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.

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.

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.

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.

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.

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.