The Artists of Data Science

Support the show: https://www.buymeacoffee.com/datascienceharp Find Lara online: https://www.drlarapence.com/ Watch the video of this episode: https://www.youtube.com/watch?v=jKwGLkMvzis Memorable Quotes from the Episode: [00:38:09] "Whatever we can do to actually, like, connect to our fellow man and woman is really, really important. And exercise and activity is a big way to do that. And the community serves our mental health. Whether you're an introvert or an extrovert, it doesn't really matter. Being around people serves you and allows you to feel like you're part of something like a belonging, right? So I think in all those ways, activity and exercise is huge for individuals who are just really trying to establish a baseline of mental health." Highlights of the show: [00:01:31] Guest Introduction [00:03:03] Where you grew up and what it was like there? [00:04:56] Did you think you're going to be into when you're in high school? [00:06:36] It's fascinating that you love to study humans because we are interesting, interesting creatures.We always like to compare ourselves to others. So what is it? What is it about humans that make us always go through this, this comparison thing? [00:09:13] If somebody else is talking the same topics that I'm talking about and they've got a bigger audience, if anything, they're attracting more people to it. So it's just this little mindset shift. Can we work through my comparison issues on the air? Is that something you want to explore with a couple of questions? [00:13:13] Speaking to my audience, a lot of them are are definitely future leaders, if not already current leaders. It may include senior level management type of level, things like that. As we move up the chain in responsibility it can get tempting for us to take on more and more responsibilities, right? [00:13:31] At some point we need to start saying no, but how? How do we go about saying no? Why is it important that we are able to say no? [00:17:17] "Busy calendar and a busy mind will destroy your ability to do great things in the world." [00:19:02] Decision making is definitely an important aspect of data science, especially at the leadership level. You've got to make decisions, you've got to make them well because the consequences could cost in many different ways. I wonder if you can share some ways for us to improve our decision making process. [00:22:58] Let's talk about self-awareness as it relates to coming up with our values. First, how do we describe self-awareness in this context? How can we use that to help us identify our values? [00:26:37] Is there something that we can attest that we can give ourselves to determine just how self-aware we actually are? [00:29:33] What is this concept of of a personal true north? Talk to us about this this concept and how do we define that for ourselves? [00:31:27] What are some surefire ways that that we can use to make sure that we can avoid distraction and stay productive? [00:35:55] There's an interesting connection between movement and mental health, if you just talk to us a little bit about that. [00:39:49] How do we fight that urge and force ourselves to get that movement in because it's going to help us in the long term, right? [00:44:01] Talking about your obsession with curiosity. What do you find so curious about curiosity? [00:46:31] "I don't need anyone's permission to be curious either. It's free." [00:46:44] What can I do to ensure that I don't do anything that would cause him (Harpreet's son) to lose that curiosity? [00:49:21] How do we cultivate that sense of curiosity as adults? [00:51:24] It is 100 years in the future. What do you want to be remembered for? Random Round [00:52:11] What are you most active with in terms of podcasting? [00:53:06] What is the the life box substance about this? [00:54:46] What in your opinion, what do you think people think within the first few seconds of meeting you for the first time? [00:55:11] What are you currently reading? [00:55:31] What song do you have on repeat? [00:55:54] What accomplishment are you most proud of? [00:56:26] What sport are you playing? [00:56:31] What makes you cry? [00:57:14] What is your favorite city? [00:57:50] What is something you can never seem to finish? -- Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Watch the video of this episode: https://www.youtube.com/watch?v=NstXQM0M5JI&t=5s Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Find Jonathan online: https://www.linkedin.com/in/jonathan-wonsulting/ Find Jerry Lee online: https://www.linkedin.com/in/jehakjerrylee/ Watch the video of this episode: https://www.youtube.com/watch?v=KdfFyY_-XT8&t=89s Memorable Quotes from the Episode: [00:26:43] " I think one of the best things about the content is that we sort of have seen on LinkedIn, Austin, Balzac as an example, he posts very, very actionable content and that's very much what we sort of like to strive for as well when we talk about job search, making it extremely tangible so that someone who is reading it can take action that very second. So for you, if just content that you really like and enjoy with and enjoyed and you're like, Listen, I'm going to try it. If it takes you less than 5 minutes, do it. Try it. Best case scenario is that you get a job out of it. The worst case scenario, you use 5 minutes." Highlights of the show: [00:00:41] Guests Introduction [00:03:41] Jerry, talk to us about where you grew up and what it was like there? [00:05:18] Jonathan Mann, tell us a little bit about yourself. Where did you grow up and what it was like there? [00:06:49] How do you guys know each other? These guys grow up together. You guys go to high school together. You know, what's what's the back story there? [00:07:55] Talk to us first about the genesis of the company. How did this idea start? How did this idea come about? What were you seeing in the world that was just like I just just couldn't take anymore. You had to do something about it. Like, what was that moment? [00:10:49] So Jonathan talks about 'what is the definition of an underdog' . Who are the underdogs? And then maybe after that, Jerry, why is it that companies tend to overlook people just because of their "pedigree"? [00:12:25] What is it about these companies overlooking people just because of their pedigree? [00:14:50] What's like one of the first few things that you start to do with people? What are the first, I guess, myths you start to debunk or the mindset shift mindset shifts you help people go through or anything like that? [00:16:11] Jonathan what is the first two steps to getting from that rejection to redirection path? [00:17:20] When when you go to a LinkedIn profile, what's the immediate thing you go to? Let's start with that, Jerry, and then go to John. [00:19:12] When it comes to the headlines, what is a common mistake you see people make repeatedly when it comes to their headlines? [00:22:02] What are some do's and don'ts that you can share? [00:23:32] What if we just don't feel like we're an expert enough to post content? [00:25:01] There's the creating content, but then there's the consumption of content. How do you how do you ensure that you're consuming good stuff? [00:26:12] There are a lot of good content out there as well, right? Once you have the good content filter down to get your feed full of stuff that you actually do want to see, then it becomes, Oh my God, there's so much good stuff and so many good tips, like, how the fuck do I apply this to my life? What am I supposed to do? Do you have like the tips or a framework on, on how you go about doing that. [00:26:34] In terms of making use of all the wonderful tips that people are sharing because sometimes they just get so many tips, they might just get paralyzed like, oh my God, what do I do? What are your tips on that? [00:31:31] Let's say you applied for a job. You're in the in the interview and you're showing up to an interview and you don't have much experience. Let's say it's an entry level job. So I just want to get your hot take on entry level jobs requiring experience. What are your thoughts around that? How can we break that need experience to get experience a cycle? [00:34:27] Should we worry about looking a job hopper in 2022? What are your thoughts on that? [00:36:52] Before we get to that phase again, job offers and all that stuff, we can't job help them see a job offers. How about those negotiations? That's the critical piece, I think, of the job process. Do you feel that people tend to be afraid to negotiate? And where do you think that fear stems from? [00:38:44] How do you ask better questions during an interview to get to know more about the culture and environment? [00:42:28] Is there a right or wrong way to answer to the "tell me about yourself" question. Jonathan, what do you think? [00:43:25] How should we answer the "what's your biggest weakness" question? Should we actually just say weakness or what's your tips there? [00:44:57] Talk about being an influencer, LinkedIn influencer, kind of the perils of being a LinkedIn influencers. What responsibility do you think it is? I don't know if I'm counted, I only got like 43,000 followers for whatever I'm influencer or not. But I feel like I have some responsibility towards people who consume my content. What are your views on that? What responsibility do we do we have towards towards those who are following us? [00:46:59] Have you guys ever gone to any types of bouts of kind of creative burnout? What was that like? How did you overcome it? What were some early warning signs that you're starting to get burnt out? [00:49:45] What's the right way to ask for a mentor? How do we identify who we want as a mentor? [00:50:59] How do you go about finding this person might be a good candidate or that that vetting process or what have you? [00:52:40] It is 100 years in the future. What do you want to be remembered for? Random Round [00:54:08] What song do you have on repeat? [00:54:41] What talent would you like to show off in a talent show? [00:54:59] What fictional place would you most like to go? [00:55:18] If you lost all of your possessions but one, what would you want it to be? -- Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Watch the video of this episode: https://www.youtube.com/watch?v=DAkvvP6-TuQ&t=14s Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Find Jeremy online: https://www.linkedin.com/in/rjeremyadamson Watch the video of this episode: https://www.youtube.com/watch?v=UglmEt_CRQE Memorable Quotes from the episode: [00:31:19] "Design thinking is a great ideation framework for understanding based on the business outcome, how we can tackle that. It's five simple steps. The first one is to empathize with the stakeholder, and that's a word that I think we need to be saying a lot more in this practice is empathy." Highlights of the show: [00:01:22] Guest Introduction [00:03:04] Talk to us a little bit about how you got interested in data science and what was your path into the field like? [00:05:08] How much more hype has data science, A.I. and all that become since you first broke into the field? [00:06:26] What do you see happening in 2022 in data science and analytics? What's the big thing that you're excited or hopeful about? [00:13:34] What are some guiding principles that we should keep in mind to ensure that we're successfully building and leading those? [00:15:07] What's the etiquette behind the kicking of the doors? [00:16:48] We will get into 'design thinking' part of the book, but I want to double down on the 'process' aspect of the book. What is 'process' anyways and what is it all about? [00:18:16] What are some some ways that we can ensure that our processes remain parsimonious? And if you got any examples that you want to share with us. [00:19:50] Talk to us about comprehensive group of processes that that are required for for project success. [00:23:48] Walk us through prioritization projects. [00:25:25] Identifying things that are important, we talk about this with respect to a project scoping and planning that there's some questions that we should ask ourselves and ask our stakeholders. Two crucial ones. Can you share those questions with us? And what is it that we hope to get from from asking those questions? [00:27:47] When it comes to dealing with stakeholders or let's say we've identified that this is a problem that we should be working on, but how do we make it? How do we frame it from the business problem to an analytics problem? What are some questions we should use to tease out what we need to, to properly frame it? [00:31:06] There's something that you talk about called 'design thinking'. What is design thinking? What's it all about? And what does this have to do with 'process'? What does this have to do with data science? [00:32:42] It seems like designing requires a skills that are underdeveloped in a lot of data science and analytic professionals. How do we cultivate those skills and make that process enjoyable for everyone who's involved? [00:34:46] When it comes to executing a project, does Agile have a place in the data science world? [00:35:32] Do you have a structured approach for generating demand within an organization, especially for new teams where all business functions are our customers? [00:37:00] What is a SKU morph and how can we use this to our advantage in data science? [00:39:20] Are there, if you know of any studies about how agile methods can be applied to teams in data analytics or finance. [00:42:53] How can we start viewing ourselves as craftspeople? What do you mean by a 'bi craftsperson'? How can we start being ourselves as that? [00:45:34] It's been extremely hard to hire and keep great data scientists. Do you have any tips that have worked for you? You've touched on a few of those, but have you got any additional tips for that? [00:47:20] Apart from the technical skills, what is it that you look for in data science candidates? [00:48:39] How can an individual contributor embody the characteristics of a good leader without necessarily having that title? [00:50:11] It's 100 years in the future. What do you want to be remembered for? Random Round: [00:50:45] Let's just think about some interesting use cases for data science and machine learning in the aviation industries. What are a couple of ways that machine learning is being used there? [00:52:37] If you were to write a fiction novel, what would it be about and what would you title it? [00:53:00] What are you currently reading? [00:53:14] What are you currently most excited about or currently exploring? [00:53:51] What's something you learned in the last week? [00:54:02] What have you created that you're most proud of? [00:54:15] Have you ever saved someone's life? [00:54:21] What's the best compliment you've ever received? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Watch the video of this episode: https://www.youtube.com/watch?v=qHjKd4van4o Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Find Nick online: https://www.nicksingh.com/ Watch the video of this episode: https://youtu.be/7fzOYBkTHDM Memorable Quotes from the show: [00:12:47] "The part of math that I was interested in wasn't that crazy, crazy theoretical math. It was just like, Oh, how can we use data to drive better decisions? Like how can simple statistics and computing metrics and just keeping track of shit using numbers? How can that help build better products or build better systems? And that's what I learned in systems engineering. Combine that with some of my CS classes, which got me into a little bit more machine learning, and then it started clicking in my head of like, Oh, this data thing is really cool." Hightlights of the show: [00:00:40] Guest Introduction [00:03:26] Talk to us a little bit about where you grew up and what it was like there. [00:07:57] What is it about us (of Indian heritage) and software and data science? [00:09:11] Was there something you were always good at? Did you think you were ever going to be an author? [00:11:03] Was data science something that you were exposed to when you're young? [00:13:57] What is the business side of data? Please paint that picture for us. [00:19:22] Is it better to have blank space on a resume than neutral information? [00:23:34] LTalk to us about what this philosophy is for projects. [00:31:57] How do we demonstrate business value with a project, especially if we don't have on the job experience and are doing a project to demonstrate our technical ability? [00:39:20] You talk about cold emailing in your book. Is that just when someone messages somebody highly ranked on LinkedIn and leave it at that? [00:40:50] Let's say somebody sees this awesome job on LinkedIn and then started looking for people in that company. Should they go and message an individual contributor, data scientist and have them look at their profile or send a message to the CEO? Like who on the spectrum do they reach out to? [00:46:03] It is noticed that a lot of people that are new to the industry are new data scientists who are all up in their head thinking oh, man, like math and everything, thinking all about algorithms and their sleep. They think that these behavioral interview questions are just fluffy bullshit. Why do you think folks have this misconception? [00:50:10] You talk about a framework in the book at a high level. Can you share a bit of that framework for how you would answer that question (where the star format doesn't apply)? [00:52:34] Would you rather mention your knity gritty experiences from the past in an interview or do mention a little of a role that you played in math or astrophysics. Say that you're trying to get into a machine learning engineer role, can you share your response to that question with us here? [00:55:12] Auditing the "tell me about yourself" question. [01:04:50] What does product sense mean? What is it? Why are people afraid of it? Why does it seem like such a difficult skill? [01:11:35] What's the number one product sense question that you see being asked? [01:14:36] It is it's 100 years in the future. What do you want to be remembered for? Random Round [01:16:18] What do most people think? Within the first few seconds of meeting you for the first time. [01:16:47] You have this awesome blog post about books that you always bring up in conversations. One of them is written by probably my absolute favorite authors and one of my favorite books. That's Antifragile by Nassim Taleb. Talk to us about the three main takeaways you've gotten from that book. [01:21:19] What are you currently reading? [01:24:23] First question what makes you cry? [01:24:41] If you were a vegetable, what vegetable would you be? [01:24:50] What have you created that you're most proud of? [01:25:33] What's the best piece of advice you have ever received? [01:26:54] If you lost all of your possessions but one, what would you want it to be? [01:27:29] Do you ever sing When You're Alone? [01:27:52] What's your favorite candy? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Watch the video of this episode: https://www.youtube.com/watch?v=eYfHD1CkvRI Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Find David online: https://twitter.com/d_spiegel Read David's article "Will I live longer than my cat?": https://www.bbc.co.uk/news/magazine-19467491 Watch the video of this episode: https://youtu.be/pCWH97vBFmU Memorable Quotes from the show: [00:23:36] "...essentially what probability theory allows us to do is to make assumptions about how the world works, how the data is generated, and turn it and flip it around after we observe some data into statements about our uncertainty about underlying features of the world. We can do that, which of course is very explicit based on work indeed, where after processing data or uncertainty and it turns into uncertainty about the underlying quantities." Hightlights of the show: [00:01:29] Guest Introduction [00:03:08] Talk to us about how you first got interested in statistics and what was it that drew you to this field? [00:04:55] Why is it that it seems like mathematicians tend to dislike teaching statistics? [00:08:27] What is statistical science and what is it all about? [00:09:46] You talk about in your book, The Art of Statistics, how to handle problems and approach problems in statistics. You call the P, p, b, a C cycle. Tell us about that framework. [00:15:03] You mentioned in the book that statistics is to blame for the reproducibility and replication crises in science. Why? Why is that? [00:18:23] When we talk about induction and inductive inference, should the philosopher in us get worried at all about the problem of induction in statistics? [00:19:40] Tell our audience about the 'normal distribution'. [00:20:34] Do you have any examples of when inductive inference has failed in statistics that you could share with us? [00:22:15] Why do we need probability theory when we're doing statistics? [00:26:25] I think pouring into the Bayesian stuff is kind of taking a step back here, maybe first principles. But what is probability? How do we measure it? It seems like such a strange epistemological concept. [00:28:27] Can we say there's a at least some type of difference between epistemic probability and some physical or I believe you say aleatory? [00:30:03] Would there be a difference in the way that a philosopher or a statistician would interpret probability? [00:38:32] What's the Bayesian approach all about and why is it that courts in the UK are banning it or have banned it? [00:40:16] How is this (Bayesian approach) different from the frequentist approach to viewing probability? What's the central difference? [00:44:55] It seems like the prior distribution is something that makes base them so controversial. Why is that? [00:46:18] It seems like Bayes Theorem is the scientifically correct way to change your mind when you get new evidence, right? [00:48:18] David Deutsch mentioned lately about the Bayesian-ism, and he's having some qualms with Bayesian ism. He says that Bayesian-ism becomes controversial when you try to use it as a way to generate new ideas or judge one explanation against another. How do we reconcile that when we're faced with some epistemic. [00:49:51] About using it to help us in our everyday lives to make better decisions. How can we use Bayes in that context? [00:53:15] It is 100 years in the future. What do you want to be remembered for? Random Round [00:54:17] What do you believe that other people think is crazy? [00:55:02] What are you most curious about right now? [00:55:55] What are you currently reading? [00:58:33] What do you like most about your family? [00:58:53] What was your best birthday? Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp

Support the show: https://www.buymeacoffee.com/datascienceharp Watch the video of this episode: https://www.youtube.com/watch?v=I6uLiz4lTrU&ab_channel=HarpreetSahota%7CTheArtistsofDataScience Don't forget to register for regular office hours by The Artists of Data Science: http://bit.ly/adsoh Register for Sunday Sessions here: http://bit.ly/comet-ml-oh Listen to the latest episode: https://player.fireside.fm/v2/eac-KT9/latest?theme=dark The Artists of Data Science Social links: Support the show: https://www.buymeacoffee.com/datascienceharp YouTube: https://www.youtube.com/c/HarpreetSahotaTheArtistsofDataScience Instagram: https://www.instagram.com/datascienceharp Facebook https://facebook.com/TheArtistsOfDataScience Twitter: https://twitter.com/datascienceharp