The Artists of Data Science

Check YouTube for the video recording of this weeks call! https://www.youtube.com/watch?v=BjNbfp0s_Z4 [00:21] What is a mentor? [00:02:52] How to network [00:08:32] Navigate the data science job market [00:10:36] Should I specialize in NLP or Computer Vision? [00:23:12] How to prepare for job interviews and ask for feedback [00:34:04] The different types of data science jobs [00:42:27] How to find case studies online [00:46:03] Somebody asked a forbidden question [00:48:09] Preparing for a in-person interview Join Data Science Dream Job for 70% off: http://dsdj.co/artists70 Check it out and don't forget to register for future office hours: http://bit.ly/adsoh

Scott H Young a writer, programmer, traveler and avid reader of interesting things. For the last ten years he's been experimenting to find out how to learn and think better. Today he swings by the show and talks to us about how we can master hard skills faster! QUOTES "What you need is not just motivation, but you need some kind of system. You need some way to channel that initial burst of enthusiasm into creating structures for your life so that you will kind of consistently re-engage with it and consistently do what you need to do to learn things better." [00:07:37] "The way that mental models become useful is when you really spent a lot of time thinking about them, not when you just heard their name and kind of written them down and understood a few sentences." [00:13:54] "There is a certain type of person, I guess you could say that like they do need to stop reading, they need to actually start just taking action on things and implementing things." [00:24:23] "The possibilities of learning are a lot more vast than you've maybe previously considered." [01:02:17] CONNECT WITH SCOTT Website: https://www.scotthyoung.com/ Twitter: https://twitter.com/scotthyoung/ Facebook: https://www.facebook.com/AuthorScottYoung/ SHOW NOTES [00:01:27] Introduction for our guest [00:02:42] Talk to us a bit about your journey. How did you get to where you are today? [00:03:30] The struggles on the path to becoming an ultralearner [00:06:22] The pitfalls of motivation [00:08:25] A walk down the narrow path to success [00:10:47] How to make sure you’re applying effort intelligently [00:13:18] The benefits and limits of mental models [00:16:32] The difference between knowing the name of a thing and knowing the thing [00:18:54] Scott’s favorite mental model [00:21:05] Scott talks about his doodles [00:23:33] Is reading making you stupid? [00:26:23] The danger of learning theories and not applying them [00:27:37] You need to do more than just homework [00:29:27] What to do when you’re stunned into inaction [00:31:39] You can’t see your brain getting buff [00:33:36] Luck to destiny [00:39:14] What exactly is ultralearning? [00:40:27] How can we use ultralearning to accelerate, transition, or rescue our careers? [00:41:46] Why is it that we procrastinate? [00:42:55] Mental habits to combat procrastination [00:45:40] You’re more ready than you think you are [00:49:32] How can we mitigate the distraction of our mind? [00:51:18] Do you have any tips for our listeners for what they could start doing today to improve the quality of their focus? [00:53:27] The principle of intuition [00:59:47] Building expert intuition [01:02:04] What's the one thing you want people to learn from your story? [01:03:25] The random roundSpecial Guest: Scott H. Young.

Living life on your own time and terms is a goal that many of us have. Alison Grade comes by the show to share her insight into how Freelancing can help you achieve this goal. She's the author of the Penguin best-selling book The Freelance Bible and shares a wealth of information with us to help get us started on the freelance journey. QUOTES "The key thing about being a freelancer is that... you are in charge of your destiny. You're not waiting for someone to say, please do this...But with that comes huge autonomy because you can work where you like, when you like." [00:06:57] "I always start by asking what's in it for me to do this for nothing? So I would only do something for nothing if it was delivering value for me. " [00:21:03] "The times when I've broken my own rule, it's always been a pain in the ass because they don't value me." [00:23:01] "You've got to be self-motivated. You've got to just get out of bed and want to get on with it. If you're waiting for someone to tell you what to do you need to think about how do you change that. Because either it's really not suited to you or what you're looking at doing is just not motivating you. You know, you've got to you've got to have fire in the belly for go to want to do it." [00:31:32] FIND ALISON ONLINE Website: https://alisongrade.com/ LinkedIn: https://www.linkedin.com/in/alisongrade/ Twitter: https://twitter.com/alisongrade SHOW NOTES [00:01:31] Introduction for our guest [00:04:04] What are some of the documentaries and feature films that you've worked on that perhaps our audience might have heard of? [00:05:09] How COVID will affect the movie and theater industry [00:06:45] What does being a freelancer mean? [00:08:51] I-shaped versus T-shaped people [00:10:56] The Three C’s analysis [00:15:01] What can we do to make sure that we're pricing our services adequately? [00:19:30] How to determine your baseline rate for freelancing [00:20:52] Is there ever a situation where we should work for free? [00:23:15] Doing free work to build your portfolio [00:24:32] How can we make sure that we're getting the most out of our client meetings? [00:26:24] How can we clearly identify the problem that our client is trying to solve [00:28:33] So where do you see the future of freelancing headed in the next two to five years? [00:30:03] How do you think technology will impact freelancers in the next two to five years? [00:31:20] What do you think are some key traits that you think someone who wants to become a full-fledged entrepreneur should be cultivating within themselves? [00:33:26] Is there a difference between freelancing and entrepreneurship, or can those terms be used a bit interchangeably? [00:34:38] What would you say is the difference between the freelancer mindset and the entrepreneur mindset, having been on both kind of sides of the field? [00:35:51] What's the importance of building a personal brand as a freelancer? And how can someone build a personal brand for themselves? [00:37:50] Using Dunbar’s number to your advantage [00:40:10] How can we leverage networking events [00:42:47] Being a woman entrepreneur and freelancer [00:44:43] What's the one thing you want people to learn from your story? [00:45:39] The Random RoundSpecial Guest: Alison Grade.

On this episode of The Artists of Data Science, we get a chance to hear from Mike Delgado, a social media strategist, speaker, community builder, and podcaster who serves as the director of social media at Experian over the last decade. When he's not doing awesome work at Experian, he's mentoring and teaching social media strategy courses at the University of California at Irvine. Mike shares with us his journey into becoming a social media strategist from an English major and filmmaking background. He covers topics such as how to have more engagement on social media, the importance of compassion as a leader, and tips on finding your “why”. Mike’s passion for helping others is very evident in this episode, and his expertise and wisdom can help you find your purpose. WHAT YOU'LL LEARN [23:48] Biggest concerns of social media within the next two to five years [25:41] How can we be better citizens in our virtual community [29:39] Tips on finding your “why” [34:54] Qualities of a good leader [39:46] How we can boost our productivity and stay refreshed QUOTES [26:28] “...being part of a community means knowing when to be quiet…” [30:42] “...my calling at the deepest level is to help encourage and empower others in their work” [36:17] “I found that in my own failing, in my own mistakes, that I have grown the most.” [46:21] “the best way to help others is by taking care of yourself first” SHOW NOTES [00:01:52] Introduction for our guest [00:02:48] How did you first get into the social media space and what drew you to the field? [00:10:41] How to build a community [00:17:27] Building your brand on LinkedIn [00:18:35] Data science and social media [00:23:26] What do you think some of the biggest concerns are going to be for social media and society in the next two to five years? [00:25:16] How to be better virtual citizens [00:30:25] What is your why? [00:34:18] What makes a good leader and how you can cultivate those qualities [00:38:44] The hardest things to learn can’t be taught [00:39:33] Do you have any tips on how we can boost our productivity and stay refreshed during these work from home days? [00:41:32] How to maintain momentum in uncertain times [00:46:28] How we understand ourselves [00:48:20] What's the one thing you want people to learn from your story. [00:51:14] The Lightning RoundSpecial Guest: Mike Delgado.

Explore the world of measuring intangibles with Douglas Hubbard, an expert in decision sciences. Learn about applied information economics, the Fermi decomposition, statistical significance, and the differences between Bayesian and frequentist approaches. Challenge misconceptions about measurement, and discover how data science can benefit from these methodologies.

On this episode of The Artists of Data Science, we get a chance to hear from Camilla Pang, a scientist specializing in translational bioinformatics. At the age of eight, she was diagnosed with autism spectrum disorder and struggled to understand the world around her and the way people were. Her book, “Explaining Humans:What science can teach us about life, love, and relationships” is an original and incisive exploration of human nature and the strangeness of our social norms. Camilla shares with us her journey into science, and her mission to understand human behavior at a young age. She also discusses the potential impacts of machine learning and A.I within the next few years, and the importance of understanding the nuances in data scientists that create individuality. WHAT YOU'LL LEARN [7:18] Potential negative impacts of A.I [17:00] Learning to embrace errors [38:11] Getting over the perfectionist mindset [39:30] Important soft skills you need to cultivate [44:17] Advice for women in STEM QUOTES [6:59] “...before we get on to making the most of A.I we first need to make the most out of human minds.” [17:20] “an error in one context is a solution in the next” [47:10] Don’t judge yourself for thinking outside the box. Stay true to yourself and your vision. [55:23] “...just because you don't fit in a system, doesn't mean you weren't born to make a new one.” FIND CAMILLA ONLINE LinkedIn: https://www.linkedin.com/in/camilla-pang-8b177b69/ Instagram: https://www.instagram.com/millie_moonface/ Twitter: https://twitter.com/millzymai SHOW NOTES [00:01:32] Introduction for our guest [00:02:59] A large, open-ended question. [00:04:32] What you think the next big thing in machine learning is going to be in the next two to five years, [00:06:08] What do you think would be the biggest positive impact on society? [00:07:04] What do you think would be scariest applications of machine learning in the next two to five years? [00:07:51] What do you think separates the great Data scientists from the merely good ones? [00:09:47] Talk to us about the terms neurotypical and neurodiverse. Would you mind defining these terms for our audience? [00:11:29] What does it mean to think in boxes and what does it mean to think in trees? [00:14:59] Why are most people stuck in box thinking? [00:15:49] How to be a tree thinker [00:16:50] What can we do to start embracing errors in our own lives? [00:19:27] What do proteins have to do with personality and interpersonal relationships? [00:20:50] How could we use this understanding of proteins to be better colleagues and better teammates at work? [00:23:09] Never let your fear define your fate [00:25:16] Gradient descent in layman’s terms [00:26:47] How to use gradient descent to find our path to prioritize and identify our goals? [00:28:37] How can we use Bayes Theorem for empathy and managing the relationships that we have with ourselves? [00:31:02] What neural nets can teach us about ourselves [00:32:17] Is data science an art? Or is it a science? [00:33:30] How does the creative process manifest itself in Data science? [00:35:11] How to take better notes [00:37:26] How to stop being a perfectionist [00:39:10] Why soft skills are hard work [00:42:54] We’re both INFJ’s! [00:44:26] Advice for women in STEM [00:46:21] What can the Data community do to foster the inclusion of women in Data science and AI and STEM? [00:47:00] What's the one thing you want people to learn from this story? [00:48:37] The lightning roundSpecial Guest: Camilla Pang, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Scott Page, a professor who studies complex systems and collective intelligence teams and political and economic institutions. He's known for his research on and modeling of diversity and complexity in the social sciences with a particular interest in the roles that diversity plays in complex systems. His book, “The Model Thinker”, stresses the application of ensembles of models to make sense of complex phenomena. Scott shares with us his predictions into the future of machine learning, the importance of using a simple model, and how diversity impacts productivity. This episode is packed with amazing content that all data scientists and machine learning practitioners can apply in their lives. It was an absolute pleasure chatting with Scott! WHAT YOU'LL LEARN [12:41] Scariest applications of machine learning we might see [24:56] What is a model, and why must they be simple? [33:30] Many model thinking and it’s advantages [47:07] How diversity impacts productivity [49:46] How creativity impacts success, and how to be more creative QUOTES [6:31] “...you have to separate achievement from purpose.” [35:45] “...if you really want to understand a complex phenomena, you've got to look at it with lots of lenses…” [45:02] “...what you really want...is people who are acquiring different ways of thinking and understanding different tools, because then the whole is going to be so much more than the sum of the parts.” [46:36] “Creativity is the union of sets. Getting at the truth is the intersection of sets.” SHOW NOTES [00:01:15] Introduction for our guest [00:02:45] What drew you to the field of modeling in general and specifically game theory and complexity? [00:03:49] So what were some of the challenges you faced while you're paving your own lane in the field? [00:05:34] Separate achievement from purpose [00:06:53] The synergy of ideas [00:10:24] The biggest positive of machine learning on society in the next two to five years. [00:12:35] The scariest applications of machine learning in the next two to five years? [00:14:00] The online echo chamber [00:15:12] Big data versus thick data [00:17:05] Is thick data like longitudinal data? [00:19:23] As practitioners of data science and machine learning, what do you think will be some of our biggest areas of concern? [00:21:34] The “Scott Page Canned Beets” argument [00:24:49] What is a model and why must they be simple? [00:26:10] What are the three classes of models? [00:26:50] What are the seven uses of models, aka the REDCAPE? [00:29:00] The wisdom hierarchy [00:31:14] The importance of assumptions while constructing a model [00:33:20] Many model thinking vs single model thinking [00:35:53] The difficulties of modelling human behavior [00:39:02] Identity diversity versus cognitive diversity [00:42:42] Cognitive diversity and mental models [00:44:43] Cognitive diversity for knowledge workers [00:45:14] Diversity and creativity [00:47:04] In what ways does diversity make systems more productive? [00:48:28] Is Data science machine learning to be an art or purely a hard science? [00:49:31] Success and creativity [00:51:32] What's the one thing you want people to learn from your story? [00:53:41] The lightning roundSpecial Guest: Scott E. Page.

On this episode of The Artists of Data Science, we get a chance to hear from Charles Wheelan, a professor, journalist, speaker and author, who holds a PHD in public policy from the University Chicago. He's currently a senior lecturer and policy fellow at the Rockefeller Center at Dartmouth College, and the author of Naked Economics, a book that is an accessible and entertaining introduction to economics for the layperson. Charles shares with us his journey into becoming a prolific author, and why he decided to write Naked Economics, Naked Statistics, and so on. He also warns the use of big data and the implications it can have if used improperly. This episode is packed with insights into money, statistics, and some of the important problems the world is currently facing. WHAT YOU'LL LEARN [4:25] Charles’s tips on learning a subject effectively [12:41] What is money, and why does it matter? [21:40] How statistics can be used to make solve problems [26:55] Why humans are so bad at appreciating and conceptualizing probabilities [33:02] Important soft skills that technically oriented people need QUOTES [11:56] “Big Data is...a powerful weapon...it really can be put to great effect. Used improperly, you can do some enormous damage.” [33:07] “...even if you are very technically oriented, you have got to have an awareness of sociology, psychology, great literature and the like.” [36:23] “...if your data reflects some underlying problem, then any model you build from that data will just embed it more firmly in cement.” [48:15] …”stop thinking about what you're doing and look around the world and see what's missing” FIND CHARLES ONLINE LinkedIn: https://www.linkedin.com/in/charles-wheelan-a6220911/ Website: http://www.nakedeconomics.com/ Twitter: https://twitter.com/CharlesWheelan SHOW NOTES [00:01:19] Introduction for our guest [00:02:45] How did you become so interested in statistics? [00:04:16] Was there a lot of self study involved in learning statistics? [00:05:06] How he wrote Naked Statistics [00:06:51] What is economics? [00:09:19] Does big data impact how economics works? [00:11:21] Does big data change how the invisible hand works? [00:12:35] What is money and why does it matter? [00:16:43] Money in a world of contactless payments [00:18:18] The impact of digital currencies on society [00:20:15] Money and intersubjective reality [00:21:22] How to use statistics to make business work better [00:23:12] Which form of bias should we be most wary of? [00:24:40] How will COVID affect the election [00:26:49] Why are humans so bad at appreciating conceptualizing probabilities? [00:29:26] Why is it important that we cultivate an intuition for what probabilities represent? [00:30:39] Why we shouldn't buy the extended warranty [00:32:38] What's going to separate them from the rest of the world, the rest the competition. [00:32:54] What soft skills do you need to be successful? [00:37:19] Charles Wheelan predicted COVID in his book The Rationing [00:37:37] Draw parallels between the fiction you wrote and the reality that we're experiencing today [00:39:03] How he came up with the story for The Rationing [00:41:07] Which aspect of human nature do you think from your fiction has shown itself to become a reality with our current situation? [00:43:18] What's the one thing you want people to learn from this story? [00:44:35] The lightning roundSpecial Guest: Charles Wheelan, PhD.

On this episode of The Artists of Data Science, we get a chance to hear from Carl Osipov, who has nearly two decades of information technology experience spanning roles such as program manager at Google, an IT executive at IBM, and as an adviser to Fortune 500 companies. Today, he's here to talk about his book, “Serverless Machine Learning In Action”, which is targeted at teams and individuals who are interested in building machine learning system implementations efficiently at scale. Carl shares with us his take on the future impacts of machine learning, the creative process in feature engineering, and important soft skills that data scientists need to develop. Carl’s expertise and advice will resonate with beginners and senior data scientists alike. It was a great pleasure speaking with him! WHAT YOU'LL LEARN [5:01] Hype in machine learning and how it’s changed [8:58] The potential negative impacts of machine learning [38:21] Is machine learning an art or science? [51:47] Important soft skills you need to succeed [54:23] Tips on communicating with executives QUOTES [12:00] “I think what will make data scientists of tomorrow successful is going to be more about the understanding of human culture.” [58:03] “The most important lesson is to be persistent and continue focusing on that one successful outcome. You only need to be successful once, so don't worry about any of those individual failures.” [58:50] “Whenever you collaborate with someone and you're willing to learn from them, you're going to come away as a person who really grows as an individual…” SHOW NOTES [00:01:33] Introduction for our guest today [00:03:03] What drew you to this field? What were some of the challenges you faced breaking into the field? [00:04:46] How much more hyped has machine learning become since you first kind of broke into this? [00:05:59] Where do you see now the field of machine learning headed in the next two to five years? [00:07:41] What do you see being the biggest positive impact coming from machine learning in the next two to five years? [00:08:52] What do you think would be the scariest application of machine learning in the next two to five years? [00:10:55] What are some things that we should keep on top of our mind as areas of concern so that we can kind of mitigate the risk of these scary applications? [00:11:45] What do you think will separate the great Data scientists from just the good ones? [00:13:48] What is serverless machine learning and how is it different from regular old fashioned machine learning? [00:17:10] So what is the difference between machine learning code and machine learning platform? [00:19:14] What is it about the contemporary practice of machine learning that tends to just suck our productivity from the practitioner? [00:21:24] At what point then does it make sense for us to start using serverless machine learning? [00:23:05] The difference between row-oriented and column-oriented storage. [00:27:21] A hypothetical scenario where serverless machine learning would an ideal use case. [00:28:52] What tips you can share with our audience so that we can be more thoughtful with our feature engineering. [00:31:55] What are some tips that you can share with our audience so that we can be more thoughtful in our hyperparameter tuning? [00:34:17] What do we do once a model is put into production? [00:38:07] Is data science an art? Or is it purely a science? [00:39:51] The creative process in data science [00:43:19] The democratization of machine learning [00:45:21] What would you say was the biggest lesson you learned about democratization of A.I. while you're over at Google? [00:46:16] We discuss the many patents Carl has published [00:48:53] Which of your publications, your patents do you think are most applicable to our current times? [00:51:24] What soft-skills do you need to be successful? [00:53:49] How to communicate with executives [00:55:54] How to develop your product sense and business acumen [00:57:10] Why you shouldn’t be discouraged by these insane job descriptions [00:58:16] What’s the one thing you want to people to learn from your story? [00:59:03] Where can people find your book? [00:59:44] What's your data science superpower? [00:59:59] If AI could answer any question for you, what would you ask? [01:00:05] What do you believe that other people think is crazy? [01:00:21] If you could have a billboard anywhere. What would you put on it? [01:00:31] What is an academic topic outside of Data science that you think every data scientist should spend some time studying and researching on? [01:00:48] What would be the number one book? Fiction, nonfiction, or maybe one of each that you would recommend our audience read. And what was your most impactful takeaway from it? [01:01:21] If we can get a magic telephone that allowed you to contact 18 year old Carl, what would you tell him? [01:01:39] What's the best advice you have ever received? [01:01:43] What motivates you? [01:01:46] What song do you currently have on repeat? [01:01:56] How can people connect with you and what can they find you online?Special Guest: Carl Osipov.

On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence. Currently, he's a chief data scientist of the Strategic Artificial Intelligence Lab, where he's aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science. Scott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing Scott! WHAT YOU'LL LEARN [7:57] What is an A.I. winter? [10:54] Where the field of data science is headed in the next few years? [13:58] Tips on being an effective leader [20:39] The underrated skill of storytelling, and how to cultivate it [32:43] Tips for people that want to break into data science QUOTES [16:01] “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.” [17:50] “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.” [35:03] “Find your why and make sure it's the right why and use that to propel you…” SHOW NOTES [00:01:35] Introduction for our guest today [00:03:33] What drew you to the field and some of the challenges you faced while you're trying to break into and create your own lane in Data science? [00:05:00] How much more hyped has I become since he first broke into the field? [00:07:39] A brief history of the AI winters we've experienced and why we're on the verge of the next winter [00:10:54] Where do you see the field of data science, machine learning, artificial intelligence headed in the next two to five years? [00:12:27] In this vision of the future. What do you think is going to separate the great Data scientists from just the good ones? [00:13:42] What's it mean for you to be a good leader in Data science. And how can an individual contributor embody the characteristics of a good leader without necessarily having the title? [00:15:27] For someone who's, let's say, the first data scientist in the organization and they're kind of responsible for building up to Data Science practice, what are some of the challenges that you would see them facing and how do you think that they could overcome those challenges? [00:18:48] What would you say the hero's journey looks like for a Data scientist or anyone in a data related role? [00:19:31] The importance of story-telling in data science [00:22:27] Does the way you tell a story differ if you're talking to your manager versus maybe talking to a roomful of executives? Do you have any tips for Data scientists? [00:25:32] So what are some questions we could ask ourselves when we're starting a project that can really help us clarify exactly what the problem is? [00:27:35] There is a hidden Data science message in the movie Dr. Strange [00:28:47] How do you think a Data scientists could develop and cultivate a business acumen or a product sense for themselves? [00:30:56] The multiplicity of algorithims and the importance of feature engineering [00:32:25] Can you share some tips or words of encouragement for our listeners who's got like a couple of decades, maybe 10 to 20 years of non Data related experience under their belt and they're now trying to break into Data science? What challenges do you foresee them facing and how can they overcome these challenges? [00:35:20] What advice or insight can you share with people who are breaking into the field and they look at these job postings and some of them want the abilities of an entire team wrapped up into one person? [00:39:28] Do you have any suggestions for finance or fintech Data science projects that an aspiring Data scientists could tackle? [00:42:15] What are some things that we need to be cognizant of and monitor and track once the model is deployed, both from the Data scientists perspective and the business perspective? [00:44:22] What advice do you have for Data scientists who have who feel like they don't need to learn anymore? What would you have to say today, scientists in that mindset? [00:46:51] What's the one thing you want people to learn from your story? [00:48:58] So what are the two five letter words that really grind your gears and why? [00:49:06] So what is an academic topic or just an area of research or interest outside of Data science that you think every Data scientist should spend more time researching on? [00:49:27] What is your favorite question to ask during an interview? [00:51:00] What's the number one book you'd recommend our audience read and your most impactful take away from it? [00:52:08] If we could somehow get a magic telephone that allowed us to contact 20 year old T. Scott, what would you tell him? [00:52:42] What is the best advice you have ever received? [00:53:09] What motivates you? [00:53:35] What song do you have on Repeat right now? [00:53:44] How could people connect with you? Where can they find you?Special Guest: T. Scott Clendaniel.