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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

REGISTER FOR OPEN OFFICE HOURS: https://bitly.com/adsoh

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.

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.

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.

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.

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.

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

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[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.

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[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.

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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.

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

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[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.

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

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[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.

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

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[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.

Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn!

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[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.

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.

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