinging the patient satisfaction gap with data

[00:43:57] Emotional intelligence in data science

FIND ASHLEY ONLINE

LinkedIn: https://www.linkedin.com/in/ashleym-scott/

Instagram: https://www.instagram.com/datagirlash/

Twitter: https://twitter.com/datagirlash

SHOW NOTES

[00:01:35] Introduction for our guest today

[00:02:49] The journey into analytics

[00:07:50] The data hype cycle

[00:11:09] How do you see data analytics impacting the health care industry in the next two to five years?

[00:17:04] Privacy, biometrics, and data

[00:21:24] What do you think will separate the great Data scientists from the merely good ones?

[00:25:03] How to become a Forbes under-30 scholar

[00:27:09] The unique experiences of a health care data analyst

[00:30:54] How is Data bridging the gap between medical education and patient satisfaction?

[00:32:51] Health care data analyst project ideas

[00:39:08] How to decide your data science career path

[00:43:57] Emotional intelligence in data science

[00:48:01] What are some common mistakes that you see people make when visualizing their data?

[00:50:37] Communicating with non-technical audience

[00:54:17] Openly communicate with your teammates

[00:56:26] Being a woman in data science

[00:59:12] The Women in Data Science organization

[01:05:26] Fostering inclusion of women in data science

[01:07:45] What's the one thing you want people to learn from your story?

[01:09:00] The lightning round

Special Guest: Ashley M. Scott.

Work Less and Get More Done | Alex Pang

Harpreet Sahota — Thu, 24 Sep 2020 00:00:00 -0400

Dr. Alex Pang studies people, technologies, and the worlds they make. Since 2000 he's worked as a technology forecaster and futurist, helping companies understand new technologies and global trends, and their strategic and business implications.

QUOTES

"The challenge is figuring out how it's going to play out in different industries or different parts of the world, thinking about how we can control and shape those technologies and their users so that they give us more flexibility, more autonomy, more freedom, as opposed to just eliminating our jobs or doing other bad things." ] [00:09:11]

"I think that the this is we live in a world that doesn't take work seriously, but we also live in a world that provides us with all the tools necessary to figure out how to harness rest and bring it back on our lives and use it as something that makes our lives better and makes our work better. " [00:16:53]

"One of the other things, though, is that Ericsson found was that not only the top performers practiced differently, they also rested differently. They actually slept more than average performers..." [00:19:52]

"And why that's important is that our creative minds seem to do better when - with these routines. Stephen King has this line about how the muse will descend if it knows that you're working." [00:34:18]

"Basically, intensive periods of focused work be periods of long semi distracted work. Knowledge work is a little bit more like high intensity interval training than like running a marathon. It turns out that intense-ivity turns turns out to be a better route to higher performance and better results than the long, long grind. [00:50:41]" [00:50:16]

FIND ALEX ONLINE:

LinkedIn: https://www.linkedin.com/in/askpang/

Twitter: https://twitter.com/askpang

SHOW NOTES

[00:02:16] Introduction for our guest

[00:03:16] How Alex got so interested in the role of rest in creative lives

[00:06:36] Where do you see technology headed in the next two to five years?

[00:09:32] Society’s biggest concern with technology in the next 2-5 years.

[00:12:03] What can we do now and perhaps going into the future to mitigate our distraction from technology

[00:14:41] What is rest and what's the problem with it?

[00:17:13] The problem with the “hustle culture”

[00:18:54] Deliberate practice, deliberate rest

[00:20:42] Why is it that rest is important for those of us who don't use our bodies or tactile kind of appendages, we use our brains?

[00:23:02] The default mode network of the brain

[00:27:51] How can we convince our boss that all we need is a solid four hours?

[00:29:11] What are some horrible ways that people are resting and we should probably stop resting that way?

[00:33:42] How does having a daily routine help us be more creative? How does that help us be more productive?

[00:36:44] I talk about my struggles with my morning routine

[00:37:50] What is the design thinking framework?

[00:42:54] How can this framework then help us work better, smarter and less?

[00:49:02] How to work more effectively as a knowledge worker

[00:50:42] Flex time is not really that great

[00:52:44] What's the one thing you want people to learn from your story.

[00:53:36] Lightning round. What is your favorite way to rest?

[00:53:46] If you could put up a billboard anywhere in the world, what would it say and why?

[00:54:00] What something you believe that other people think is crazy.

[00:54:50] What would you say is the most bizarre aspect or quality of the human mind that you've come across?

[00:56:04] An interesting topic you should study

[00:56:25] What's the number one book you'd recommend our audience read and your most impactful takeaway from it?

[00:57:12] If you could somehow get a magic telephone that allows you to to contact 20 year old Alex, what would you tell him?

[00:58:20] What does creativity have to do with being a good scientist?

[00:59:34] What song do you have on repeat right now?

[01:01:02] What's the best advice you've ever received?

[01:01:48] Where to find Alex online

Special Guest: Alex Pang, PhD.

Emotional Intelligence for Data Scientists | Gilbert Eijkelenboom

Harpreet Sahota — Mon, 21 Sep 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Gilbert Eijkelenboom, an author and behavioral economist that is passionate about bridging the gap between analytical thinkers and emotional intelligence. His passion for psychology and numbers has led him to understand the need for analytical minds to become better at communication with people.

He gives insight into how your brain works, his methods for getting great feedback, and the importance of emotional intelligence.

Gilbert talks about his background as a poker player, and how human behavior impacts the success that is possible in data science and beyond. This episode brings an interesting and very important perspective into soft skills and actionable tips to implement into your daily workstream.

WHAT YOU'LL LEARN

[12:14] How your brain works and influences daily decisions

[18:58] The importance of saying no

[21:38] What is emotional intelligence and how it impacts your personal and professional life

[37:26] How to identify your bright spot

[44:21] The three step process to change your algorithms

[46:58] Gilbert’s take on intrapreneurship

QUOTES

[21:38] “...to become a really good data scientist, you need to understand the business problem...and without emotional intelligence, it's going to be very difficult.”

[24:53] “...if you don't try it yourself and fail and learn and experiment, then you're never going to be good…”

[53:12] “every day you make small decisions that all combine to really big growth”

FIND GILBERT ONLINE:

Website: https://www.mindspeaking.com/

Quora: https://www.quora.com/profile/Gilbert-Eijkelenboom

LinkedIn: https://www.linkedin.com/in/eijkelenboom/

SHOW NOTES

[00:01:35] Introduction for our guest

[00:02:45] How Gilbert went from poker pro to data dude

[00:04:48] Where do you see the field of analytics and data science headed in the next two to five years?

[00:06:09] The difference between good and great data scientists

[00:06:55] Data science and behavioral economics

[00:08:44] How we can see our brain as a set of algorithms with an input process and output?

[00:12:01] The two systems in the brain

[00:15:40] How to cope with rejection in our job search

[00:18:36] The importance of saying no

[00:21:03] What is emotional intelligence

[00:21:31] The importance of emotional intelligence in our personal and professional lives

[00:23:52] Why emotional intelligence is so important and the challenges of acquiring this skill

[00:26:22] Tips on what we could do to start developing better emotional intelligence

[00:28:16] How to ask for feedback

[00:33:07] We talk about our shared love of Steven Pressfield

[00:35:43] Emotional intelligence in the virtual world.

[00:37:14] How we can identify our "bright spots"

[00:39:00] How to cultivate better self-awareness

[00:41:15] How we create a better awareness of the algorithms in our head

[00:44:02] A three-step process for changing the negative algorithms in our heads

[00:46:34] What it means to be an intrapreneur

[00:49:24] What's the one thing you want people to learn from your story?

[00:50:54] Why Gilbert wants to impact 100,000 people

[00:51:57] The Lightning Round

Special Guest: Gilbert Eijkelenboom.

Freelancing for Data Scientists | Alison Grade

Harpreet Sahota — Mon, 14 Sep 2020 00:00:00 -0400

Living life on your own time and terms is a goal that many of us have. Alison Grade comes by the show to share her insight into how Freelancing can help you achieve this goal. She's the author of the Penguin best-selling book The Freelance Bible and shares a wealth of information with us to help get us started on the freelance journey.

QUOTES

"The key thing about being a freelancer is that... you are in charge of your destiny. You're not waiting for someone to say, please do this...But with that comes huge autonomy because you can work where you like, when you like." [00:06:57]

"I always start by asking what's in it for me to do this for nothing? So I would only do something for nothing if it was delivering value for me. " [00:21:03]

"The times when I've broken my own rule, it's always been a pain in the ass because they don't value me." [00:23:01]

"You've got to be self-motivated. You've got to just get out of bed and want to get on with it. If you're waiting for someone to tell you what to do you need to think about how do you change that. Because either it's really not suited to you or what you're looking at doing is just not motivating you. You know, you've got to you've got to have fire in the belly for go to want to do it." [00:31:32]

FIND ALISON ONLINE

Website: https://alisongrade.com/

LinkedIn: https://www.linkedin.com/in/alisongrade/

Twitter: https://twitter.com/alisongrade

SHOW NOTES

[00:01:31] Introduction for our guest

[00:04:04] What are some of the documentaries and feature films that you've worked on that perhaps our audience might have heard of?

[00:05:09] How COVID will affect the movie and theater industry

[00:06:45] What does being a freelancer mean?

[00:08:51] I-shaped versus T-shaped people

[00:10:56] The Three C’s analysis

[00:15:01] What can we do to make sure that we're pricing our services adequately?

[00:19:30] How to determine your baseline rate for freelancing

[00:20:52] Is there ever a situation where we should work for free?

[00:23:15] Doing free work to build your portfolio

[00:24:32] How can we make sure that we're getting the most out of our client meetings?

[00:26:24] How can we clearly identify the problem that our client is trying to solve

[00:28:33] So where do you see the future of freelancing headed in the next two to five years?

[00:30:03] How do you think technology will impact freelancers in the next two to five years?

[00:31:20] What do you think are some key traits that you think someone who wants to become a full-fledged entrepreneur should be cultivating within themselves?

[00:33:26] Is there a difference between freelancing and entrepreneurship, or can those terms be used a bit interchangeably?

[00:34:38] What would you say is the difference between the freelancer mindset and the entrepreneur mindset, having been on both kind of sides of the field?

[00:35:51] What's the importance of building a personal brand as a freelancer? And how can someone build a personal brand for themselves?

[00:37:50] Using Dunbar’s number to your advantage

[00:40:10] How can we leverage networking events

[00:42:47] Being a woman entrepreneur and freelancer

[00:44:43] What's the one thing you want people to learn from your story?

[00:45:39] The Random Round

Special Guest: Alison Grade.

What is Your Why? | Mike Delgado

Harpreet Sahota — Thu, 10 Sep 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Mike Delgado, a social media strategist, speaker, community builder, and podcaster who serves as the director of social media at Experian over the last decade. When he's not doing awesome work at Experian, he's mentoring and teaching social media strategy courses at the University of California at Irvine.

Mike shares with us his journey into becoming a social media strategist from an English major and filmmaking background. He covers topics such as how to have more engagement on social media, the importance of compassion as a leader, and tips on finding your “why”. Mike’s passion for helping others is very evident in this episode, and his expertise and wisdom can help you find your purpose.

WHAT YOU'LL LEARN

[23:48] Biggest concerns of social media within the next two to five years

[25:41] How can we be better citizens in our virtual community

[29:39] Tips on finding your “why”

[34:54] Qualities of a good leader

[39:46] How we can boost our productivity and stay refreshed

QUOTES

[26:28] “...being part of a community means knowing when to be quiet…”

[30:42] “...my calling at the deepest level is to help encourage and empower others in their work”

[36:17] “I found that in my own failing, in my own mistakes, that I have grown the most.”

[46:21] “the best way to help others is by taking care of yourself first”

SHOW NOTES

[00:01:52] Introduction for our guest

[00:02:48] How did you first get into the social media space and what drew you to the field?

[00:10:41] How to build a community

[00:17:27] Building your brand on LinkedIn

[00:18:35] Data science and social media

[00:23:26] What do you think some of the biggest concerns are going to be for social media and society in the next two to five years?

[00:25:16] How to be better virtual citizens

[00:30:25] What is your why?

[00:34:18] What makes a good leader and how you can cultivate those qualities

[00:38:44] The hardest things to learn can’t be taught

[00:39:33] Do you have any tips on how we can boost our productivity and stay refreshed during these work from home days?

[00:41:32] How to maintain momentum in uncertain times

[00:46:28] How we understand ourselves

[00:48:20] What's the one thing you want people to learn from your story.

[00:51:14] The Lightning Round

Special Guest: Mike Delgado.

Why You Have More Information Than You Think | Douglas W. Hubbard

Harpreet Sahota — Mon, 07 Sep 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Douglas Hubbard, a management consultant, speaker, and author in decision sciences. He's the inventor of the Applied Information Economics method and he's an internationally recognized expert in the field of measuring intangibles. He is also the author of many books, with his most recent one being “How to Measure Anything in Cybersecurity Risk”.

Doug shares with us his journey into quantitative methodology, how to measure and quantify intangible things, and some of the misconceptions of statistics that are still being propagated. Doug’s expertise and knowledge in statistics is vast, and our listeners can gain a whole new perspective in measuring intangibles!

WHAT YOU'LL LEARN

[14:47] How data scientists can benefit from the methodologies of applied information economics
[25:28] The Fermi decomposition

[30:54] Three reasons why people think something can’t be measured
[41:59] The concept of statistical significance
[47:56] The difference between a Bayesian and frequentist

QUOTES

[21:18] “...measure with micrometer, cut with an axe.”

[27:10] “...it's really easy to get lost in all the stuff you don't know”

[43:11] “It's not just literacy you have to improve. It's not just that we have to learn new things about statistics. We have to unlearn misconceptions.”

[43:52] “If you know almost nothing, almost anything will tell you something.’

SHOW NOTES

[00:01:36] Introduction for our guest today

[00:02:59] Talk to us how you first got interested in measuring the intangibles?

[00:05:14] What were some notable projects that you worked on during the early part of your career that helped you shape your philosophy of being able to measure anything?

[00:09:20] What is applied information economics?

[00:12:14] The importance of taking ideas from different domains and combining them in new days.

[00:14:32] How do you see Data scientists benefiting from using the methodologies of applied information economics?

[00:17:04] Where do you see the field of quantitative methodology headed in the next two to five years?

[00:22:30] The difference between a decision models and predictive models

[00:25:04] How to measure anything with Fermi decompositions

[00:30:37] The three reasons people think something can’t be measured

[00:38:16] Common misconceptions about statistics

[00:41:52] Why is it so challenging for people to understand that concept of statistical significance and what it actually represents?

[00:46:42] A purely philosophical interlude on Bayesian statistics

[00:56:12] What’s the one thing you want people to learn from your story and from your work?

[00:58:19] Jump into a quick lightning round. If you could meet any historical figure, who would it be?

[00:58:38] What's the one thing you would say we truly cannot measure?

[01:01:19] If you could have a billboard placed anywhere, what would you put on it?

[01:01:25] What's the number one book, either fiction or nonfiction or even one of each that you would recommend our audience read, and what was your most impactful takeaway from it?

[01:03:33] What is the best advice you have ever received?

[01:04:42] Where can people find your books?

[01:05:46] How can people connect with you? Where else can they find you online?

Special Guest: Douglas W. Hubbard.

Explaining Humans | Camilla Pang

Harpreet Sahota — Thu, 03 Sep 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics.

At the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were.

Her book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms.

Camilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality.

WHAT YOU'LL LEARN

[7:18] Potential negative impacts of A.I

[17:00] Learning to embrace errors

[38:11] Getting over the perfectionist mindset

[39:30] Important soft skills you need to cultivate

[44:17] Advice for women in STEM

QUOTES

[6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.”

[17:20] “an error in one context is a solution in the next”

[47:10] Don’t judge yourself for thinking outside the box. Stay true to yourself and your vision.

[55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.”

FIND CAMILLA ONLINE

LinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/

Instagram: https://www.instagram.com/millie_moonface/

Twitter: https://twitter.com/millzymai

SHOW NOTES

[00:01:32] Introduction for our guest

[00:02:59] A large, open-ended question.

[00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years,

[00:06:08] What do you think would be the biggest positive impact on society?

[00:07:04] What do you think would be scariest applications of machine learning in the next two to five years?

[00:07:51] What do you think separates the great Data scientists from the merely good ones?

[00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience?

[00:11:29] What does it mean to think in boxes and what does it mean to think in trees?

[00:14:59] Why are most people stuck in box thinking?

[00:15:49] How to be a tree thinker

[00:16:50] What can we do to start embracing errors in our own lives?

[00:19:27] What do proteins have to do with personality and interpersonal relationships?

[00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work?

[00:23:09] Never let your fear define your fate

[00:25:16] Gradient descent in layman’s terms

[00:26:47] How to use gradient descent to find our path to prioritize and identify our goals?

[00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves?

[00:31:02] What neural nets can teach us about ourselves

[00:32:17] Is data science an art? Or is it a science?

[00:33:30] How does the creative process manifest itself in Data science?

[00:35:11] How to take better notes

[00:37:26] How to stop being a perfectionist

[00:39:10] Why soft skills are hard work

[00:42:54] We’re both INFJ’s!

[00:44:26] Advice for women in STEM

[00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM?

[00:47:00] What's the one thing you want people to learn from this story?

[00:48:37] The lightning round

Special Guest: Camilla Pang, PhD.

The Many Models Mindset | Scott E. Page

Harpreet Sahota — Mon, 31 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Scott Page, a professor who studies complex systems and collective intelligence teams and political and economic institutions. He's known for his research on and modeling of diversity and complexity in the social sciences with a particular interest in the roles that diversity plays in complex systems. His book, “The Model Thinker”, stresses the application of ensembles of models to make sense of complex phenomena.

Scott shares with us his predictions into the future of machine learning, the importance of using a simple model, and how diversity impacts productivity. This episode is packed with amazing content that all data scientists and machine learning practitioners can apply in their lives. It was an absolute pleasure chatting with Scott!

WHAT YOU'LL LEARN

[12:41] Scariest applications of machine learning we might see

[24:56] What is a model, and why must they be simple?

[33:30] Many model thinking and it’s advantages

[47:07] How diversity impacts productivity

[49:46] How creativity impacts success, and how to be more creative

QUOTES

[6:31] “...you have to separate achievement from purpose.”

[35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses…”

[45:02] “...what you really want...is people who are acquiring different ways of thinking and understanding different tools, because then the whole is going to be so much more than the sum of the parts.”

[46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.”

SHOW NOTES

[00:01:15] Introduction for our guest

[00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity?

[00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field?

[00:05:34] Separate achievement from purpose

[00:06:53] The synergy of ideas

[00:10:24] The biggest positive of machine learning on society in the next two to five years.

[00:12:35] The scariest applications of machine learning in the next two to five years?

[00:14:00] The online echo chamber

[00:15:12] Big data versus thick data

[00:17:05] Is thick data like longitudinal data?

[00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern?

[00:21:34] The “Scott Page Canned Beets” argument

[00:24:49] What is a model and why must they be simple?

[00:26:10] What are the three classes of models?

[00:26:50] What are the seven uses of models, aka the REDCAPE?

[00:29:00] The wisdom hierarchy

[00:31:14] The importance of assumptions while constructing a model

[00:33:20] Many model thinking vs single model thinking

[00:35:53] The difficulties of modelling human behavior

[00:39:02] Identity diversity versus cognitive diversity

[00:42:42] Cognitive diversity and mental models

[00:44:43] Cognitive diversity for knowledge workers

[00:45:14] Diversity and creativity

[00:47:04] In what ways does diversity make systems more productive?

[00:48:28] Is Data science machine learning to be an art or purely a hard science?

[00:49:31] Success and creativity

[00:51:32] What's the one thing you want people to learn from your story?

[00:53:41] The lightning round

Special Guest: Scott E. Page.

Naked Data Science | Charles Wheelan

Harpreet Sahota — Thu, 27 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Charles Wheelan, a professor, journalist, speaker and author, who holds a PHD in public policy from the University Chicago. He's currently a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College, and the author of Naked Economics, a book that is an accessible and entertaining introduction to economics for the layperson.

Charles shares with us his journey into becoming a prolific author, and why he decided to write Naked Economics, Naked Statistics, and so on. He also warns the use of big data and the implications it can have if used improperly. This episode is packed with insights into money, statistics, and some of the important problems the world is currently facing.

WHAT YOU'LL LEARN

[4:25] Charles’s tips on learning a subject effectively

[12:41] What is money, and why does it matter?

[21:40] How statistics can be used to make solve problems

[26:55] Why humans are so bad at appreciating and conceptualizing probabilities

[33:02] Important soft skills that technically oriented people need

QUOTES

[11:56] “Big Data is...a powerful weapon...it really can be put to great effect. Used improperly, you can do some enormous damage.”

[33:07] “...even if you are very technically oriented, you have got to have an awareness of sociology, psychology, great literature and the like.”

[36:23] “...if your data reflects some underlying problem, then any model you build from that data will just embed it more firmly in cement.”

[48:15] …”stop thinking about what you're doing and look around the world and see what's missing”

FIND CHARLES ONLINE

LinkedIn: https://www.linkedin.com/in/charles-wheelan-a6220911/

Website: http://www.nakedeconomics.com/

Twitter: https://twitter.com/CharlesWheelan

SHOW NOTES
[00:01:19] Introduction for our guest

[00:02:45] How did you become so interested in statistics?

[00:04:16] Was there a lot of self study involved in learning statistics?

[00:05:06] How he wrote Naked Statistics

[00:06:51] What is economics?

[00:09:19] Does big data impact how economics works?

[00:11:21] Does big data change how the invisible hand works?

[00:12:35] What is money and why does it matter?

[00:16:43] Money in a world of contactless payments

[00:18:18] The impact of digital currencies on society

[00:20:15] Money and intersubjective reality

[00:21:22] How to use statistics to make business work better

[00:23:12] Which form of bias should we be most wary of?

[00:24:40] How will COVID affect the election

[00:26:49] Why are humans so bad at appreciating conceptualizing probabilities?

[00:29:26] Why is it important that we cultivate an intuition for what probabilities represent?

[00:30:39] Why we shouldn't buy the extended warranty

[00:32:38] What's going to separate them from the rest of the world, the rest the competition.

[00:32:54] What soft skills do you need to be successful?

[00:37:19] Charles Wheelan predicted COVID in his book The Rationing

[00:37:37] Draw parallels between the fiction you wrote and the reality that we're experiencing today

[00:39:03] How he came up with the story for The Rationing

[00:41:07] Which aspect of human nature do you think from your fiction has shown itself to become a reality with our current situation?

[00:43:18] What's the one thing you want people to learn from this story?

[00:44:35] The lightning round

Special Guest: Charles Wheelan, PhD.

The Contemporary Practice of ML SUCKS! | Carl Osipov

Harpreet Sahota — Mon, 24 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Carl Osipov, who has nearly two decades of information technology experience spanning roles such as program manager at Google, an IT executive at IBM, and as an adviser to Fortune 500 companies. Today, he's here to talk about his book, “Serverless Machine Learning In Action”, which is targeted at teams and individuals who are interested in building machine learning system implementations efficiently at scale.

Carl shares with us his take on the future impacts of machine learning, the creative process in feature engineering, and important soft skills that data scientists need to develop. Carl’s expertise and advice will resonate with beginners and senior data scientists alike. It was a great pleasure speaking with him!

WHAT YOU'LL LEARN

[5:01] Hype in machine learning and how it’s changed

[8:58] The potential negative impacts of machine learning

[38:21] Is machine learning an art or science?

[51:47] Important soft skills you need to succeed

[54:23] Tips on communicating with executives

QUOTES

[12:00] “I think what will make data scientists of tomorrow successful is going to be more about the understanding of human culture.”

[58:03] “The most important lesson is to be persistent and continue focusing on that one successful outcome. You only need to be successful once, so don't worry about any of those individual failures.”

[58:50] “Whenever you collaborate with someone and you're willing to learn from them, you're going to come away as a person who really grows as an individual…”

SHOW NOTES

[00:01:33] Introduction for our guest today

[00:03:03] What drew you to this field? What were some of the challenges you faced breaking into the field?

[00:04:46] How much more hyped has machine learning become since you first kind of broke into this?

[00:05:59] Where do you see now the field of machine learning headed in the next two to five years?

[00:07:41] What do you see being the biggest positive impact coming from machine learning in the next two to five years?

[00:08:52] What do you think would be the scariest application of machine learning in the next two to five years?

[00:10:55] What are some things that we should keep on top of our mind as areas of concern so that we can kind of mitigate the risk of these scary applications?

[00:11:45] What do you think will separate the great Data scientists from just the good ones?

[00:13:48] What is serverless machine learning and how is it different from regular old fashioned machine learning?

[00:17:10] So what is the difference between machine learning code and machine learning platform?

[00:19:14] What is it about the contemporary practice of machine learning that tends to just suck our productivity from the practitioner?

[00:21:24] At what point then does it make sense for us to start using serverless machine learning?

[00:23:05] The difference between row-oriented and column-oriented storage.

[00:27:21] A hypothetical scenario where serverless machine learning would an ideal use case.

[00:28:52] What tips you can share with our audience so that we can be more thoughtful with our feature engineering.

[00:31:55] What are some tips that you can share with our audience so that we can be more thoughtful in our hyperparameter tuning?

[00:34:17] What do we do once a model is put into production?

[00:38:07] Is data science an art? Or is it purely a science?

[00:39:51] The creative process in data science

[00:43:19] The democratization of machine learning

[00:45:21] What would you say was the biggest lesson you learned about democratization of A.I. while you're over at Google?

[00:46:16] We discuss the many patents Carl has published

[00:48:53] Which of your publications, your patents do you think are most applicable to our current times?

[00:51:24] What soft-skills do you need to be successful?

[00:53:49] How to communicate with executives

[00:55:54] How to develop your product sense and business acumen

[00:57:10] Why you shouldn’t be discouraged by these insane job descriptions

[00:58:16] What’s the one thing you want to people to learn from your story?

[00:59:03] Where can people find your book?

[00:59:44] What's your data science superpower?

[00:59:59] If AI could answer any question for you, what would you ask?

[01:00:05] What do you believe that other people think is crazy?

[01:00:21] If you could have a billboard anywhere. What would you put on it?

[01:00:31] What is an academic topic outside of Data science that you think every data scientist should spend some time studying and researching on?

[01:00:48] What would be the number one book? Fiction, nonfiction, or maybe one of each that you would recommend our audience read. And what was your most impactful takeaway from it?

[01:01:21] If we can get a magic telephone that allowed you to contact 18 year old Carl, what would you tell him?

[01:01:39] What's the best advice you have ever received?

[01:01:43] What motivates you?

[01:01:46] What song do you currently have on repeat?

[01:01:56] How can people connect with you and what can they find you online?

Special Guest: Carl Osipov.

How to Become a Chief Data Scientist | T. Scott Clendaniel

Harpreet Sahota — Thu, 20 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence. Currently, he's a chief data scientist of the Strategic Artificial Intelligence Lab, where he's aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science.

Scott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing Scott!

WHAT YOU'LL LEARN

[7:57] What is an A.I. winter?

[10:54] Where the field of data science is headed in the next few years?

[13:58] Tips on being an effective leader

[20:39] The underrated skill of storytelling, and how to cultivate it

[32:43] Tips for people that want to break into data science

QUOTES

[16:01] “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.”

[17:50] “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.”

[35:03] “Find your why and make sure it's the right why and use that to propel you…”

SHOW NOTES

[00:01:35] Introduction for our guest today

[00:03:33] What drew you to the field and some of the challenges you faced while you're trying to break into and create your own lane in Data science?

[00:05:00] How much more hyped has I become since he first broke into the field?

[00:07:39] A brief history of the AI winters we've experienced and why we're on the verge of the next winter

[00:10:54] Where do you see the field of data science, machine learning, artificial intelligence headed in the next two to five years?

[00:12:27] In this vision of the future. What do you think is going to separate the great Data scientists from just the good ones?

[00:13:42] What's it mean for you to be a good leader in Data science. And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?

[00:15:27] For someone who's, let's say, the first data scientist in the organization and they're kind of responsible for building up to Data Science practice, what are some of the challenges that you would see them facing and how do you think that they could overcome those challenges?

[00:18:48] What would you say the hero's journey looks like for a Data scientist or anyone in a data related role?

[00:19:31] The importance of story-telling in data science

[00:22:27] Does the way you tell a story differ if you're talking to your manager versus maybe talking to a roomful of executives? Do you have any tips for Data scientists?

[00:25:32] So what are some questions we could ask ourselves when we're starting a project that can really help us clarify exactly what the problem is?

[00:27:35] There is a hidden Data science message in the movie Dr. Strange

[00:28:47] How do you think a Data scientists could develop and cultivate a business acumen or a product sense for themselves?

[00:30:56] The multiplicity of algorithims and the importance of feature engineering

[00:32:25] Can you share some tips or words of encouragement for our listeners who's got like a couple of decades, maybe 10 to 20 years of non Data related experience under their belt and they're now trying to break into Data science? What challenges do you foresee them facing and how can they overcome these challenges?

[00:35:20] What advice or insight can you share with people who are breaking into the field and they look at these job postings and some of them want the abilities of an entire team wrapped up into one person?

[00:39:28] Do you have any suggestions for finance or fintech Data science projects that an aspiring Data scientists could tackle?

[00:42:15] What are some things that we need to be cognizant of and monitor and track once the model is deployed, both from the Data scientists perspective and the business perspective?

[00:44:22] What advice do you have for Data scientists who have who feel like they don't need to learn anymore? What would you have to say today, scientists in that mindset?

[00:46:51] What's the one thing you want people to learn from your story?

[00:48:58] So what are the two five letter words that really grind your gears and why?

[00:49:06] So what is an academic topic or just an area of research or interest outside of Data science that you think every Data scientist should spend more time researching on?

[00:49:27] What is your favorite question to ask during an interview?

[00:51:00] What's the number one book you'd recommend our audience read and your most impactful take away from it?

[00:52:08] If we could somehow get a magic telephone that allowed us to contact 20 year old T. Scott, what would you tell him?

[00:52:42] What is the best advice you have ever received?

[00:53:09] What motivates you?

[00:53:35] What song do you have on Repeat right now?

[00:53:44] How could people connect with you? Where can they find you?

Special Guest: T. Scott Clendaniel.

Overcoming Imposter Syndrome | Paul McLachlan, PhD

Harpreet Sahota — Mon, 17 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Paul McLachlan, a data scientist who has over a decade of experience applying his knowledge and expertise to academia, corporate businesses, and entrepreneurial endeavours.

His contributions and expertise have led to numerous startups and nonprofits inviting him to serve as an advisor. He gives insight into how what sparked his interest into the data science field, his tips for beginners in data science, and how he stays motivated.

Paul shares with us his powerful journey from being a high school dropout to getting his PhD in computational social science and becoming the A.I. research leader for the Consumer and Industry Lab at Ericsson Research. This episode is packed with advice, wisdom, and tips that will change your mindset.

WHAT YOU WILL LEARN

[21:37] How A.I. can help fight COVID-19
[27:15] Extended reality vs. virtual reality
[32:11] Tips for breaking into data science
[35:29] Important soft skills for data scientist
[44:22] Staying motivated in difficult times

QUOTES
[19:05] "Data science is really a collective endeavour… even the most skilled and successful data scientist is going to have to be able to successfully work with technical stakeholders, non-technical stakeholders…"

[34:51] "…Start from a position of humility…that that can go much further for data scientists than always trying to be the smartest technical person in a conversation…"

[45:29] "Having fun and staying connected and staying entertained is actually part of your job responsibilities rather than something that can be set aside."

SHOW NOTES
[00:01:40] Introduction for our guest today

[00:03:38] What sparked your interest in the field of Data Science? Where did you start and how did you get to where you are today?

[00:05:50] How to not be afraid of math and overcome imposter syndrome

[00:07:42] Where do you see the field of Data science machine learning and A.I. headed in the next two to five years?

[00:09:38] What do you think will be the biggest area of concern for the application of A.I. in the next, say, two to five years?

[00:11:22] What do you think will separate the great Data scientists from the good ones?

[00:12:57] Ericcson's involvement with the White House Office of Science and Technology COVID-19 open research dataset challenge using information retrieval and NLP

[00:13:24] What is information retrieval?

[00:14:02] What is Natural Language Processing?

00:14:40] How information retrieval and Natural Language Processing played a role in the innovative solutions that Ericsson data scientists developed for the challenge.

[00:19:31] What the resulting product looked like

[00:20:52] Interesting findings that came from the challenge

[00:24:30] Congratulations on your new role. AI Research Leader for the consumer and industry lab. So can you tell us a little bit about how the consumer and industry lab fits into Ericsson?

[00:26:56] What XR and VR are and share with us what aspects of XR and VR are most interesting to you.

[00:31:45] How to build a culture of data science

[00:35:13] What do you look for in a data scientists beside those those technical skills?

[00:37:47] How to gain industry experience if you don't have any

[00:39:52] How to communicate with executives as a data scientist

[00:42:10] Thought leadership in data science

[00:44:06] Tips to stay motivated when you're feeling down in your learning journey

[00:47:03] What's the one thing you want people to learn from your story?

[00:48:40] The lightning round

Special Guest: Paul McLachlan, PhD.

Physics and the Art of Data Science | Santona Tuli, PhD

Harpreet Sahota — Thu, 13 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Santona Tuli, a physicist and data scientist who has a PhD in physics specializing in nuclear science and quantum chromodynamics.

She currently leads a team of five doctoral and postdoctoral physicists studying a new plasma phase of matter and the elusive nuclear effects in high energy proton and nucleus collisions at the Large Hadron Collider at CERN in Geneva, Switzerland.

Santona shares with us her journey into data science as a physicist, and her perspective on the future of the field. She also discusses the differences between data science and decision science, tips to break into the field, and advice for women in STEM.

It was an absolute delight hearing Santona’s advice, and I believe her unique perspectives can help all data scientists!

WHAT YOU'LL LEARN
[4:46] Where the field of data science is headed

[32:42] Is data science an art or science?

[49:07] Tips for breaking into data science

[57:55] How to get over the perfectionist mindset and feeling like a failure

[1:02:07] Diversity and inclusion of minorities in STEM

QUOTES
[34:51] “...just being able to step outside and think of alternative approaches, stepping outside the predefined paths. To me, that's how the creative part of my brain is really engaged when I'm doing Data science.”

[39:38] “...the audience should be able to look up at this screen and see themselves reflected in it, being able to understand that the physics that's going on...physics is very much within their reach. Science is very much within their reach.”

[52:40] “...separate or distinguish what the end goal is and the steps that you need to take in order to get there”

[55:21] “get over this idea that it has to be perfect before [you] push it out...What's the worst that can happen? Maybe someone criticizes in some way...But it might turn out that this criticism that you're receiving on it is actually going to help you iterate on that project and make it better.”

WHERE TO FIND SANTONA

LinkedIn: https://www.linkedin.com/in/santona-tuli/

SHOW NOTES

[00:01:21] Introduction for our guest today

[00:02:40] The path into data science

[00:03:20] What the heck is quantum chromodynamics?

[00:03:54] Data science and the study of nuclear forces

[00:04:49] The future of data science

[00:08:17] Data science and empathy

[00:09:27] How to be a great data scientist

[00:10:48] What is CERN?

[00:13:13] What is this Y particle?

[00:15:15] The data science work flow and particle physics

[00:20:25] Data reduction and data bottlenecks

[00:23:43] Selection cuts and rules based clustering

[00:29:43] The importance of feature engineering

[00:32:31] How do you view data science? Do you view it as an art or a science?

[00:34:17] How does the creative process come to life in Data science?

[00:36:39] Santona talks about the IMAX movie that she stars in

[00:40:43] The difference between interpretable and explainable machine learning.

[00:44:22] Decision science and data science

[00:48:49] Words of encouragement for people learning new things

[00:51:04] What does it mean to think like a product manager?

[00:54:14] Break free of the perfectionist mindset

[00:57:00] How to deal with feedback and criticism

[00:58:31] What are some soft skills that you think Data scientists are missing?

[01:01:29] Advice and words of encouragement for the women in our audience who are breaking into tech or currently in tech.

[01:05:48] Santona talks about the impact she hopes to have on young women in STEM

[01:09:08] What can men do, in particular in the Data community, to help foster the inclusion of women in STEM, in tech and Data?

[01:11:28] What's the one thing you want people to learn from your story,

[01:11:57] What's your data science superpower.

[01:12:02] What would you say is the most fundamental truth of physics that all human beings should understand?

[01:12:19] What do you think is the most mysterious aspect of our universe?

[01:12:43] What is an academic topic outside of Data science that you think every data scientist should spend some time researching or studying on.

[01:12:53] What's the number one book? Fiction, nonfiction? Or if you want to pick one of each that you would recommend our audience read. And what was your most impactful takeaway from it?

[01:14:02] If we can somehow get a magical telephone that allowed you to contact 18 year old Santona, what would you tell her?

[01:15:09] What song do you have on repeat.

[01:15:28] How do people connect with you? Where can they find you?

Special Guest: Santona Tuli, PhD.

Data Science Double Bam | Joshua Starmer

Harpreet Sahota — Mon, 10 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Josh Starmer a data scientist who has helped empower learners from all over the globe by breaking down complicated statistics and machine learning topics into small bite sized pieces that are easy to understand.

You may know Joshua from his youtube channel StatQuest, where he's beloved by his audience of over 320,000 subscribers and 15 million viewers.

Joshua shares with us his powerful journey from being a cellist and music composer to getting his PhD in computational biology and then creating StatQuest.

This episode is packed with advice, wisdom, and tips for developing a creative process and facing your fears. It was a great honor interviewing Joshua!

WHAT YOU'LL LEARN

[9:05] How music has helped Joshua become more creative

[17:19] Inspiration for StatQuest

[24:00] The most challenging part of creating content

[28:02] The most misunderstood concept from statistics and machine learning

[36:38] How Joshua approaches his creative endeavours

QUOTES

[9:38] "I pick up my guitar, my ukulele, and I start playing, and my head just completely clears."

[19:52] "what I really want people to take home is that anyone can understand these things [statistics]. Ninety nine times out of 100, the only thing between them and understanding is fancy terminology and fancy notation"

[23:31] "It's probably a good thing that I'm a little nervous…because it pushes me just a little harder to make sure that what I'm talking about is correct"

[33:16] "…if you want to educate someone…you have to relate with them and you have to see the material from their perspective."

FIND JOSHUA ONLINE

LinkedIn: https://www.linkedin.com/in/joshua-starmer-95a554130/

YouTube: https://www.youtube.com/user/joshstarmer

Website: https://statquest.org/

SHOW NOTES

[00:01:40] Introduction for our guest

[00:03:13] How Joshua got into statistics

[00:04:12] Where do you see the field of Data science headed in the next two to five years?

[00:05:12] What do you think is gonna separate the great Data scientists from the really good ones?

[00:06:22] Talk to us a bit about what music theory is, what a music theorist does.

[00:08:59] Do you think having a deep understanding of math has helped you be more creative as a musician or vice versa?

[00:11:22] What are some of the commercials and shows that feature your music?

[00:15:32] Joshua describes his process for creating music

[00:17:12] The inspiration of StatQuest

[00:19:27] The StatQuest mission

[00:20:40] Overcoming the resistance when it comes to creating and publishing content

[00:23:53] What's the most challenging part for you when it comes to creating content for the channel?

[00:25:15] What's your personal favorite video from the archives?

[00:26:16] The absolute must watch video from StatQuest

[00:27:53] The most misunderstood statistical concept

[00:30:23] Why you don't need to memorize forumals

[00:32:37] Can you recommend a good book for learning statistics?

[00:34:27] The art and science of data science

[00:36:25] Creativity and data science

[00:38:05] What would you say are the similarities and differences in the creative process for, let's say, writing a research publication, composing music or creating youtube video?

[00:39:38] What's the one thing you want people to learn from your story?

[00:40:47] The lightning round.

Special Guest: Joshua Starmer, PhD.

We're All Soldiers in Cyberwarfare | Chase Cunningham, PhD

Harpreet Sahota — Thu, 06 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Chase Cunningham, a retired Navy chief cryptologist with nearly two decades of experience in cyber, forensic and analytic operations. He holds both PHD and Masters in Computer Science, and has been named one of Security magazine's most influential people in security for 2019.

Chase shares with us the definition of cybersecurity and cyberwarfare, how cyberspace has evolved over the past decade, and the dangers of operating within this space. Chase’s knowledge within cybersecurity will help data scientists identify ways for them to build models that have better real world outcomes and give them insights into a field that impacts our work.

WHAT YOU'LL LEARN

[4:15] What is cyberwarfare and cybersecurity?

[5:33] How does cybersecurity impact data science?

[8:19] The truth about hackers

[16:22] Autonomous vehicles and cybersecurity concerns

[26:20] Ways for data scientists to prevent biases within their models

QUOTES

FIND CHASE ONLINE

LinkedIn: https://www.linkedin.com/in/dr-chase-cunningham-54b26243/

Twitter: https://twitter.com/CynjaChaseC

SHOW NOTES
[00:01:30] Introduction for our guest today

[00:02:37] Talk to us a bit about your professional journey, how you first heard of cyber security, cyber warfare, and kind of what drew you into that field.

[00:04:06] Can you define what cyber warfare and cyber security are?

[00:05:19] Cyber security and data science

[00:06:01] Cybersecurity, data science, and machine learning

[00:06:52] What are some of the biggest concerns in cyber warfare that we'll face both kind of at individual user level and at the organizational level over the next two to five years?

[00:07:56] Hollywood hackers aren't real like hackers

[00:09:05] How hacking has evolved overtime

[00:10:02] How to practice for cyberwarefare

[00:11:03] How can machine learning help detect or prevent these hacking incidents from occurring?

[00:11:29] Cybersecurity projects

[00:13:01] The Cyber Shot Heard around the world.

[00:14:04] What you mean by kinetic outcomes?

[00:14:33] Modern cybersecurity and kinetic outcomes

[00:15:02] Perimeter based security mode

[00:15:42] Alternative to a perimeter based security

[00:16:09] What does cyber security have to do with autonomous vehicles?

[00:16:50] Cyber security attacks on autonomous vehicles

[00:18:14] How cyber security, social media, and A.I can be used for bad

[00:19:15] How to not be tricked by deep fakes

[00:20:38] Weaponizing biometrics

[00:21:26] Cyber warfare campaigns

[00:22:26] Societal impacts of deep fakes, machine learning, A.I. and cloud computing?

[00:24:18] What the history of warfare can teach us about cyberwarfare

[00:25:04] What happens, when Data and A.I. studies go awry?

[00:26:05] How to prevent bias in machine learning systems

[00:27:01] What do you think would be the equivalent of the nuclear bomb for cyber warfare, cyber security?

[00:27:38] You've got six patents that are credited to you. Which one is your favorite one?

[00:29:05] Why should we kill the password?

[00:29:38] What would be the alternative to passwords?

[00:30:07] What's the one thing you want people to learn from your story?

[00:30:39] The lightning round

Special Guest: Chase Cunningham, PhD.

Flash Statistics | Marco Andreoni

Harpreet Sahota — Mon, 03 Aug 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Marco Andreoni, a statistician and data scientist who has a master's degree in mathematics and machine learning, as well as a master's degree in mathematics and cryptography.

He is the lead data scientist at Quantyca, where he covers every part of the data lifecycle from ingestion, storage, analytics, web applications, cloud storage and beyond.

Marco shares with us his passion for teaching other statistics in a more meaningful way. This led him to create Flash statistics, a way to make statistics more accessible to people. Marco brings an interesting perspective into sharing knowledge in a creative way, that all of our listeners should develop to be competitive!

WHAT YOU'LL LEARN

[5:59] Relationship between cryptography and data science

[23:57] What happens when you deploy a model to production

[27:11] The importance of version controlling models

[28:47] The importance of version controlling data

[30:33] Evaluation metrics for post production

[32:00] The importance of creativity

[36:00] Tips on communicating effectively

QUOTES

[21:03] "You don't need to memorize every single equation…But you must know the underlying idea."

[31:23] "Only if you measure something, you can control something"

[35:00] "Focus on the process, the result takes care of itself"

FIND MARCO ONLINE

LinkedIn: https://www.linkedin.com/in/marcoandreoni1/

Website: https://www.flashstatistics.com/

SHOW NOTES

[00:01:24] Introduction for our guest

[00:02:43] Talk to us about your journey, how you first got interested in statistics, machine learning, data science? What drew you to the field?

[00:04:10] Can you give us an overview of what cryptography is?

[00:05:52] How do you see machine learning and cryptography kind of inter playing in the near future?

[00:07:52] GDPR and data science

[00:08:53] Talk to us about the genesis of flash statistics? What was your inspiration for creating it?

[00:09:35] The mission of flash statistics

[00:10:23] Did you feel any type of internal hesitation or a fear with creating the content? And if you did, how did you overcome it?

[00:12:19] The challenge of creating content

[00:13:21] Do you have a personal favorite graphic from the archives?

[00:13:57] Correlation and causation explained via the story of the Stork.

[00:16:20] The one flash statistics painting you need to check out

[00:17:21] What would you say is the most misunderstood concept from statistics and machine learning?

[00:17:51] Would you mind clarifying or demystifying that concept for us?

[00:20:35] Do you think it's important to learn all the formula and equations even though we have advanced software that doesn't work?

[00:21:15] Do you have any tips or any good ways for somebody to learn the underlying idea behind what the software is doing?

[00:22:27] Do you consider Data science and machine learning to be an art or purely hard science? And why?

[00:23:57] What happens when you deploy a model to production

[00:27:11] The importance of version controlling models

[00:28:47] The importance of version controlling data

[00:30:33] Evaluation metrics for post production

[00:31:46] How to be creative

[00:35:57] How to effectively communicate

[00:38:22] The creative process in data science and the artistic process

[00:39:24] What's the one thing you want people to learn from your story?

[00:40:12] The lightning round

Special Guest: Marco Andreoni.

The Infinite Retina | Irena Cronin

Harpreet Sahota — Thu, 30 Jul 2020 09:30:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Irena Cronin, the co-author of "The Infinite Retina".

She currently serves as CEO of Infinite Retina, an organization which provides research and business strategy to help companies succeed in spatial computing. She gives insight into what sparked her interest into spatial computing, how she sees spatial computing influencing our world, and the potential data problems that will result from more spatial computing technology.

Irena shares with us what led her from leaving her career as an equity research analyst on Wall Street to working with AR/VR and other spatial computing tech. This episode is packed with interesting insights in our future, and I believe anyone listening will have something to ponder on!

WHAT YOU WILL LEARN

[4:09] Spatial computing is all the technology associated with bringing a 3D realm to it's users.

[8:15] Concerns of spatial computing

[17:20]The four technical paradigm shifts

[28:56] Spatial computing and autonomous vehicles shaping our future

QUOTES

[16:59] "Technology…it's always been a tool for us. But even more so with spatial computing."

[43:12] "I'd say the most important thing you can ever do is to be extremely persistent, no matter what"

[44:42] "I think it's extremely important to have professors and the students in a class, …take time to listen to everyone who wants to speak… and not let anyone monopolize that precious time."

FIND IRENA ONLINE

Instagram: https://www.instagram.com/infiniteretina/

Twitter: https://twitter.com/IrenaCronin

LinkedIn: https://www.linkedin.com/in/irenacronin/

SHOW NOTES
[00:01:33] Introduction for our guest today

[00:02:52] How did you get to where you are today?

[00:04:02] What is spatial computing, and how is it different from regular computing?

[00:04:53] In what ways is spatial computing already a part of our daily lives?

[00:06:47] Where is spatial computing technology headed in the next two to five years?

[00:08:06] What do you think are some of the biggest concerns that society will face due to spatial computing technology in the next two to five years?

[00:10:51] What is the prime directive?

[00:13:04] How does spatial computing play into meeting that prime directive?

[00:14:35] How will spatial computing change what it means to be human?

[00:17:07] What is the fourth paradigm?

[00:20:08] What's the intersection between spatial computing and artificial intelligence look like?

[00:21:29] Voice first technology, spatial computing, and the prime directive.

[00:24:42] Can AI create a government for itself?

[00:28:36] How will spatial computing and autonomous vehicles help shape cities of the future?

[00:31:02] Can you talk to us a bit about what Data bubbles are and what they have to do with the cities of the future.

[00:33:24] Concerns that local municipalities are having with the use of this spatial computing technology.

[00:39:39] How spatial computing will change the way we attend live events in a COVID world

[00:42:55] Advice for women who are in STEM fields

[00:44:07] How can we foster the inclusion of women in Data science, in AI, and in STEM?

[00:46:08] What's the one thing you want people to learn from your story?

[00:46:38] The lightning round

Special Guest: Irena Cronin.

Start From The Bottom | Carlos Mercado

Harpreet Sahota — Mon, 27 Jul 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Carlos Mercado, a
data scientist, economist and urban studies enthusiast.

Throughout his career, he's had a diverse range of experience, including time as a freight broker, a year long stint teaching English in Korea and working as a data science freelancer. He's currently a senior data scientist at a global consulting firm.

Carlos shares with us his journey into data science, the importance of building your brand, and
tips for those who want to break into the field. Carlos is an example of someone who has
worked hard to learn the fundamentals, and his story shows that it is possible to break into data
science!

WHAT YOU'LL LEARN

[5:16] Where is the field heading?

[10:23] Carlos’s background in economics, and how it relates to data science

[23:52] Lessons regarding how to get the job you want

[30:39] How to use reframing and paradoxes for your mindset

[45:24] Advice on building a resume for data science

[51:40] Building your personal brand

QUOTES

[23:12] “...without the history, you’re not going to have context.”

[25:51] “...your resume is a sales document. So if you don't include it in your sale, they're not going to know to buy.”

[29:33} “...the most important part of data science, besides knowing math, is being able to communicate to business people and making sure that they understand...”

FIND CARLOS ONLINE

LinkedIn: https://www.linkedin.com/in/crmercado/

SHOW NOTES

[00:01:36] Introduction of our guest

[00:02:52] Let's talk about how you first heard of Data science and what drew you to the field.

[00:05:12] Where do you see the field headed in the next two to five years?

[00:06:42] How to be a great data scientist

[00:08:31] Natural language process and voice data

[00:10:15] What is economics and why data scientists should care

[00:11:12] Economics and big data

[00:14:11] Bitcoin and Data Science

[00:17:24] What you need to know about GIS, Urban Economics, and Data Science

[00:22:26] Do you have any other resources or articles that are kind of covering that topic that our readers can go check out if they want to learn more?

[00:23:24] Lessons learned in the data science job search process

[00:26:58] What you've learned about Data science working for a psychiatrist at a nonprofit school.

[00:30:20] Reframe and Paradox

[00:34:36] What it's like working as a consulting data scientist

[00:39:09] How does this differ from working in a regular organization?

[00:40:34] Phoenix project and Unicorn Project

[00:41:04] Freelancing as a data scientist

[00:45:15] How to make a good data science resume

[00:49:57] How to make a good data science project

[00:51:33] How to build your data science brand

[00:53:05] The qualities that Carlos looks for in a data scientist

[00:54:06] What's the one thing you want people to learn from your story?

[00:54:49] The lightning round

Special Guest: Carlos Mercado.

AI Through The Ages | Djamila Amimer, PhD

Harpreet Sahota — Mon, 20 Jul 2020 09:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Djimila Amimer, an experienced business leader and entrepreneur with a broad range of experience across multiple domains.

She is the CEO and founder of Mindsenses Global, a management consultancy specializing in artificial intelligence with a mission to help businesses and organizations apply A.I. and unlock its full potential.

Djimila shares with us her journey into the field of A.I, and some of the concerns she sees the field facing within the next few years, along with the applications of A.I. expanding to other businesses and organizations.

She also highlights important soft skills everyone should develop, and advice for women in tech.

This episode is packed with tips from an expert in A.I.!

WHAT YOU'LL LEARN

[8:32] Biggest concerns for data scientists within the next few years

[16:56] Ethical concerns that data scientists should understand with general A.I

[21:24] How A.I. can help in the fight against COVID-19

[27:10] Djimila’s work with Mindsenses Global

[32:42] Advice on how to become an entrepreneur

QUOTES

[33:17] “...your journey is going to be lonely. So you have to have a lot of resilience to be able to sustain yourself and you grow your business…”

[34:02] “I believe that if you want to do it, if you really, really want to and you believe in it...you will succeed no matter what…”

[35:28] “…you have to be able to adapt to a changing environment.”

FIND DJAMILA ONLINE

LinkedIn: https://www.linkedin.com/in/dr-djamila-amimer-142662137/

Twitter: https://twitter.com/mind_senses

Website: https://mindsenses.co.uk/

SHOW NOTES

[00:01:21] Introduction for our guest today

[00:03:15] Talk to us a bit about how you got involved with the field of artificial intelligence, what drew you to the field?

[00:03:48] Where do you see the field of A.I. headed to the next two to five years? What do you think is going to be the next wave of A.I.?

[00:05:09] A historical tour through the three waves of A.I.

[00:07:07] What do you think separates the great Data scientists from the good ones?

[00:08:26] What do you think are going to be some of the biggest concerns that a Data scientist will face in the next two to five years?

[00:10:13] Narrow AI, General AI, and the future of AI

[00:16:43] The ethical concerns Data scientists will face as AI evolves

[00:21:19] How can AI be used to help us fight this Covid-19 pandemic?

[00:24:57] Do you think that we could use AI and machine learning to identify or at least predict the next pandemic?

[00:25:30] Which one of your research works do you think is most relevant to our current times and can you maybe make the connection for us?

[00:27:02] A deep diver into the work that Dr. Amimer does at Mind Sense Global

[00:32:28] Tips for anyone who is thinking of becoming an entrepreneur

[00:33:44] How to cultivate an entrepreneurial mindset

[00:35:32] Data science entrepreneurship opportunities in the COVID world

[00:38:05] The soft skills you need to standout

[00:41:13] How can a student with nothing but a laptop and an Internet connection to use AI for good?

[00:44:22] Advice for women in STEM

[00:46:11] What can the Data community do to foster the inclusion of women in STEM?

[00:48:27] What's the one thing you want people to learn from your story?

[00:49:12] The lightning round

Special Guest: Djamila Amimer, PhD.

You ARE Good Enough | Lisa Shiller

Harpreet Sahota — Mon, 13 Jul 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Lisa Shiller, a mathematician and data scientist who loves dancing, cooking and adventure. She's most passionate about using her skills to make a positive impact, improve people's well-being, create sustainable abundance and decrease our carbon footprint by spreading awareness of sustainability.

Lisa shares with us her work at FoodMaestro, the importance of sustainability, interesting findings from her COVID-19 related project, and advice for women in tech. Lisa provides great advice for data scientists on how to impact the culture of their organizations and the importance of being authentic. It was a great pleasure interviewing her!

WHAT YOU'LL LEARN

[6:01] What is sustainability?

[19:52] Lisa’s COVID-19 project in Mexico

[28:19] Challenges in cultivating a data science culture in an organization

[32:41] Important soft skills every data scientist needs

[38:51] Advice for women in tech

QUOTES

[8:38] “...it's all about taking the data that we have, interpreting it and allowing just like everyday people to have access to information to make smarter, healthier decisions.”

[31:22] “I think it's important to... work with other people that are also who they are authentically.”

[36:57] “I don't know everything right now, but I will figure it out. And that's totally OK.”

FIND LISA ONLINE

LinkedIn: https://www.linkedin.com/in/lisa-shiller-a7471551/

Instagram: https://www.instagram.com/lisashiller/

Twitter: https://twitter.com/lisa_shiller

Facebook: https://www.facebook.com/lshiller

Website: https://www.lisashiller.com/

SHOW NOTES

[00:01:44] Introduction for our guest

[00:02:58] Lisa’s path into Data science. What sparked her interest? Where did she start? And how did she get to where she is today?

[00:04:08] Talk to us about the work you're doing at FoodMaestro. How are you applying data science to help deliver a better food experience?

[00:05:48] What sustainability means in terms of the work Lisa does

[00:07:15] How will Data science will impact clinical health, wellness, and sustainability even in the next two to five years?

[00:08:48] In what ways do you feel we can leverage data science to help reduce our carbon footprint and promote sustainability?

[00:09:45] In what ways do you think Data science will have a big impact or at least the biggest positive impact on people's food choices in the next two to five years?

[00:12:06] Lisa talks to us about the project she worked on, where she used math and data science to predict COVID-19 in the state of Guanajuato, Mexico.

[00:14:09] Lisa explains what the SEIR model from epidemiology is

[00:15:37] Lisa talks to us about the importance of having good or strong assumptions when undertaking a project?

[00:19:44] Lisa shares what she found to be the most interesting or important finding that she got from this project?

[00:21:54] Lisa defines what herd immunity is

[00:22:54] How do you view data science? Do you view it as an art or as a science?

[00:24:08] How does the creative process manifests itself in mathematics and Data science?

[00:25:28] What do you think are the essentials to lay the foundation on which to build a data science team in your organization?

[00:28:02] Tips for the first data scientist in the organization.

[00:29:45] What is it that you look for in a Data science candidate?

[00:32:14] What are some of these soft skills that candidates are missing that are really in a separate from their competition?

[00:34:30] How to communicate with non-technical audiences

[00:35:32] How to communicate when you don’t know the answer

[00:38:33] Words of encouragement for our women in the audience who are breaking in to or currently in tech.

[00:40:44] Can you talk to us about how you grappled with imposter syndrome and how you overcame that?

[00:43:03] What can the Data community as a whole do to foster inclusion of women in Data science and AI?

[00:44:52] What's the one thing you want people to learn from your story?

[00:45:39] The lightning round

Special Guest: Lisa Shiller.

Pick The Right Voices To Listen To | Brenda Hali

Harpreet Sahota — Mon, 06 Jul 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Brenda Hali, a marketing guru turned data scientist who is passionate about using data to understand causation and to promote company growth. She gives insight into how she broke into the data science field, how marketing and data science are related in some ways, and the struggles she faced when breaking into tech.

Brenda shares with us her transition from marketing into data science, along with the importance of having the representation of women and other minorities in the tech industry. This episode really shows why diversity and inclusion in tech is so important, and how we can all play a role to help others break into the field.

WHAT YOU'LL LEARN

[6:56] What marketers can learn from data scientists

[11:07] Steps to take when beginning a new project

[17:33] How to communicate effectively with your team in the post-COVID world

[20:56] Advice for women and minorities that want to enter into data science

QUOTES

[15:02] “...you need to have communication with your team, and that communication needs to be in one place”

[15:47] “...experiment fast and let things go…”

[23:52] “Be careful with who you listen to, and be careful when those voices are close to you.”

FIND BRENDA ONLINE

LinkedIn: https://www.linkedin.com/in/brenda-hali

Instagram: https://www.instagram.com/datanauti/

Twitter: https://twitter.com/brendahali

Medium: https://medium.com/@brendahalih

SHOW NOTES

[00:01:31] Introduction for our guest today

[00:02:19] Let's talk a little bit about how you first heard of data science and what drew you to the field.

[00:06:16] As someone who is a marketer turned data scientist, what would you say that the data scientist and the marketer can learn from each other?

[00:08:46] How do you see data science impacting marketing and what could the data scientists and the marketer do to best serve each other in this vision of the future that you have?

[00:10:49] What are some of the first things that you do when taking on a new project? And what are some of the steps you take to kind of keep you on track while going through and navigating the ambiguity of a data science project?

[00:12:51] You wrote on a "Starting Guide to Excel at Teamwork." I was wondering if you could talk to us a bit about the importance of teamwork for data scientists. Do you mind sharing the key points from that post with our audience?

[00:17:17] How do you think teamwork will change or be affected in this post-Covid world? What can we do to start being better team members when we're actually not going to be for a while at least some people aren't going to be in the same room, in the same office as their colleagues.

[00:20:38] Do you have any advice or words of encouragement for the women in our audience who are breaking into tech or who are currently in the tech space.

[00:24:11] What can the Data community do to foster the inclusion of women in Data science and A.I?

[00:29:37] What's the one thing you want people to learn from your story?

[00:31:39] How universities, probably will change their business model.

[00:32:27] What is your Data science superpower?

[00:33:03] What's an academic topic outside of Data science that you think Data scientists should spend some time researching on?

[00:33:13] What is the number one book, fiction, non-fiction or both that you would recommend our audience read. And what was your most impactful takeaway from it?

[00:34:09] What's the biggest blunder of bias you've seen or heard of with an algorithm?

[00:34:55] If we can somehow get a magic telephone that allowed you to contact 20 year old Brenda, what would you tell her?

[00:35:43] What's the best advice you have ever received?

[00:36:23] What motivates you?

[00:38:08] What song do you have on repeat?

[00:38:21] How can people connect with you? Where can they find you?

Special Guest: Brenda Hali.

Why We Should Be More Like Winnie The Pooh | Khuyen Tran

Harpreet Sahota — Mon, 29 Jun 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Khuyen Tran, a student of data science that is currently in pursuit of breaking into the field. She gives insight into how she prioritizes her tasks every day and strategies she uses to take notes and read books.

This episode gives our listeners a fresh perspective on how to approach the data science field, and some very interesting soft skills that you can implement to step up your game! Khuyen is definitely someone I believe will bring lots of value into the data science field.

WHAT YOU WILL LEARN

[3:23] Ways to boost your efficiency and learning rate

[9:34] What inspired Khuyen to begin writing her posts on data science

[11:42] How to initiate projects in data science

[26:43] Reading books the right way

QUOTES

[4:43] “…maximize important tasks over the urgent but not important tasks…”

[11:25] “…the best way to learn anything is not from taking notes, but from… using it.”

[24:15] “…learn to love whatever you are doing and you will start to do it really well.”

FIND KHUYEN ONLINE
LinkedIn: https://www.linkedin.com/in/khuyen-tran-1401/
Medium: https://medium.com/@khuyentran1476
Twitter: https://twitter.com/KhuyenTran16
Website: http://mathdatasimplified.com/

SHOW NOTES
[00:01:17] Introduction for our guest

[00:02:28] How did you get interested in Data science and machine learning. What kind of drew you to the field?

[00:03:02] Khuyen talks to us about her struggle to dedicate time for Data science, and shares some of the struggles and strategies that she's used to enable yourself to boost your learning rate and accomplish more.

[00:04:11] Khuyen talks about how she uses the Eisenhower decision matrix to manage her priorities

[00:06:11] How to manage the distactions that could derail you while you're studying

[00:07:17] How to cultivate the right mindset for studying

[00:09:23] Khuyen talks to us about some of the projects she's done and how posting her work on Towards Data Science has helped her

[00:10:55] Khuyen shares her tips for taking notes while studying

[00:11:32] How to come up with ideas for your projects

[00:12:46] How do you find the right Data? How do you organize your thoughts? How do you structure your project? How do you overcome these challenges?

[00:13:51] Tips for networking with experts in the field

[00:14:41] Some tips on how to identfy and use the right resources

[00:16:49] What's your data and analysis discovery process like?

[00:18:18] How to answer questions you don't know the answer to during an interview

[00:21:51] What's the one thing you want people to learn from your story?

[00:22:16] The lightning round

Special Guest: Khuyen Tran.

Take a Leap of Faith | Alistair Croll

Harpreet Sahota — Mon, 22 Jun 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Alistair Croll, a well-established entrepreneur, analyst, and author.

He's the author of Lean Analytics, when we co-wrote with Benjamin Yoskovitz. He's also one of the founders of Coradiant, Year One Labs, and the Strata confersence.

He shares some excellent tips one how to ask the right questions when working with data, essentials of business communication, and the need to be obsessed as an entrepreneur.

WHAT YOU WILL LEARN:

[28:28] How to be an intrepreneur

[13:39] Incorporate philosophy with data

[19:11] Why you need to be obsessed as an entrepreneur

QUOTES

[14:22] …”as an early stage company, your focus is your biggest currency.”
[22:10] …”crises have a way of accelerating the inevitable.”
[46:04] “...you got to first seek to engage and entertain and then you have the ability to inform people.”
[51:38] …”find a way to capture attention that you can turn into profitable demand better than the competition.”

WHERE TO FIND ALISTAIR ONLINE:

Twitter:https://twitter.com/acroll
LinkedIn:https://www.linkedin.com/in/alistaircroll/
Website: http://solveforinteresting.com/

SHOW NOTES
[00:01:37] Introduction for our guest today

[00:03:20] Alistair talks about his early work with Coradiant

[00:05:47] What do you think the next two to five years is going to look like for businesses leveraging data and analytics?

[00:07:55] Why A.I. will need a therapist

[00:08:26] In this new vision of the future then what's really going to separate, like the great data scientists from just the merely good ones?

[00:11:03] The importance of privacy and GDPR for data scientists

[00:13:56] The concept of "one metric that matters" and how that's going to manifest in terms ofmeasuring privacy

[00:15:00] Why Zoom DOES NOT deserve to be the videoconferencing platform in the world

[00:17:30] Do you have any advice or tips for anyone who's been toying with the idea of entrepreneurship?

[00:19:22] Why we need to instill leaps of faith in people who want to be founders

[00:21:06] In terms of data science, entrepreneurship in this COVID/post-COVID area. What do you see as some problems with tackling that in enterprising data? Scientists can can identify and then get into an opportunity?

[00:22:29]So you've been writing a lot about innovation at Tilt to the Windmill. How should be incumbents think about innovation?

[00:23:02]A deep dive into various models of innovation

[00:26:38] An excellent and thorough discussion on intrapreneur

[00:30:37] Some great advice for one man data science teams who are on an intrapreneurial journey

[00:33:50] The stages of growth intrapreneur developing data products within their organization will face and how to overcome challenges in those stages

[00:36:50] We get into music science and its intersection with data science

[00:43:13] What's your go to music?

[00:43:54] The important soft-skills that a data scientist needs for success

[00:47:11] What are some key takeaways from your book - Propose, Prepare, Present - that you think a data scientist should apply when communicating with non-technical audiences?

[00:49:27] Let's talk a bit about being evil. You say start-ups should be more evil, that sounds terrible. What are you thinking? What are you trying to communicate with that?

[00:53:23] What's the one thing you want people to learn from your story?

[00:54:24] What's it mean to solve for interesting?

[00:56:00] Jumping into a quick lightning round: What would be the number one book, other fiction or non-fiction or both that you'd recommend our audience read and your most impactful takeaway from it?

[00:57:19] If we could somehow get a magical telephone that allowed you to contact 18 year old Allistair. What would you tell him?

[00:58:45] What it means to cultivate a personality

[01:00:07] What's something you've done at one of your ventures that's been just evil enough?

[01:02:57] What's the best advice you've ever received?

[01:04:58] What motivates you?

[01:06:10]So what song do you currently have on repeat?

[01:06:34] How could people connect with you? Where can they find you?

Special Guest: Alistair Croll.

How to Use Your Unique Gift and Perspective | Deborah Berebichez, PhD

Harpreet Sahota — Mon, 15 Jun 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Deborah Berebichez, a physicist, data scientist, and TV host. Her passion for learning and teaching has led her to become a voice for women and minorities in STEM.

She gives insight into how she broke into the data science field, how to cultivate the right mindset to succeed, and the importance of diversity and inclusion in tech.

Deborah shares with us how she grew up in a conservative environment, and the obstacles that she had to overcome to become the first Mexican woman to graduate with a physics PhD from Stanford University.

WHAT YOU WILL LEARN

[17:11] What value Deborah believes data science will bring within the next few years

[20:43] Deborah's role model for being curious and inquisitive

[27:42] Actionable tips for cultivating the habit of critical thinking

[40:07] Advice on how to be the hero when you feel like a failure

[51:47] Advice for women that want to break into tech

QUOTES
[19:57] "…I think the most amazing things that are going to happen [due to data science] are giving transparency to industries and to communities of people that otherwise in the past have remained quite invisible"

[24:19] "I am a very strong supporter of making people learn and educat[ing] others in the basics of science so that we can become empowered citizens and know more about the world."

[24:50] "…Critical thinking to me is about questioning authority…[it] allows us to to gain the proficiency in being able to discard lies from the truth."

[28:12] "…Make sure that you recognized the biases that you have about the world and what you want to be truth so that you don't blind yourself to the actual results of a data analysis"

[40:59] "…The people who end up succeeding in life are not the ones for whom things come easily. They are the ones for for whom obstacles are just something to transcend and the ones that get up every time that they experience a failure in their lives and they keep going."

FIND DEBORAH BEREBICHEZ ONLINE

LinkedIn: https://www.linkedin.com/in/berebichez/

YouTube: https://www.youtube.com/channel/UCT58Xn45TFrXGIEqRcT_yTg

Instagram: https://www.instagram.com/debbiebere/

Twitter: https://twitter.com/debbiebere

SHOW NOTES
[00:03:44] The path into data science

[00:07:59] Dr. Berebichez talks about how she got involved with Metis and the work she's doing there.

[00:09:36] What data science will look like in 2-5 years

[00:11:05] The need for different skillsets in data science, from translators to engineers.

[00:12:12] How to be a great data scientist

[00:14:30] What do you think would be the scariest application or the scariest abuse or misuse of data science machine learning in the next two to five years?

[00:16:46] What ways do you think Data science will have the biggest positive impact on society in the next two to five years?

[00:20:34] Dr. Berebichez talks about a historical figure that means a lot to her: Tycho Brahe

[00:24:38] Critical thinking and the data scientist

[00:27:33] Actionable tips to become a better critical thinker

[00:29:33] Why are humans so bad at appreciating or conceptualizing probabilities?

[00:31:09] Why is it important that we cultivate this intuition for what probability represents?

[00:33:53] Is data science an art or science?

[00:36:16] How does the creative process tend to manifest itself in Data science?

[00:38:00] For people out there who are trying to break into data science and maybe they feel like they don't belong or they don't know enough or they aren't smart enough. Do you have any words of encouragement for them?

[00:39:54] So in those moments where we feel like we're failing or failures, we want to give up because it's hard upskilling and learning so much to get into Data science. What can we do to feel like a hero?

[00:41:48] Breaking into data science when you're coming from a non-technical background

[00:44:06] What would you say would be like the biggest myth that people tend to hold in their heads about breaking into Data science? And would you mind debunking that for us?

[00:45:49] The story of Rupesh

[00:49:59] The importance of progress over perfection

[00:51:32] Debbie shares her experience being a woman in tech and provides the women in our audience some advice and encouragement.

[00:53:30] What could the Data community and men in the Data community do to foster inclusion of women in Data science and AI?

[00:55:39] What's the one thing you want people to learn from your story?

[00:56:24] The lightning round

Special Guest: Deborah Berebichez, PhD.

Don't Be Afraid To Build Your Brand | Srivatsan Srinivasan

Harpreet Sahota — Mon, 08 Jun 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Srivatsan Srinivasan, a data scientist who has nearly two decades of applying his intense passion for building data driven products.

He's a strong leader who effectively motivates, mentors, and directs others, and has served as a trusted advisor to senior level executives. He gives insight into how he broke into the data science field, the importance of focusing on business outcomes,, and some important soft skills.

Srivatsan shares with us his tips on how to navigate crazy job descriptions, as well as his methods for communicating with executives. This episode contains actionable advice from someone who has been working with data since the beginning!

WHAT YOU WILL LEARN

[10:26] What it means to be a good leader in data science

[11:45] How to productionize a model

[15:01] Concept Drift

[17:54] How to navigate difficult job descriptions

[20:33] Tips on communicating with executives

QUOTES

[9:09] "I think more and more data scientists today are technology focused. They need to use technology to just solve a problem…they should focus more on business outcomes."

[10:26] "…a good leader in data science…should be ready to embrace failure"

[12:21] "…start with modularizing your code, see where are your common functions that you can use"

FIND SRIVATSAN ONLINE

LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/

YouTube: https://www.youtube.com/c/AIEngineeringLife

SHOW NOTES

[00:01:17] Introduction of our guest today

[00:02:58] Let's talk a little bit about how you first heard of data science and what drew you to the field and maybe touch on some of the challenges you faced while breaking into the field.

[00:05:13] You've been so generous with your knowledge and sharing your knowledge, creating some really well crafted content for LinkedIn and YouTube. And I'm wondering what's the inspiration behind that?

[00:06:35] Where do you see the field headed in the next two to five years?

[00:08:41] In this vision of the future, what's going to separate the great data scientists from the ones that are just merely good?

[00:10:08] What does it mean to be a good leader in data science? And how can an individual contributor embody the characteristics of a good leader without necessarily having the title?

[00:11:30] What are some challenges that a a notebook data scientist can face when it comes time to productionize a model? And do you have any tips for how to overcome those hurdles?

[00:12:43] Some actionable tips that you can use today for moving outside of notebooks

[00:13:32] What are some things that we should be keeping track of once we have deployed our model into production?

[00:14:44] A discussion of concept drift and data drift

[00:17:08] Do you have any advice or insight for people that are breaking into the field and they see these job postings that look like they want the abilities of an entire team rolled up into one person and then they they just become scared of applying. Do you have any tips or advice for them?

[00:19:12] What are some soft skills that candidates are missing that are really going to separate them from their competition?

[00:20:23] And do you have any tips for a data scientist who might find themselves having to present to a non-technical audience or perhaps a room full of executives?

[00:21:16] What's the one thing you want people to learn from your story?

[00:22:03] The lightning round

Special Guest: Srivatsan Srinivasan.

The Monsters in Your Head | Brandon Quach, PhD

Harpreet Sahota — Mon, 01 Jun 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Brandon Quach, a data scientist who has a PhD in bioengineering, and has worked on threat analysis for security and business ecosystems. He's currently a principal data scientist and manager, leading the charge to modernize the customer experience by applying machine learning to customer support.

Brandon shares his perspective on how data scientists should approach problems, the importance of passing on knowledge, how to be a leader in the data workspace, and the appropriate mindset to develop when faced with difficult problems. Speaking with him was an honor, and this episode has something for everyone to take away from.

WHAT YOU'LL LEARN

[11:50] Brandon discusses automation and whether or not we will be able to automate human judgement

[18:01] What qualities do you need to become an intrapreneur in your organization

[22:19] A unique way to approach leadership in your organization

[30:08] Why great thinkers abhor being told what to do

[37:37] How important is agile and scrum methodology in data science

[46:13] The mindset you need to accept the monsters in your life

QUOTES

[22:37] “...trust, to me, comes from your ability to not be scared of the results that come out of your work or anything that you do.”

[27:25] …”If I received good advice and….good guidance, then I feel it's sort of my job, my duty, to pass it on to the next generation”

[30:08] “Great thinkers like to figure things out and come to a point that they believe in the solution.”

[35:33] “I want people to look back long after I've gone and say...that decision that was made early on that nobody had appreciated...turned out to be really critical down the road…”

[53:33] “...successful data scientists can think through any kind of problem surrounding data science, not just the core problem.”

[57:05] “You should learn how to think through code. How can you learn how to think through code?. Well, either you have a built in imagination... and/or you have gone through a lot of iterations of code and you can understand the process...”

FIND BRANDON ONLINE

Twitter: https://twitter.com/databrandon

Linkedin: https://www.linkedin.com/in/bquach/

Website: https://databrandon.com/

SHOW NOTES

[00:01:16] The introduction for our guest today

[00:03:52] Brandon's journey from academia to industry

[00:06:13] What were some of the the struggles and challenges he faced during your journey?

[00:09:48] Things are never as simple as they seem in data science

[00:11:41] The future of data science

[00:12:06] The automation of data science workflows

[00:13:58] The automation of human judgment and human creativity in problem solving

[00:15:45] What separates the great data scientists from the good ones

[00:17:01] Why a lot of data scientists tend to have PhDs

[00:18:01] What is an intrapreneur?

[00:21:56] A leadership philosophy for data science

[00:27:40] Great advice for data scientists new to the career

[00:29:34] Why you should never tell a data scientist what to do

[00:32:25] Autonomy and mastery lead to purpose for data scientists

[00:33:42] The mindset of future judgement

[00:37:25] Agile and scrum in data science

[00:42:34] Grit, Mindset, and Drive for data scientists

[00:43:55] Dealing with data science stakeholders and handling machine learning setbacks

[00:47:25] Imposter syndrome in data science

[00:50:31] Soft skills for data scientists

[00:51:56] Brandon talks about some interesting interview questions he asks to assess a candidates thought process

[00:54:54] How to deepen your intuition and knowledge of data science

[00:58:08] What's the one thing you want people to learn from your story?

[00:58:56] The lightning round

Special Guest: Brandon Quach, PhD.

Skepticism is NOT a Denial Activity | Kyle Polich

Harpreet Sahota — Mon, 25 May 2020 06:30:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Kyle Polich, a computer scientist turned data skeptic. He has a wide array of interests and skills in A.I, machine learning, and statistics.

These skills have made him a sought after consultant in the data science field. He is also the host of the very popular data podcast, Data Skeptic, which discusses topics related to data science all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.

In this episode, Kyle defines what a data skeptic is, and also goes on to give advice on how to communicate effectively with leaders and executives as a data scientist. Kyle brings a very unique perspective related to all things data, along with actionable advice!

WHAT YOU WILL LEARN

[00:11:49] Probabilistic data structures

[00:15:19] How probabilitistic data structures will change the future

[18:55] Is data science more of an art or science?

[23:36] Advice for data scientists trapped in a perfectionist mindset

[30:43] Important soft skills that you need to succeed

[39:40] How to communicate your ideas with executives

QUOTES

[11:43] "…greatness is achieved by a commitment to your craft and pursuing it."

[16:42] "The greatest trick the devil ever pulled was convincing the world he didn't exist. That's what good data science does to me."

[24:42] …"being able to fall down but get up fast is important."

FIND KYLE ONLINE
LinkedIn:https://www.linkedin.com/in/kyle-polich-5047193/

Twitter:https://twitter.com/DataSkeptic

Podcast:https://dataskeptic.com/

SHOW NOTES

[00:03:01] How Kyle got into data science

[00:05:20] What the heck is a data skeptic?

[00:07:47] What do you think the next big thing in data science is going to be the next, say, two to five years.

[00:11:04] How to be a great data scientist

[00:11:49] Kyle gives us a primer on probabilistic data structures

[00:15:19] How do you see probabilistic data structures impacting society in the next two to five years?

[00:17:19] Data skeptic mission

[00:18:39] Kyle answers the question - how do you view data science? Do you think it's more of the art or more science?

[00:21:09] We talk about principles and methodologies as it related to art and science

[00:21:52] Kyle shares his thoughts on the creative process in data science

[00:23:22] Kyle shares his thoughts on being a perfectionist when you're working on a project

[00:25:28] Do you have any tips for people who are coming from a non-technical background and they're coming up to these technical concepts face to face for the first time?

[00:26:42] We talk about the importance of being a lifelong learner and Kyle shares some advice for aspiring data scientists out there who feel like they haven't learned enough yet to even consider breaking into the field.

[00:28:47] What is your advice for data scientists who they feel like they've learned enough, and just don't even need to learn anything else to be successful?

[00:30:27] We talk about the soft-skils that candidates should pick-up, and Kyle shares advice for people who are in their first data science roles.

[00:31:03] Some insight into the purpose of your resume and how you should leverage that for your job search

[00:34:17] We talk about the pursuit of certificates versus the achievement of self-directed learning projects

[00:36:18] Tips on finding the right type of project to add to your portfolio

[00:39:13] For those people a little further along in their career, Kyle shares tips on how to effectively communicate with executives

[00:42:16] We talk about our shared love for Bill Murray

[00:43:06] How you should respond when you come across job postings that look like they want the skills of an entire team rolled up into one person.

[00:46:22] What's the one thing you want people to learn from your story?

[00:47:19] The lightning round.

Special Guest: Kyle Polich.

How to Whisper to Data (and Executives) | Scott Taylor

Harpreet Sahota — Mon, 18 May 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Scott Taylor, also known as the "Data Whisperer."

He has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. He has also helped organizations, such as Microsoft and Nielsen, comb through and organize their data for meaningful use.

Scott shares his "eight 'ates of master data", a set of rules to engage with master data in a meaningful way. He also goes over his tips for communicating with executives, along with important soft skills that are being overlooked by data scientists.

Scott is very articulate, and his passion for data and teaching are definitely evident in this episode!

WHAT YOU WILL LEARN

[12:57] The eight 'ates of master data management

[17:04] Data science communication with executives

[21:45] Legacy data systems

QUOTES
[3:37] "It's not all about building. Sometimes it's about making sure things are structured and organized the right way."

[7:11] "Hardware comes and goes. Software comes and goes. Data always remains."

[16:11] "Data, to have value, has got to be in motion."

[20:36] "If you're a data scientist, you are the business….and it's impossible for you to learn too much about your own business."

[27:08] "…you've got to bring people from "I have no idea what you're talking about" to "how can we live without this?" and that comes from telling a good story."

WHERE TO FIND SCOTT ONLINE

LinkedIn: https://www.linkedin.com/in/scottmztaylor/

Twitter: https://twitter.com/stdatawhisperer

Website: https://www.metametaconsulting.com/

SHOW NOTES
[00:01:20] The introduction for our guest today

[00:02:54] Scott talk to us a bit about his professional journey, how he got involved in the data world. And what drew him to this field?

[00:04:40] Scott talks to us about some of the early gigs he had in the data space.

[00:05:54] Where do you see kind of the field of big data and digital transformation? What's this landscape going to look like in two to five years?

[00:07:41] Scott talks about how the stakes are changing and how data management is unavoidable

[00:08:32] Scott goes more in-depth as to how the stakes are changing and how he's seen it play out across enterprise organizations.

[00:09:56] In this vision of the future where the stakes are changing, what do you think is going to separate the great data professionals from the merely good ones?

[00:11:25] Scott takes us through what he calls the "eight 'Ates" of data: Relate, Aggregrate, Validate, Integrate, Interoperate, Evaluate, Communicate, Circulate

[00:16:29] Scott breaks down how to effectively communicate with executives and what they care about - hint: not necessarily what you care about as a data scientist

[00:18:27] Scott shares some tips for data scientists coming into organizations with legacy organizations and how to navigate that landscape

[00:21:11] What are the similarities or differences in the challenges a legacy system organization faces versus a tech startup? And how can one navigate these waves?

[00:23:40] So what would you say is the biggest data blunder in the last year or two? He describes the system of hotel keys and how it relates to master data, very interesting!

[00:25:12] So what about some data wonders? He describes an everyday application of a wonder: the checkout counter at a grocery store.

[00:26:41] More insight on communicating with stakeholders and executives

[00:27:56] What are some of the soft skills that that candidates are missing that are really going to separate them from the competition?

[00:29:29] There's a lot of people out there who are trying to break into the data space and maybe they feel like they don't belong there or know enough for they aren't smart enough. Do you have any words of encouragement for them?

[00:31:20] Scott does a deep dive into his passion for data and how you can cultivate it in yourself

[00:33:02] What's the one thing you want people to learn from your story?

[00:34:21] The lightning round

Special Guest: Scott Taylor.

Embrace Diversity in Data Science | Brandeis Marshall, Phd

Harpreet Sahota — Mon, 11 May 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Brandeis Marshall, a computer scientist that is excellent at breaking down difficult concepts into easily digestible pieces.

She is passionate about educating people on data, as well as understanding the impact data has on race, gender, and socio-economic disparities.

She is the CEO of DataEdx, a company which focuses on making data science accessible to all professionals.

She shares her perspective on how data impacts communities, how to promote diversity and inclusion in the data science space, and the importance of documenting your process. It was an absolute pleasure to hear her perspective, and I believe her message will help broaden the data science field.

WHAT YOU WILL LEARN

[8:29] How data impacts marginalized communities

[13:29] From Brandeis's perspective, what separates great data scientist from good ones

[14:48] Understanding how data is packaged, and ways to break it down into bite-size portions

[19:30] The impact of live tweeting on social movements

[30:09] Discussing inclusiveness in the data workspace

[39:46] How to be gritty and break away from negative thoughts

QUOTES
[7:57] "I'm trying to do my best to be… that beacon to talk about data in sizeable, understandable nuggets, because it's not just a science thing. It is our everyday life."

[11:45] "…if you stay within your own lane in your own expertise, only talking to people who have your particular background, you're losing the whole story… and with data, there's always a story"

[29:34] "…I want…other people to know that they can talk about their particular ethnicities, content in a research space, in the tech space, and still be successful."

FIND BRANDEIS ONLINE

Twitter: https://twitter.com/csdoctorsister

LinkedIn: https://www.linkedin.com/in/brandeis-marshall/

Website: https://www.brandeismarshall.com/

DataedX: https://www.dataedx.com/

SHOW NOTES
[00:01:50] Introduction for our guest today

[00:04:51] Brandeis talks to us about how she heard of data science. What drew her to the field and some of the struggles and challenges she faced as she were breaking into the field

[00:07:21] Break data in sizeable, understandable nuggets.

[00:08:21] So where do you see the field headed in the next two to five years?

[00:09:12] How do we shift the conversation so that all people are included in the data conversation?

[00:10:39] What could data scientists start doing today so that two to five years in the future they understand the need for diversity of data and they're cognizant of it. What are some things that they could start doing today?

[00:11:03] Data scientists need to get out of their comfort zone

[00:13:12] How to be a great data scientist

[00:14:27] What is data competency

[00:16:38] What's the mission for your new startup, DataEdX?

[00:19:30] Live tweeting, social movements, and data science

[00:22:28] The technical aspects of the Black twitter project

[00:27:31] Project Ideas for Data Scientists

[00:29:04] If there is any impact that you want your work in this space to have on society as a whole?

[00:30:08] The unfortunate effects marginalization in the data workspace

[00:33:30] Diversity in data science

[00:36:34] Dispelling the myth of "it's all about technical skills" and questioning the "move fast" ideology in tech.

[00:39:46] Grit and being determined to seeing your goals through even in the face of challenges.

[00:43:05] What's the one thing you want people to learn from your story.

[00:43:23] The lightning round

Special Guest: Brandeis Marshall, PhD.

All The Things I Wish They Taught Us In Bootcamps | Eric Weber, PhD

Harpreet Sahota — Mon, 04 May 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Eric Weber, a lifelong learner, mathematician, and data scientist.

He has cultivated a passion for sharing his work and experience with others to help them become excited about data science, as well as educating executives on all aspects of data science.

He gives insight into his perspective of learning, how to be a leader in the data science field, and important skills that data scientists need to develop.

Eric shares with us what drew him to the field, and his transition from academia to the business side of data science.

This episode highlights the journey and success of someone who has seen the field develop from the beginning, and has continuously improved over time. I think there is a lot to learn from this conversation!

WHAT YOU WILL LEARN

[4:43] How to transition from academia to industry

[11:40] How to become a great data scientist

[20:59] How to communicate effectively with your team

[24:07] The art in science

[34:52] What soft skills you need

[41:15] What you should do about data science job descriptions

QUOTES

[6:35] "…my journey was all about figuring out two things. One, how to work with data at scale. And two, what does it mean to actually do data science in a business context. And those two things are really, really important…"

[12:17] "You don't need to build an incredibly powerful model for every situation, but you need to know what's going to allow the business to thrive in a productive way."

[19:48] …"getting by is not a long term solution to delivering value for a business, because what you're doing right now to get by is probably going to be automated in a few years…"

[23:50] "You're not always gonna be the expert in the room. And if you are, you're probably in the wrong room."

FIND ERIC ONLINE
LinkedIn: https://www.linkedin.com/in/eric-weber-060397b7/

Twitter: https://twitter.com/edweber1

[00:01:12] Introduction for our guest today

[00:04:17] How Eric broke into data science

[00:06:20] The challenges of transitioning from academia to industry

[00:08:21] Where do you see the field headed in the next two to five years

[00:09:16] Eric talks about the age of the specialist, and how its become the norm recently. He also talks about how this is now a "prove it" time for data science teams

[00:11:32] How to be a great data scientist

[00:12:54] Eric goes into detail about the need to deliver business value versus scientific value

[00:14:01] Data scientists are lifelong learners

[00:16:00] Why data science tends to be a more highly compensated field

[00:16:17] What's your advice to aspiring data scientists who feel like they have not learned enough to start applying for jobs?

[00:18:44] Why you never stop learning as a data scientist

[00:20:47] Don't be afraid to not know something

[00:22:09] The importance of finding teams where asking questions and being open is is valued

[00:23:59] The art of data science

[00:25:20] Curiosity and creativity in data science

[00:30:10] How to be a great leader in data science

[00:33:15] We talk about the book by Liz Wiseman called Multipliers

[00:34:36] The soft skills you need to succeed

[00:38:48] How could data scientists develop their business acumen and product sense?

[00:41:15] Don't be discouraged by these job descriptions

[00:43:28] Going from notebooks to productionizing models

[00:45:51] Why do we even build models in the first place? Mainly for two reasons, find out here.

[00:47:09] What's the one thing you want people to learn from your story?

[00:48:04] The lightning round

Special Guest: Eric Weber.

Overcome Hurdles in the Job Search by Igniting Your Passion | Chhavi Arora

Harpreet Sahota — Mon, 27 Apr 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Chhavi Arora, one of the rising stars in the data science industry! She gives insight into how she broke into the field, the hurdles she had to overcome in the job search, and how she answered commonly asked questions during an interview.

Chhavi shares with us what got her interested in data science in the first place, along with the biggest self-limiting fear that she had to overcome in order to begin her journey into data science. If you are interested in becoming a data scientist but don’t know where to start, then this episode can answer many of your questions!

WHAT YOU'LL LEARN

[9:23] The mindset you need to adopt during the job search process

[11:04] How Chhavi overcame her biggest self-limiting belief

[14:58] How to get a leg-up on your competition when applying for jobs

[18:05] Commonly asked questions during interviews, and how to answer them

[24:55] How to prepare questions for the interviewees, and why it’s crucial

QUOTES

[6:39] “...every project you do as a data scientist needs to be something that you have interest in so that you know what questions you are looking for and you will eventually find answers to your work.”

[12:46] “...every little weakness that you think you have can become a positive thing if you spin the story right.”

[17:16] “...the most important thing is to never, never stop being passionate about data science...because the learning never stops.”

FIND CHHAVI ONLINE

LinkedIn: https://www.linkedin.com/in/chhavi-arora/

SHOW NOTES

[00:01:23] Introduction for our guest today

[00:03:14] Chhavi talks to us about her experience at the NGO and how that got her interested in data science and and machine learning.

[00:05:07] Chhavi tells us more about how she went about building out her projects. And how she comes up with ideas for her projects. She talks about how he creates independent projects based on what she finds interesting.

[00:09:23] How important is having the right mindset during the job search? She talks about the importance of the growth mindset and how it carried her through the ups and downs

[00:11:04] So what would you say was kind of the biggest self limiting belief that you had to overcome when you were in the job search?

[00:12:14] How did you address resumé gaps during the interview process? It's all a matter of perspective - it's only a negative if you let it be negative. Chaavi gives some great tips

[00:14:42] We get into what the job search process was like for Chhavi and she walks us through her process for applying for jobs and then getting interviews or whatnot. Listen to find out why it's not enough to send a resume and just hope that somebody would call you back.

[00:16:06] ow many interviews did you go on before landing your current role?

[00:17:02] Do you have any words of advice or encouragement for those rising stars out there who are now in the same position that you once were?

[00:18:05] Jumping into a mock interview where Chhavi will answer commonly asked interview questions. Starting with: Tell Me About Yourself

[00:19:42] Can you describe a time when you had to deal with competing priorities and with competing deadlines? And how did you handle that?

[00:21:10] What's the most difficult type of person to deal with and how do you deal with them?

[00:23:08] Walk me through your discovery process when you're starting a new project.

[00:24:20] We talk a bit about the STAR format for answering interview questions

[00:24:55] What's the process for coming up with questions to ask during the interview?

[00:27:11]Let's say it comes time to talk about a technical question and the interviewer is asking you about some technical topic. How do you handle that type of question?

[00:28:49] What's the one thing you want people to learn from your story?

[00:29:24] Let's jump into a quick lightning round here. Python or R?

[00:29:29] All right. Where do you see yourself in five years?

[00:30:01] If you can go back in time to have a conversation with 18 year old Chhavi, what would it be? What would you tell her?

[00:30:28] So how about your favorite book, fiction or non-fiction or both of you, if you'd like, and your biggest takeaway from them?

[00:31:23] How people can connect with Chhavi, and also tips on ineffective ways to connect with anyone on LinkedIn.

Special Guest: Chhavi Arora.

The Legend of Data Science | Jeff Jonas

Harpreet Sahota — Mon, 20 Apr 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Jeff Jonas, a data scientist who, for over three decades, has been at the forefront of solving complex big data problems for companies and governments.

His software has helped casinos identify fraud, increased voter registration, protected Singapore’s waterways from piracy, and even predicted possible collisions between 600,000 asteroids over 25 years.

Jeff shares with us his journey from creating word processors in high school to being able to sell one of his companies to IBM, along with being one of three people to complete every Ironman triathlon in the global circuit.

QUOTES
[15:46] "For everybody that's had a close call in life…every day since then has been an extra day. When you think about life like that, it allows you to just unleash a little bit more and make the most of it…"

[31:01] "…You have to let new observations reverse earlier assertions."

[34:31] "If you don't have something that's like 10 times better and high margins, then you can't innovate"

[43:03] "…My work is often about helping humans focus their finite resources"

WHERE TO FIND JEFF ONLINE

LinkedIn: https://www.linkedin.com/in/jeff-jonas/

Twitter: https://twitter.com/JeffJonas

SHOW NOTES
[00:01:20] The introduction for our guest today

[00:03:53] Jeff walks us through professional journey, how you first heard of data science and machine learning. And what drew him to the field.

[00:05:53] Where do you see the field of artificial intelligence data science machine learning headed in the next two to five years? Jeff talks abou how he sees the field flatlining and how COVID-19 is changing the landscape of the field

[00:07:55] Jeff talks to us about what he thinks is going to separate the great data scientists from the good ones. He talks about the importance of being able to combine data in a way that is going to make it easy to understand the real world, he also makes a distinction between AI and Machine Learning

[00:09:59] There's there's a time very early in his career when he went bankrupt and was living out of his car. Jeff talks to us about what he's saying to himself to get him through that. What did he learn from that to go on to create something bigger and better than what you had before?

[00:13:25] When Jeff 23 years old he was completely paralyzed after terrible accident, he talks about his mindset and the self talk he had during that time. He shares was going on in his head and then how he you overcame those challenges

[00:16:45] A bit of data history - Jeff talks about the different programming languages he was using early in his career.

[00:17:01] Tips for anyone contemplating entrepreneurship

[00:20:19] Jeff talks about what he thinks will be the biggest opportunities for entrepreneurship in the post-COVID world.

[00:22:33] The one soft-skil that will make or break your career as a data scientist and how you can cultivate it within yourself.

[00:24:32] So what compelled you to come to complete every Iron Man on the planet? And can you share some of the many, many accomplishments that you've had in that space?

[00:27:01] Jeff describes an ironman event he did in Mallorca, Spain and the logistics of having to travel half way around the world back to Kentucky to compete in another ironman two days later.

[00:28:42] The infamous "Tastes like Mango" Story

[00:31:25] There's a lot of people out there who were trying to to break into data science. And maybe they don't feel like they feel like they don't belong or they don't know enough. They aren't smart enough or whatever. Do you have any words of encouragement for them?

[00:32:41] What's the one thing you want people to learn from your story?

[00:33:09] Jumping in to the lightning round: What's the number one book, fiction or nonfiction that you would recommend for our audience to read and who are most impactful take away from that?

[00:34:50] So if you could somehow get a magical telephone that allowed you to contact 18 year old Jeff, what would you tell him?

[00:35:50] Jeff talks about the work he's done in his career from the Llama Birth registration project he completed, to the modernization of voter registration.

[00:37:12] Jeff has over 100 inventions to his name - he talks about some of his most favorite ones.

[00:38:30] Jeff talks about the project he did with astronomers which involved identifying where in space asteroids are going to be, and which ones may possibly collide with each other or earth.

[00:43:54] Which of your inventions do you think is most relevant now to the current times?

[00:45:43] A quick primer on entity resolution and a very simple example of interweaving common sense with real time AI

[00:47:29] So what's the best advice you ever received?

[00:47:57] Do you have a favorite Iron Man event?

[00:48:31] So what motivates you?

[00:49:10] So how can people connect with you? When can they find you?

[00:49:57] The importance of being accessible

Special Guest: Jeff Jonas.

Secrets to Success in Data Science | Kyle McKiou

Harpreet Sahota — Mon, 13 Apr 2020 00:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Kyle McKiou, a data scientist who took the lessons from his own struggles that he faced attempting to break into data science and packaged them into a course for up and coming data scientists.

He is known for his remarkable talent for building skilled, balanced and productive teams. He gives insight into how he broke into the data science field, his approach for problem solving, and they importance of facing your fears.

Kyle shares with us the importance of finding a mentor that can guide you to accomplish your goals and the important soft skills that you may be overlooking. Kyle brings unprecedented wisdom and advice to this episode, and the points he outlines can help everyone step up their professional goals.

WHAT YOU WILL LEARN
[7:43] What value Kyle believes data science will bring within the next few years
[11:38] How to transition into data science
[16:33] The importance of cultivating a growth mindset
[28:30] Soft skills that data science candidates are missing
[33:01] The single biggest myth about breaking into data science

QUOTES
[16:13] "Be risk averse; Test everything."

[24:50] "You've got to engineer a system that solves the problem for you, because if you have to leverage your own intelligence to solve a problem, well, you're going to be very limited in the amount of work that you can do."

[27:23] "…you start with the problem you want to solve. You break it down to simpler problems. You break those problems down to simpler problems…all the way back until you get to your present state and then you see the exact path forward at any point time…"

[28:31] "…I think in most careers it's not going to be the hard skills that separate you, particularly in data science…[it's] those soft skills, because you realize that if you want to make an impact in the company as a scientist, you're going to need other people to work with you…"

[34:55] "…it doesn't matter how much you know, it matters how much you can learn and adapt."

FIND KYLE ONLINE
Instagram: https://www.instagram.com/kylemckiou/
LinkedIn: https://www.linkedin.com/in/kylemckiou/
Facebook: https://www.facebook.com/datasciencekyle/
Data Science Dream Job: https://dsdj.co/artists70

SHOW NOTES
[01:30] Introduction of our guest today

[03:10] Talk to us a little bit about how you first heard data science and what drew you to the field

[4:50] How software engineering is different from data science

[06:42] What do you love most about the field of data science?

[07:29] Why do you think the field is headed the next two to five years?

[09:46] What do you think is in the separate the great data scientists from the merely good ones?

[11:21] Switching from software engineering to data science

[12:42] How to productionize a machine learning model

[13:19] Why notebooks don't scale

[16:18] The importance of the growth mindset for data scientists

[19:38] Fear as an indicator

[24:29] The engineers mindset for data science

[28:30] Soft skills for data science

[33:01] The biggest myth about breaking into data science

[35:00] Poker and data science

[37:07] What's the one thing you want people to learn from your story?

[39:17] The lightning round

Special Guest: Kyle McKiou.

Remove Your Self-Limiting Beliefs and You Will Soar | Lediona Nishani, PhD

Harpreet Sahota — Wed, 08 Apr 2020 17:00:00 -0400

Remove your self-limiting beliefs, unleash your inner spirit animal, and climb the ranks of the data science competence hierarchy

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

[04:34] Lediona talks about her journey from the research world to data science and touches on some of the challenges she faced along the way and how she overcame them.

[06:56] Lediona talks about show passionate she isabout is NLP, what got her interested in NLP and what she thinks the future holds for this particular area of data science.

[10:45] Lediona talks about some of the common challenges she's seen up and coming data scientists face when it comes time to take research into production.

[14:20] Lediona walks us through her analysis discovery process and the first thing she does when she's taking on a new project. She also talks about some of the steps she takes to keep herself on track while navigating the ambiguity of some of data science projects.

[16:21] Lediona talks about the skills she considers to be an essential skill to be and remain successful as a data scientist.

[18:25] Lediona talks about what she is looking for in an up-and-coming data scientist.

[20:15] We talk about the skills that really set Lediona apart from the pack and the non-technical qualities that's really contributed most to her success.

[21:52] We talk more about the growth mindset and how not to let your beliefs limit your success.

[22:53] Lediona speaks to her experience being a woman in tech, her involvement in Toronto WIDS and shares some words of encouragement for our female listeners.

[24:48] She shares the one thing she want everone to learn from her story.

[26:25] Jump into our lightning round with an opening question: Python or R

[26:51] She speaks about her favorite algorithm

[27:41] What's a book that every data scientist should read?

[29:05] How about a book recommendation for people that are wanting to learn NLP.

[29:19] We talk about her favorite question to as the interviewers during an interview and how it helps he find out if this is the right company for her.

[30:00] We talk about the strangest question she's been asked in an interview and also talk about our spirit animals, and touch on being a generalist or a specialist.

[31:02] Lediona let's you know how you can connect with her online

Special Guest: Lediona Nishani.

How to Learn Effectively and More Tips for Success | Mark Nagelberg

Harpreet Sahota — Wed, 08 Apr 2020 17:00:00 -0400

One of Winnipeg's finest data scientists talks about the skills that have helped him become successful (hint: doesn't involve memorize every hyper-parameter of every algorithm).

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience.

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfData Science, on FB:facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[04:38] We talk about how Mark got into data science and the path that led him to where he is now.

[05:59] Mark talks about his awesome blog and how creating the blog helped him learn and grow as a data scientist.

[07:43] Mark tells us about Toastmasters and how being a part of that has helped him imporve his speaking skills and the benefits that a data scientist can gain from joining a Toastmasters club.

[11:00] He tell us a bit more about space repetition and how it's helped him learn more effectively.

[12:53] We discuss the similairties and differences between spaced repetition and deliberate practice, and Mark shares his tips for creating flashcards to help you quiz yourself.

[14:23] Mark talks to us about the hidden power of compouding and how all it takes to master anything is a solid plan of attack, time, and growth will occurr.

[17:50] He share some resources and blogs that expound on the concept of compounding.

[18:30] We get into what Mark's creative process is like for bringing his project ideas to reality and he shares tips for the up and coming data scientists who don't know where to start with their project.

[19:54] How he goes about identifying where to find data to start working on a project. He also talks about scraping the web for projects, some packages you can use for that, and some warnings so you don't get in trouble.

[21:47] Mark talks about how his idols shouted him out on their blog after he scraped their website and analyzed their posting behaviour.

[23:18] We get into another project that Mark worked on which involved analyzing data about trees from the City of Winnipeg open data portal.

[25:34] He also talks about some interesting and weird data that he's seen out there and then Mark talks about his framework for decision making and how this framework has helped him navigate the ambiguities of data science projects.

[27:30] How to use costs and benefits when making deciisons and find out how to best add value.

[28:32] Advice for people starting a new job as a data scientist and how to identify expectations and set expectations so that all parties involved are on the same page.

[29:47] How he describes his role to people within his organization who don't know what a data scientist is.

[30:48] The one thing Mark wants everyone to learn from his story.

[32:39] Getting into our lightning round - Python or R.

[32:58] A book he recommends every data scientist reads

[33:30] His favorite question to interviewee's ask during a job interview.

[34:05] Mark talks about the weird question he's been asked during an interview.

[34:36] Mark talks about his preference for self-directed learning and projects over certifications.

[35:19] How you can get in touch and connect with Mark online!

Special Guest: Mark Nagelberg.

How to Find Your Ikigai | Daniel Bourke

Harpreet Sahota — Wed, 08 Apr 2020 17:00:00 -0400

There's no way you can't be hype after this conversation.

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[02:24] The introduction for our guest

[04:05] Daniel walks us down the path that led him to data science and machine learning and ties it all back to his Ikigai.

[06:05] How the movie Robot Man inspired him to code.

[06:49] Daniel talks to us about how he used to work as an Apple Genius and preferred a customer facing role, and how that experience led to him developing his first app

[09:41] How Siraj Raval got him excited about machine learning and his experiences learning to code in Python for the first time through a Udacity Nanodegree

[14:00] Where Daniel thinks the field of data science and machine learning is headed in the next two to five years.

[16:15] Daniel talks about what is going to seperate the great data scientists from the merely good ones in the future he is imagining. He also talks about the struggles of shiny object syndrome that all engineers face and how to approach your work like a craftsman.

[19:22] We discuss if data science is an art or a science, how it can be both depending on how you're expressing yourself.

[21:11] How Danies expresses himself artistically using data science.

[22:16] What it's like when he's being scientific with it.

[23:04] How Daniel started on his #100DaysOfCode journey.

[25:00] He talks about his favorite day during the challenge.

*[25:54] * Daniel shares some tips for our listeners that they can implement today to help them along in their upskilling process.

[26:53] How to be a fan of yourself by putting your soul into the work that you're doing.

[29:07] How to find a mentor for yourself, how to be a mentor to yourself, and things a good mentor does and doesn't do.

[34:09] How a good mentor plants a seed in your mind, and doesn't just give you the answer.

[37:30] Why it's OK to suck at the beginning, and how to navigate through that suck phase

[39:18] Why you shouldn't compare progress on a day to day basis, but give youself a long enough timeframe so that a meaningful comparison can be made.,

[42:03] How to navigate the myriad courses out there, find some that will work for you, and design your own "Masters" program.

[46:50] How to build enough of a foundation in the basics, and then apply what you learn on top of that using the weekend project principle.

[47:39] Why your certificates don't really mean much without a project.

[49:16] The one thing Daniel wants everyone to learn from his story.

[50:24] We jump into our lightning round - Python or R

[50:43] Daniel talks about some books that he recommends and his biggest takeaways from them

[53:07] Daniel describes his morning routine

[54:32] Daniel tells us the best advice that he's ever recieved - it's from his dad.

[55:55] Daniel lets us know how we can connect with him and where we can find him online

Special Guest: Daniel Bourke.

How to become a data engineer | Andreas Kretz

Harpreet Sahota — Wed, 08 Apr 2020 16:00:00 -0400

One of LinkedIn's Top Voices in Data Science and Analytics for two years in a row (2018 and 2019) stops by the show to talk about his journey into data engineering, why you dozens of data science certificates are meaningless, and how you can become a data engineer!

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[02:20] The introduction for our guest today

[04:16] Andreas talks to us about how he got into the world of data science

[05:58] The importance of having both engineers and data scientists on your data science team, and why you need both to really be successful.

[06:35] Andreas talks to us about his upcoming book - The Data Engineering Cookbook

[07:57] What his creative process is like for writing the book, and the differences and similarities between that and doing a data science project.

[09:56] Andreas shares he views on the value of certificates

[11:54] Andreas takes us through a workflow for creating a data engineering project and how you can build one for your portfolio.

[14:47] We talk about his new coaching and mentoring platform and what he is aiming to accomplish and achieve with that. We also talk more details for building out a data engineering project.

[17:21] More details on his coaching platform and what he wants students to gain from going through the program

[19:56] Jump into to the lightning round here. Python or R?

[21:02] What cloud platform data engineers should start using : AWS or Azure?

[21:46] Self study or certificates?

[21:53] Favorite big data tool?

[22:09] His favorite question to ask during an interview

[23:14] The weirdest question he's been asked in an interview

[23:41] How you can connect with Andreas and where you can find him online

Special Guest: Andreas Kretz.

Don't Let Them Tell You What You Can't Do | David Tello

Harpreet Sahota — Wed, 08 Apr 2020 14:30:00 -0400

From nearly getting booted from college to going on to earn a PhD in Mathematics.

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[02:32] The introduction for our guest today

[04:07] David talks to us about the struggles he faced when he emigrated to the USA from Peru.

[06:01] David talks about how mathematics turned his life around.

[06:39] Early in his career a professor told David that "it's clear that your first derivative is positive. The question is, is are secondary derivative positive?" He explains to us what this means in mathematical terms, what the professor meant using the metaphor. He walks us through the troubles he faced being on academic probation, how he tried to get a letter of recommendation, and he talks about the impact that meeting had on him.

[10:57] A meeting with a professor who told him that he wasn't good enough to be on this campus. He talks about the pain he felt when he wasn't sure what his path in life was going to be.

[11:54] He talks about his experiences at the University of Michigan and the impact of being around mathematicians that looked like him had on his career.

[12:24] I ask David what it's like to be a minority in a field filled with people who look like me (mostly Indians and Asians) and he how he views himself in this industry, and how being a minority in the field of mathematics is different from being a minority in the field of data science

[16:51] David talks about the struggles and obstacles he faced while trying to get past his PhD qualifying., how he almost didn't return back to school, and how he just kept coming back up after setbacks.

[23:46] He shares advice for how to manage the upskilling process thats required to be a data scientist.

[26:26] David tells us the one thing he wants people to learn from his story

[27:45] We jump into the lightning round: Python or R?

[27:55] Favorite classification algorithm

[28:26] Favorite question to ask the interviewer during an interview?

[29:13] The weirdest question he's been asked during an interview

[31:02] David tells us how awesome DSDJ is

[32:12] David lets us know how we can find him online

Special Guest: David Tello.

Scrum for Data Science Teams | Amit Jain

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

Software engineer turned machine learning engineer talks about the challenges of taking research into production, as well as what it means to be a leader in data science, the importance of staying humble, and how to handle scrum on data science teams.

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[02:22] The introduction for our guest today

[03:57] Amit talks to us about his journey from software engineering into data science and machine learning and touches on some of the challenges that he faced along the way and how he overcame them

[08:18] He discusses some of the challenges he's seen freshers confront when taking something from proof of concept into production

[10:54] How freshers can gain an intuition behind the data and the models they are building so that they can deliver business value

[13:57] We talk about the challenges of monitoring model performance post-production

[15:28] How agile methodology plays out on data science teams and the difference he's seen between its implementation in software enginerring and data teams

[17:57] How to navigate the ambiguity of data science projects

[19:56] What are some steps that someone can take to go from expiring data scientists to, to a data science or machine learning team lead?

[22:38] The essential skills that are need that so individuals can be and remain successful as either a data scientist or a machine learning engineer

[24:25] Some characteristics that he is looking for in a up and coming data

[25:29] Apart from your stunning technical skills, what are some qualities you feel have contributed to your success a machine learning engineer?

[26:31] The one thing that he wants people to learn from his story

[26:48] Let's go ahead and jump into our lightning rounds. Python or R?

[27:05] What's your favorite algorithm

[27:39] What's a book that every data scientist or machine learning engineer should read?

[27:51] His favorite question to ask an interviewee in a job interview

[28:32] The stranges question he's been asked in a job interview

[29:25] Amit lets us know how we can connect with him and where we can find him online

Special Guest: Amit Jain.

You ARE Going to Struggle But It Will Make You Better | Mikiko Bazeley

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

There will be a lot of ups and downs on your journey, but how you end up depends on how you frame them...

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

[02:41] The introduction for our guest today

[04:26] Mikiko walks us down the career path that ultimately led to her becoming a data scientists. She came from a completelt non-technical background and through hardwork, determination, and grit she was able to accomplish her goals

[07:37] She shares with us the various courses of studies she pursued while trying to find something that really resonated with her

[09:43] She then shares with us how hard it was trying to find a job after graduation and eventually ended up working in a hair salon, which

[12:07] She talks about how she used this opportunity to level up her skillset so that she could be more competitive in the marketplace

[13:43] Mikiko talks to us about the first time she got involved with data anlaytics and goes into something she calls the "MacGyver Principle"

[17:31] We talk a bit about thinking like a business leader and why after a certain point, an accumulation of memorized facts doesn't get you to the executive level.

[21:09] Picasso and Data Science

[23:55] What exactly is a growth hacker?

[27:44] Mikiko shares some life lessons she learned from a long time mentor of hers

[29:43] The importance of being so good they can't ignore you

[32:55] Why you need to treasure a days work

[35:58] Mikiko discusses where her desire to help aspiring data scientists comes from

[39:45] She tells us about the concept of "mentors at a distance" and shes with us some of hers

[40:58] Mikiko talks to us about passion, grit, and a growth mindset.

[42:02] How the Pareto principle manifests itself in the day to day job of a data scientist

[43:07] Passion is not innate or something to be found, its something to be cultivated through hardwork and sustained effort.

[45:25] The concept of adaptability and how its helpful navigating the the data science job search process.

[51:24] Mikiko talks about her experience being a woman in tech, being harassed on LinkedIn, and why women need to bring their full selves to the office.

[01:03:42] The one thing Mikiko wants us to learn from her story

[01:04:55] Jumping into the lightning round - Python or R?

[01:05:07] Mikiko's favorite question to ask an interviewee during an interview.

[01:06:22] The weirdest question she's been asked in an interview

[01:07:31] She tells us what her favorite fiction book is

[01:07:57] She shares her favorite non-fiction book

[01:08:54] What she would say to 20 year old Mikiko

Special Guest: Mikiko Bazeley.

How to Crush Your Interviews | Alex Lim

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

A mock interview with a rising star of our industry.

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[02:33] The introduction for the episode and our guest today

[04:23] Alex tells us about the path that led him to data science and machine learning as a career choice

[05:22] Alex tells us about the inspiration behind one of this data science projects

[06:46] He then walks us through the plan of attack for coming up with a strategy for executing on his project.

[07:48] Alex goes into detail about struggles he had to face kind of sourcing data, organizing his thoughts, the project structure, how he overcome these challenges

[08:47] He walk us through his post application protocol for getting interviews

[09:51] Some tips on how to find the right people in an organization to reach out to

[11:10] Alex goes through, in detail, the challenges he faced in the job search, how many interviews he went on, and how he kept his head right during rejections.

[13:13] Alex shares some books and some advice for cultivating the right mindset to navigate you through the job search ups and downs.

[14:23] We start off the mock interview portion with the first question usually asked in an interview: Tell me about yourself.

[15:50] Can you describe a time when you had to deal with competing priorities or competing deadlines?

[16:42] What would you say is the most difficult type of person to deal with and how do you deal with that type of person?

[17:50] Can you walk me through your discovery process when you're starting a new project?

[19:10] Alex tells us the formula he uses to come up with such well crafted responses to commonly asked interview questions

[21:04] Alex talks to us about his process for coming up with questions to ask during an interview

[21:56] The one thing Alex wants us to learn from his story

[22:31] Jumping into the lightning round:Python or R?

[22:44] What's a book every data scientist should read?

[23:00] His favorite question to ask the interviewer in a job interview?

[23:40] His view on certifications and self-directed learning

[24:15] Alex let's us know how we can connect with him and where we can find him online

Special Guest: Alex Lim.

Data Science Needs People Like YOU | Angela Baltes, PhD

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

Why diversity and inclusion is necessary in data scientist and why you shouldn't spend your time trying to "spot a fake data scientist".

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

[2:32] The introduction for our guest today

[03:55] Angela walks us through her background and how he started off as a Criminology major, did some data projects, fell in love with the field, and then decided that data is what she wanted to pursue.

[05:32] She talks to us about the inspiration for doing the #100DaysOfCode challenge and how it helped combat imposter syndrome.

[07:15] Angela walks us through the process for planning out and executing on her undertaking of the #100DaysOfCode challenge.

[08:14] Angela tells us about her favorite day during the challenge.

[08:55] She then tells us about her least favorite day during the challenge

[10:14] Angela tells us how she stayed focused, disciplined, and maintained her execution during her #100DaysOfCode.

[11:08] She talk to us about emotional intelligence and why we, as data scientists, need to start incorporating soft skills into our toolkit

[12:52] Angela talks to us about some of the skills up-and-coming data scientists are missing and the importance of knowing your audience and how to present to them.

[15:43] She also shares some tips on how to network with people in LinkedIn

[16:53] She talks about including personalized messages with your request to connect.

[17:28] She shares some tips with us on how to present findings and how to develop projects that add business value and address the bottom line.

[18:51] Angela talks to us about being a woman in tech, why we need everyone in tech, and how our strength is in diversity.

[19:54] Angela shares with us how she finds fulfillment outside of work.

[20:54] Angela tells us the one thing she wants everyone to learn from her story.

[21:44] Jumping into the lightning round: Python or R?

[21:54] Angela tells us what her favorite algorithm is

[22:19] We also learn the title of her PhD dissertation

[22:27] She also shares her favorite data visualization tool with us

[23:05] We learn what her data science superpower is

[23:31] She shares the title of her favorite machine learning book

[23:46] The largest data set that she's worked with

[24:15] Angels lets us know where we can find her and how we can connect with her

Special Guest: Angela Baltes.

Data Science is Doomed, But WE Can Save It | Vin Vashishta

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

One of LinkedIn's 2019 Top Voice's for Data Science shares why he thinks we're all doomed.

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

[02:29] The introduction for our guest today

[03:58] How Vin first heard of data science and what drew him into the field

[05:22] Why data science is doomed

[07:44] What separates the great data scientists from the merely good ones

[10:22] What role does being creative and curious play in being successful as a data scientist and how can someone who doesn't see themselves as creative be creative?

[13:04] What are some soft skills that candidates are missing that are really going to separate them from their competition?

[15:23] Why women are excelling in data science

[18:18] Vin talks to us about the growth mindset, gives us his definition of it, and how it's important that data scientists embrace this type of mindset.

[19:58] It's not a zero sum game: If you are of a growth mindset, you're not only will want to teach, you want to learn and those two pieces of communication are essential

[20:38] Vin reflects back on his career and recounts the importance of diversity

[22:23] How a up and coming data scientists can tie a particular ability or a particular requirements with a business need specifically in in cases where one doesn't have any work experience to speak of?

[23:47] How up-and-coming data scientists are actually in a better place then those who have been working on the same team for a long time

[24:58] Could you share some tips or words of encouragement for our listeners who've got a couple of decades, let's say 10 to 20 years of a traditional IT experience under the belt who are now trying to break into data science. What challenges do you foresee them facing and how can they overcome some of those challenges they built?

[27:04] I ask Vin what advice or insight he could share with people breaking into the field who are looking at these job postings? Some that seemingly want the abilities of an entire team wrapped up in one person and they end up feeling dejected or even discouraged from applying.

[30:48] What are some challenges that a notebook data scientists face when it comes time to productionalize a model. And do you have any tips for them to overcome those hurdles?

[33:11] If you've already mastered Python, Vin tells you what programming languages you should learn next

[34:31] We touch on the importance of writing good comments in your code

[35:04] What cloud technology should people pick up prior to breaking into the field? Or is this something they should even focus on if they're just looking to land their first role?

[36:04] The one thing Vin wants us to learn from his story

[36:37] We jump into our lightning round: Python or R?

[36:47] What's your data science super power?

[37:29] What's your favorite algorithm for regression and your favorite algorithm for classification?

[37:51] . So what's the number one book you would recommend our audience read and your most impactful takeaway from it?

[38:13] I go off into a tirade about how much that book has changed my life.

[39:05] Certifications vs self-study

[40:00] What motivates you?

[41:44] The societial impact that COVID-19 is going to have

[43:45] Vin let's us know how we can connect with him and shares a message for smaller businesses going through rough times due to our current global pandemic situation

Special Guest: Vin Vashishta.

Microsoft Executive Shares Her Leadership Secrets | Pooja Sund

Harpreet Sahota — Wed, 08 Apr 2020 14:00:00 -0400

On this episode of The Artists of Data Science, we get a chance to hear from Pooja Sund, a technology leader who has over two decades of global technology and financial experience delivering business and organizational impact across a variety of roles.

Her contributions and expertise have led her to be a powerful leader and energizer, and she currently serves as the Director of Technology and Analytics at Microsoft.

She gives insight into her journey into working for Microsoft, her tips to becoming more self-aware, and how she energizes her teams.

Pooja shares with us his powerful journey from switching career paths and landing her dream job at Microsoft. This episode is packed with advice, wisdom, and tips about cultivating a growth mindset.

WHAT YOU WILL LEARN
[10:29] Desirable qualities of a data scientist

[17:38] Why mindset is key

[24:32] How to develop self-awareness

Find Pooja Online
LinkedIn: https://www.linkedin.com/in/pooja3p/

QUOTES
[7:03] "You need to really look at the things that are in front of you and decide what are the things that excite you…"

[12:42] …"Rather than jumping in, take time to understand the problem."

[24:42] "I have seen people, including me, thinking that… I need to keep on learning…there's nothing wrong with it but at times you'll need to really look at the arsenal that you have created for yourself."

SHOW NOTES
[02:47] The introduction for our guest today

[04:57] Pooja talks to us about the path she took from finance into data analytics and shares some tips for those making a similar transition

[08:43] She shares some things that aren't taught in school about leadership, how to think outside the box so that you can align your team goals with the greater organizational goals, and tells us about the "mindshare mindset".

[10:17] Pooja talks to us about the things we can do to cultivate the qualities of a good leader within ourselves, and what she is looking for when she's interviewing candidates.

[11:54] She talks to us about her philisophy that insights aren't useful without understanding the key question to be answered and gives us tips for how we can cut through the BS to get to the heart of the question and find out the key question to be answered.

[15:10] Pooja gives us her take on what it means to be a thought leader in data science and how one would be a thought leader even if they're operating out of an individual contributor role.

[17:14] We talk about how to go from the "impossible" to the "i'm possible" mindset

[20:55] We discuss the importance of the growth mindset, the nearly unlimited potential of human beings, and how the pursuit of skills is never time lost.

[24:32] Pooja gives us her definition of executive presence and how important it is to be self-aware.

[26:16] Pooja talks to us about servant leadership and why its so important. You're only a leader if people want to follow you, and everyone get's a bigger piece of the pie if we all work together to make the pie bigger.

[27:24] Pooja talks to us about her experience being a women in tech, and that you need to be assertive and bring your ideas to the table.

[27:45] The one thing Pooja wants all of us to learn from her story.

[28:55] The lightning round

Special Guest: Pooja Sund.

The Stories of Data Science

Harpreet Sahota — Wed, 08 Apr 2020 12:00:00 -0400

Clips of one piece of advice that our guests want you to take away from their stories.

Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience