The Artists of Data Science

On this episode of The Artists of Data Science, we get a chance to hear from Jeff Jonas, a data scientist who, for over three decades, has been at the forefront of solving complex big data problems for companies and governments. His software has helped casinos identify fraud, increased voter registration, protected Singapore’s waterways from piracy, and even predicted possible collisions between 600,000 asteroids over 25 years. Jeff shares with us his journey from creating word processors in high school to being able to sell one of his companies to IBM, along with being one of three people to complete every Ironman triathlon in the global circuit. QUOTES [15:46] "For everybody that's had a close call in life…every day since then has been an extra day. When you think about life like that, it allows you to just unleash a little bit more and make the most of it…" [31:01] "…You have to let new observations reverse earlier assertions." [34:31] "If you don't have something that's like 10 times better and high margins, then you can't innovate" [43:03] "…My work is often about helping humans focus their finite resources" WHERE TO FIND JEFF ONLINE LinkedIn: https://www.linkedin.com/in/jeff-jonas/ Twitter: https://twitter.com/JeffJonas REGISTER FOR OPEN OFFICE HOURS: https://bitly.com/adsoh SHOW NOTES [00:01:20] The introduction for our guest today [00:03:53] Jeff walks us through professional journey, how you first heard of data science and machine learning. And what drew him to the field. [00:05:53] Where do you see the field of artificial intelligence data science machine learning headed in the next two to five years? Jeff talks abou how he sees the field flatlining and how COVID-19 is changing the landscape of the field [00:07:55] Jeff talks to us about what he thinks is going to separate the great data scientists from the good ones. He talks about the importance of being able to combine data in a way that is going to make it easy to understand the real world, he also makes a distinction between AI and Machine Learning [00:09:59] There's there's a time very early in his career when he went bankrupt and was living out of his car. Jeff talks to us about what he's saying to himself to get him through that. What did he learn from that to go on to create something bigger and better than what you had before? [00:13:25] When Jeff 23 years old he was completely paralyzed after terrible accident, he talks about his mindset and the self talk he had during that time. He shares was going on in his head and then how he you overcame those challenges [00:16:45] A bit of data history - Jeff talks about the different programming languages he was using early in his career. [00:17:01] Tips for anyone contemplating entrepreneurship [00:20:19] Jeff talks about what he thinks will be the biggest opportunities for entrepreneurship in the post-COVID world. [00:22:33] The one soft-skil that will make or break your career as a data scientist and how you can cultivate it within yourself. [00:24:32] So what compelled you to come to complete every Iron Man on the planet? And can you share some of the many, many accomplishments that you've had in that space? [00:27:01] Jeff describes an ironman event he did in Mallorca, Spain and the logistics of having to travel half way around the world back to Kentucky to compete in another ironman two days later. [00:28:42] The infamous "Tastes like Mango" Story [00:31:25] There's a lot of people out there who were trying to to break into data science. And maybe they don't feel like they feel like they don't belong or they don't know enough. They aren't smart enough or whatever. Do you have any words of encouragement for them? [00:32:41] What's the one thing you want people to learn from your story? [00:33:09] Jumping in to the lightning round: What's the number one book, fiction or nonfiction that you would recommend for our audience to read and who are most impactful take away from that? [00:34:50] So if you could somehow get a magical telephone that allowed you to contact 18 year old Jeff, what would you tell him? [00:35:50] Jeff talks about the work he's done in his career from the Llama Birth registration project he completed, to the modernization of voter registration. [00:37:12] Jeff has over 100 inventions to his name - he talks about some of his most favorite ones. [00:38:30] Jeff talks about the project he did with astronomers which involved identifying where in space asteroids are going to be, and which ones may possibly collide with each other or earth. [00:43:54] Which of your inventions do you think is most relevant now to the current times? [00:45:43] A quick primer on entity resolution and a very simple example of interweaving common sense with real time AI [00:47:29] So what's the best advice you ever received? [00:47:57] Do you have a favorite Iron Man event? [00:48:31] So what motivates you? [00:49:10] So how can people connect with you? When can they find you? [00:49:57] The importance of being accessibleSpecial Guest: Jeff Jonas.

On this episode of The Artists of Data Science, we get a chance to hear from Kyle McKiou, a data scientist who took the lessons from his own struggles that he faced attempting to break into data science and packaged them into a course for up and coming data scientists. He is known for his remarkable talent for building skilled, balanced and productive teams. He gives insight into how he broke into the data science field, his approach for problem solving, and they importance of facing your fears. Kyle shares with us the importance of finding a mentor that can guide you to accomplish your goals and the important soft skills that you may be overlooking. Kyle brings unprecedented wisdom and advice to this episode, and the points he outlines can help everyone step up their professional goals. WHAT YOU WILL LEARN [7:43] What value Kyle believes data science will bring within the next few years [11:38] How to transition into data science [16:33] The importance of cultivating a growth mindset [28:30] Soft skills that data science candidates are missing [33:01] The single biggest myth about breaking into data science QUOTES [16:13] "Be risk averse; Test everything." [24:50] "You've got to engineer a system that solves the problem for you, because if you have to leverage your own intelligence to solve a problem, well, you're going to be very limited in the amount of work that you can do." [27:23] "…you start with the problem you want to solve. You break it down to simpler problems. You break those problems down to simpler problems…all the way back until you get to your present state and then you see the exact path forward at any point time…" [28:31] "…I think in most careers it's not going to be the hard skills that separate you, particularly in data science…[it's] those soft skills, because you realize that if you want to make an impact in the company as a scientist, you're going to need other people to work with you…" [34:55] "…it doesn't matter how much you know, it matters how much you can learn and adapt." FIND KYLE ONLINE Instagram: https://www.instagram.com/kylemckiou/ LinkedIn: https://www.linkedin.com/in/kylemckiou/ Facebook: https://www.facebook.com/datasciencekyle/ Data Science Dream Job: https://dsdj.co/artists70 SHOW NOTES [01:30] Introduction of our guest today [03:10] Talk to us a little bit about how you first heard data science and what drew you to the field [4:50] How software engineering is different from data science [06:42] What do you love most about the field of data science? [07:29] Why do you think the field is headed the next two to five years? [09:46] What do you think is in the separate the great data scientists from the merely good ones? [11:21] Switching from software engineering to data science [12:42] How to productionize a machine learning model [13:19] Why notebooks don't scale [16:18] The importance of the growth mindset for data scientists [19:38] Fear as an indicator [24:29] The engineers mindset for data science [28:30] Soft skills for data science [33:01] The biggest myth about breaking into data science [35:00] Poker and data science [37:07] What's the one thing you want people to learn from your story? [39:17] The lightning round Special Guest: Kyle McKiou.

Remove your self-limiting beliefs, unleash your inner spirit animal, and climb the ranks of the data science competence hierarchy Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience [04:34] Lediona talks about her journey from the research world to data science and touches on some of the challenges she faced along the way and how she overcame them. [06:56] Lediona talks about show passionate she isabout is NLP, what got her interested in NLP and what she thinks the future holds for this particular area of data science. [10:45] Lediona talks about some of the common challenges she's seen up and coming data scientists face when it comes time to take research into production. [14:20] Lediona walks us through her analysis discovery process and the first thing she does when she's taking on a new project. She also talks about some of the steps she takes to keep herself on track while navigating the ambiguity of some of data science projects. [16:21] Lediona talks about the skills she considers to be an essential skill to be and remain successful as a data scientist. [18:25] Lediona talks about what she is looking for in an up-and-coming data scientist. [20:15] We talk about the skills that really set Lediona apart from the pack and the non-technical qualities that's really contributed most to her success. [21:52] We talk more about the growth mindset and how not to let your beliefs limit your success. [22:53] Lediona speaks to her experience being a woman in tech, her involvement in Toronto WIDS and shares some words of encouragement for our female listeners. [24:48] She shares the one thing she want everone to learn from her story. [26:25] Jump into our lightning round with an opening question: Python or R [26:51] She speaks about her favorite algorithm [27:41] What's a book that every data scientist should read? [29:05] How about a book recommendation for people that are wanting to learn NLP. [29:19] We talk about her favorite question to as the interviewers during an interview and how it helps he find out if this is the right company for her. [30:00] We talk about the strangest question she's been asked in an interview and also talk about our spirit animals, and touch on being a generalist or a specialist. [31:02] Lediona let's you know how you can connect with her onlineSpecial Guest: Lediona Nishani.

One of Winnipeg's finest data scientists talks about the skills that have helped him become successful (hint: doesn't involve memorize every hyper-parameter of every algorithm). Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience. Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfData Science, on FB:facebook.com/TheArtistsOfDataScience, and on LinkedIn! [04:38] We talk about how Mark got into data science and the path that led him to where he is now. [05:59] Mark talks about his awesome blog and how creating the blog helped him learn and grow as a data scientist. [07:43] Mark tells us about Toastmasters and how being a part of that has helped him imporve his speaking skills and the benefits that a data scientist can gain from joining a Toastmasters club. [11:00] He tell us a bit more about space repetition and how it's helped him learn more effectively. [12:53] We discuss the similairties and differences between spaced repetition and deliberate practice, and Mark shares his tips for creating flashcards to help you quiz yourself. [14:23] Mark talks to us about the hidden power of compouding and how all it takes to master anything is a solid plan of attack, time, and growth will occurr. [17:50] He share some resources and blogs that expound on the concept of compounding. [18:30] We get into what Mark's creative process is like for bringing his project ideas to reality and he shares tips for the up and coming data scientists who don't know where to start with their project. [19:54] How he goes about identifying where to find data to start working on a project. He also talks about scraping the web for projects, some packages you can use for that, and some warnings so you don't get in trouble. [21:47] Mark talks about how his idols shouted him out on their blog after he scraped their website and analyzed their posting behaviour. [23:18] We get into another project that Mark worked on which involved analyzing data about trees from the City of Winnipeg open data portal. [25:34] He also talks about some interesting and weird data that he's seen out there and then Mark talks about his framework for decision making and how this framework has helped him navigate the ambiguities of data science projects. [27:30] How to use costs and benefits when making deciisons and find out how to best add value. [28:32] Advice for people starting a new job as a data scientist and how to identify expectations and set expectations so that all parties involved are on the same page. [29:47] How he describes his role to people within his organization who don't know what a data scientist is. [30:48] The one thing Mark wants everyone to learn from his story. [32:39] Getting into our lightning round - Python or R. [32:58] A book he recommends every data scientist reads [33:30] His favorite question to interviewee's ask during a job interview. [34:05] Mark talks about the weird question he's been asked during an interview. [34:36] Mark talks about his preference for self-directed learning and projects over certifications. [35:19] How you can get in touch and connect with Mark online!Special Guest: Mark Nagelberg.

There's no way you can't be hype after this conversation. Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! [02:24] The introduction for our guest [04:05] Daniel walks us down the path that led him to data science and machine learning and ties it all back to his Ikigai. [06:05] How the movie Robot Man inspired him to code. [06:49] Daniel talks to us about how he used to work as an Apple Genius and preferred a customer facing role, and how that experience led to him developing his first app [09:41] How Siraj Raval got him excited about machine learning and his experiences learning to code in Python for the first time through a Udacity Nanodegree [14:00] Where Daniel thinks the field of data science and machine learning is headed in the next two to five years. [16:15] Daniel talks about what is going to seperate the great data scientists from the merely good ones in the future he is imagining. He also talks about the struggles of shiny object syndrome that all engineers face and how to approach your work like a craftsman. [19:22] We discuss if data science is an art or a science, how it can be both depending on how you're expressing yourself. [21:11] How Danies expresses himself artistically using data science. [22:16] What it's like when he's being scientific with it. [23:04] How Daniel started on his #100DaysOfCode journey. [25:00] He talks about his favorite day during the challenge. *[25:54] * Daniel shares some tips for our listeners that they can implement today to help them along in their upskilling process. [26:53] How to be a fan of yourself by putting your soul into the work that you're doing. [29:07] How to find a mentor for yourself, how to be a mentor to yourself, and things a good mentor does and doesn't do. [34:09] How a good mentor plants a seed in your mind, and doesn't just give you the answer. [37:30] Why it's OK to suck at the beginning, and how to navigate through that suck phase [39:18] Why you shouldn't compare progress on a day to day basis, but give youself a long enough timeframe so that a meaningful comparison can be made., [42:03] How to navigate the myriad courses out there, find some that will work for you, and design your own "Masters" program. [46:50] How to build enough of a foundation in the basics, and then apply what you learn on top of that using the weekend project principle. [47:39] Why your certificates don't really mean much without a project. [49:16] The one thing Daniel wants everyone to learn from his story. [50:24] We jump into our lightning round - Python or R [50:43] Daniel talks about some books that he recommends and his biggest takeaways from them [53:07] Daniel describes his morning routine [54:32] Daniel tells us the best advice that he's ever recieved - it's from his dad. [55:55] Daniel lets us know how we can connect with him and where we can find him onlineSpecial Guest: Daniel Bourke.

One of LinkedIn's Top Voices in Data Science and Analytics for two years in a row (2018 and 2019) stops by the show to talk about his journey into data engineering, why you dozens of data science certificates are meaningless, and how you can become a data engineer! Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! [02:20] The introduction for our guest today [04:16] Andreas talks to us about how he got into the world of data science [05:58] The importance of having both engineers and data scientists on your data science team, and why you need both to really be successful. [06:35] Andreas talks to us about his upcoming book - The Data Engineering Cookbook [07:57] What his creative process is like for writing the book, and the differences and similarities between that and doing a data science project. [09:56] Andreas shares he views on the value of certificates [11:54] Andreas takes us through a workflow for creating a data engineering project and how you can build one for your portfolio. [14:47] We talk about his new coaching and mentoring platform and what he is aiming to accomplish and achieve with that. We also talk more details for building out a data engineering project. [17:21] More details on his coaching platform and what he wants students to gain from going through the program [19:56] Jump into to the lightning round here. Python or R? [21:02] What cloud platform data engineers should start using : AWS or Azure? [21:46] Self study or certificates? [21:53] Favorite big data tool? [22:09] His favorite question to ask during an interview [23:14] The weirdest question he's been asked in an interview [23:41] How you can connect with Andreas and where you can find him onlineSpecial Guest: Andreas Kretz.

From nearly getting booted from college to going on to earn a PhD in Mathematics. Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! [02:32] The introduction for our guest today [04:07] David talks to us about the struggles he faced when he emigrated to the USA from Peru. [06:01] David talks about how mathematics turned his life around. [06:39] Early in his career a professor told David that "it's clear that your first derivative is positive. The question is, is are secondary derivative positive?" He explains to us what this means in mathematical terms, what the professor meant using the metaphor. He walks us through the troubles he faced being on academic probation, how he tried to get a letter of recommendation, and he talks about the impact that meeting had on him. [10:57] A meeting with a professor who told him that he wasn't good enough to be on this campus. He talks about the pain he felt when he wasn't sure what his path in life was going to be. [11:54] He talks about his experiences at the University of Michigan and the impact of being around mathematicians that looked like him had on his career. [12:24] I ask David what it's like to be a minority in a field filled with people who look like me (mostly Indians and Asians) and he how he views himself in this industry, and how being a minority in the field of mathematics is different from being a minority in the field of data science [16:51] David talks about the struggles and obstacles he faced while trying to get past his PhD qualifying., how he almost didn't return back to school, and how he just kept coming back up after setbacks. [23:46] He shares advice for how to manage the upskilling process thats required to be a data scientist. [26:26] David tells us the one thing he wants people to learn from his story [27:45] We jump into the lightning round: Python or R? [27:55] Favorite classification algorithm [28:26] Favorite question to ask the interviewer during an interview? [29:13] The weirdest question he's been asked during an interview [31:02] David tells us how awesome DSDJ is [32:12] David lets us know how we can find him onlineSpecial Guest: David Tello.

There will be a lot of ups and downs on your journey, but how you end up depends on how you frame them... Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience [02:41] The introduction for our guest today [04:26] Mikiko walks us down the career path that ultimately led to her becoming a data scientists. She came from a completelt non-technical background and through hardwork, determination, and grit she was able to accomplish her goals [07:37] She shares with us the various courses of studies she pursued while trying to find something that really resonated with her [09:43] She then shares with us how hard it was trying to find a job after graduation and eventually ended up working in a hair salon, which [12:07] She talks about how she used this opportunity to level up her skillset so that she could be more competitive in the marketplace [13:43] Mikiko talks to us about the first time she got involved with data anlaytics and goes into something she calls the "MacGyver Principle" [17:31] We talk a bit about thinking like a business leader and why after a certain point, an accumulation of memorized facts doesn't get you to the executive level. [21:09] Picasso and Data Science [23:55] What exactly is a growth hacker? [27:44] Mikiko shares some life lessons she learned from a long time mentor of hers [29:43] The importance of being so good they can't ignore you [32:55] Why you need to treasure a days work [35:58] Mikiko discusses where her desire to help aspiring data scientists comes from [39:45] She tells us about the concept of "mentors at a distance" and shes with us some of hers [40:58] Mikiko talks to us about passion, grit, and a growth mindset. [42:02] How the Pareto principle manifests itself in the day to day job of a data scientist [43:07] Passion is not innate or something to be found, its something to be cultivated through hardwork and sustained effort. [45:25] The concept of adaptability and how its helpful navigating the the data science job search process. [51:24] Mikiko talks about her experience being a woman in tech, being harassed on LinkedIn, and why women need to bring their full selves to the office. [01:03:42] The one thing Mikiko wants us to learn from her story [01:04:55] Jumping into the lightning round - Python or R? [01:05:07] Mikiko's favorite question to ask an interviewee during an interview. [01:06:22] The weirdest question she's been asked in an interview [01:07:31] She tells us what her favorite fiction book is [01:07:57] She shares her favorite non-fiction book [01:08:54] What she would say to 20 year old Mikiko Special Guest: Mikiko Bazeley.

A mock interview with a rising star of our industry. Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! [02:33] The introduction for the episode and our guest today [04:23] Alex tells us about the path that led him to data science and machine learning as a career choice [05:22] Alex tells us about the inspiration behind one of this data science projects [06:46] He then walks us through the plan of attack for coming up with a strategy for executing on his project. [07:48] Alex goes into detail about struggles he had to face kind of sourcing data, organizing his thoughts, the project structure, how he overcome these challenges [08:47] He walk us through his post application protocol for getting interviews [09:51] Some tips on how to find the right people in an organization to reach out to [11:10] Alex goes through, in detail, the challenges he faced in the job search, how many interviews he went on, and how he kept his head right during rejections. [13:13] Alex shares some books and some advice for cultivating the right mindset to navigate you through the job search ups and downs. [14:23] We start off the mock interview portion with the first question usually asked in an interview: Tell me about yourself. [15:50] Can you describe a time when you had to deal with competing priorities or competing deadlines? [16:42] What would you say is the most difficult type of person to deal with and how do you deal with that type of person? [17:50] Can you walk me through your discovery process when you're starting a new project? [19:10] Alex tells us the formula he uses to come up with such well crafted responses to commonly asked interview questions [21:04] Alex talks to us about his process for coming up with questions to ask during an interview [21:56] The one thing Alex wants us to learn from his story [22:31] Jumping into the lightning round:Python or R? [22:44] What's a book every data scientist should read? [23:00] His favorite question to ask the interviewer in a job interview? [23:40] His view on certifications and self-directed learning [24:15] Alex let's us know how we can connect with him and where we can find him onlineSpecial Guest: Alex Lim.

Why diversity and inclusion is necessary in data scientist and why you shouldn't spend your time trying to "spot a fake data scientist". Follow the show in Twitter: @ArtistsOfData, on IG: @TheArtistsOfDataScience, on FB: facebook.com/TheArtistsOfDataScience, and on LinkedIn! Join the FREE open Slack mastermind community where I'll answer questions and keep you posted on bi-weekly office hours: https://bit.ly/artistsofdatascience [2:32] The introduction for our guest today [03:55] Angela walks us through her background and how he started off as a Criminology major, did some data projects, fell in love with the field, and then decided that data is what she wanted to pursue. [05:32] She talks to us about the inspiration for doing the #100DaysOfCode challenge and how it helped combat imposter syndrome. [07:15] Angela walks us through the process for planning out and executing on her undertaking of the #100DaysOfCode challenge. [08:14] Angela tells us about her favorite day during the challenge. [08:55] She then tells us about her least favorite day during the challenge [10:14] Angela tells us how she stayed focused, disciplined, and maintained her execution during her #100DaysOfCode. [11:08] She talk to us about emotional intelligence and why we, as data scientists, need to start incorporating soft skills into our toolkit [12:52] Angela talks to us about some of the skills up-and-coming data scientists are missing and the importance of knowing your audience and how to present to them. [15:43] She also shares some tips on how to network with people in LinkedIn [16:53] She talks about including personalized messages with your request to connect. [17:28] She shares some tips with us on how to present findings and how to develop projects that add business value and address the bottom line. [18:51] Angela talks to us about being a woman in tech, why we need everyone in tech, and how our strength is in diversity. [19:54] Angela shares with us how she finds fulfillment outside of work. [20:54] Angela tells us the one thing she wants everyone to learn from her story. [21:44] Jumping into the lightning round: Python or R? [21:54] Angela tells us what her favorite algorithm is [22:19] We also learn the title of her PhD dissertation [22:27] She also shares her favorite data visualization tool with us [23:05] We learn what her data science superpower is [23:31] She shares the title of her favorite machine learning book [23:46] The largest data set that she's worked with [24:15] Angels lets us know where we can find her and how we can connect with herSpecial Guest: Angela Baltes.