Show notes
01:25 - Teaching, Learning, and Education 06:16 - Becoming a Data Scientist Opportunities to Create New Knowledge Data Science Science 19:36 - Solving Bias in Data Science Weapons of Math Destruction 23:36 - Recommendations for Aspiring Data Scientists Hire a Career Coach Creating and Maintaining a Portfolio * Make a Rosetta Stone Make a Cheat Sheet Write an Article on a Piece of Software You Dislike A Few Times, I’ve Broken Pandas Kyle Kingsbury Posts Contribute to Another Project Post On Project Contribution Spend $$$/Invest on Transition Bet On Yourself 45:36 - Impostor Syndrome Immunity Boosts Community Know Your Baseline Clance Impostor Phenomenon Test Dr. Pauline Rose Clance The Imposter Phenomenon: An Internal Barrier To Empowerment and Achievement by Pauline Rose Clance and Maureen Ann O'Toole Disseminate Knowledge Confidence Leads to Confidence Dunning-Kruger Effect Johari Window Reflections: Mae: Checking out the metrics resources on Impostor Syndrome listed above. Casey: Writing about software in a positive, constructive tone. Mando: Investing in yourself. from:sheaserrano bet on yourself Adam: Talking about career, data science, and programming in a non-technical way. Also, Twitter searches for book names! This episode was brought to you by @therubyrep of DevReps, LLC. To pledge your support and to join our awesome Slack community, visit patreon.com/greaterthancode To make a one-time donation so that we can continue to bring you more content and transcripts like this, please do so at paypal.me/devreps. You will also get an invitation to our Slack community this way as well. Transcript: MANDO: Good afternoon, everyone! Welcome to Greater Than Code. This is Episode number 241. I'm Mando Escamilla and I'm here with my friend, Mae Beale. MANDO: Hi, there! And I am also here with Casey Watts. CASEY: Hi, I am Casey! And we're all here with Adam Ross Nelson, our guest today. Welcome, Adam. ADAM: Hi, everyone! Thank you so much for having me. I'm so glad to be here. CASEY: Since 2020, Adam is a consultant who provides research, data science, machine learning, and data governance services. Previously, he was the inaugural data scientist at The Common Application which provides undergraduate college application platforms for institutions around the world. He holds a PhD from The University of Wisconsin: Madison in Educational Leadership & Policy Analysis. Adam is also formerly an attorney with a history of working in higher education, teaching all ages, and educational administration. He is passionate about connecting with other data professionals in-person and online. For more information and background look for his insights by connecting with Adam on LinkedIn, Medium, and other online platforms. We are lucky we have him here today. So Adam, what is your superpower and how did you acquire it? ADAM: I spent so much time thinking about this question, I really wasn't sure what to say. I hadn't thought about my superpower in a serious way in a very long time and I was tempted to go whimsy with this, but I got input from my crowd and my tribe and where I landed was teaching, learning, and education. You might look at my background with a PhD in education, leadership, and policy analysis, all of my work in education administration, higher education administration, and teaching and just conclude that was how I acquired the superpower. But I think that superpower goes back much further and much deeper. So when I was a kid, I was badly dyslexic. Imagine going through life and you can't even tell the difference between a lowercase B and a lowercase D. Indistinguishable to me. Also, I had trouble with left and right. I didn't know if someone told me turn left here, I'd be lucky to go – I had a 50/50 chance of going in the right direction, basically. Lowercase P and Q were difficult. For this podcast, the greater than sign, I died in the math unit, or I could have died in the math unit when we were learning greater than, or less than. Well, and then another one was capital E and the number 3, couldn't tell a difference. Capital E and number 3. I slowly developed mnemonics in order to learn these things. So for me, the greater than, less than pneumonic is, I don't know if you ever think about it, but think of the greater than, or less than sign as an alligator and it's hungry. So it's always going to eat the bigger number. [laughs] It’s always going to eat the bigger quantity. So once I figured that mnemonic out and a bunch of other mnemonics, I started doing a little bit better. My high school principal told my parents that I would be lucky to graduate high school and there's all kinds. We can unpack that for days, but. MANDO: Yeah. ADAM: Right? Like what kind of high school principal says that to anybody, which resonates with me now in hindsight, because everything we know about student learning, the two most influential factors on a student's ability to learn are two things. One, teacher effectiveness and number two, principal leadership. Scholarship always bears out. MAE: Whoa. ADAM: Yeah. So the principal told my family that and also, my household growing up, I was an only child. We were a very poor household; low income was an understatement. So my disadvantages aside, learning and teaching myself was basically all I had. I was the kid who grew up in this neighborhood, I had some friends in the neighborhood, and I was always exploring adjacent areas of the neighborhoods. I was in a semi-rural area. So there were wooded areas, there were some streams, some rivers, some lakes and I was always the kid that found something new. I found a new trail, a new street, a new whatever and I would run back to my neighborhood and I'd be like, “Hey everybody, I just found something. Look what I found, follow me and I will show you also. I will show you the way and I'll show you how cool that is.” MAE: Aw. ADAM: I love this thinking. [laughs] MAE: I love that! CASEY: Sharing. ADAM: I'm glad because when I'm in the classroom, when I'm teaching – I do a lot of corporate training now, too. When I'm either teaching in a traditional university classroom, or in corporate setting, that is me reliving my childhood playtime. It's like, “Hey everybody, look at this cool thing that I have to show you and now I'm going to show it to you, also.” So teaching, learning, and education is my superpower and in one way, that's manifested. When I finished school, I finished my PhD at 37. I wasn't 40 years old yet, if you count kindergarten had been in school for 23 years. Over half of my life, not half of my adult life, half of my entire life I was in school [chuckles] and now that I'm rounding 41—that was last week, I turned 41. Now that I'm rounding 41 – MAE: Happy birthday! ADAM: Thank you so much. Now that I'm rounding 41, I'm finally a little more than half of my life not in school. MANDO: Congrats, man. That's an accomplishment. [laughs] So I'm curious to know how you transitioned from that academic world into being a data scientist proper, like what got you to that point? What sets you down that path? Just that whole story. I think that'd be super interesting to talk about and dig into. ADAM: Sure. I think context really matters; what was going on in the data science field at the time I finished the PhD. I finished that PhD in 2017. So in 2017, that was that the apex of – well, I don't know if it was, or maybe we're now at the apex. I don't know exactly where the apex was, or is, or will be, but there was a lot of excitement around data science as a field and as a career in about 3, or 4 years ago. MANDO: For sure. ADAM: So when I was finishing the PhD, I had the opportunity to tech up in my PhD program and gain a lot of the skills that others might have gained via other paths through more traditional computer science degrees, economics degrees, or bootcamps, or both. And then I was also in a position where I was probably—and this is common for folks with a PhD—probably one of the handful of people in the world who were a subject matter expert in a particular topic, but also, I had the technical skills to be a data scientist. So there was an organization, The Common Application from the introduction, that was looking for a data scientist who needed domain knowledge in the area that I had my PhD and that's what a PhD does for you is it gives you this really intense level of knowledge in a really small area [chuckles] and then the technical skills. That's how I transitioned into being a data scientist. I think in general, that is the template for many folks who have become a data scientist. Especially if you go back 3, or 4, or 5, or 6 years ago, before formal data science training programs started popping up and even before, and then I think some of the earliest bootcamps for data science were about 10 years ago. At least the most widely popular ones were about 10 years ago to be clear. And then there's another view that that's just when we started calling it data science because the skills for – all of the technologies and analytical techniques we're using, not all of them, many of them have been around for decades. So that's important to keep in mind. So I think to answer your question, I was in the right place at the right time, there was a little bit of luck involved, and I always try and hold myself from fully giving all the credit away to luck because that's something. Well, maybe we'll talk about it later when it comes to imposter syndrome, that's one of the symptoms, so to speak, of imposter syndrome is giving credit for your success away to luck while you credit the success of others to skill, or ability. But let me talk about that template. So the template is many data scientists become a data scientists with this three-step process. One, you establish yourself as an expert in your current role and by establishing yourself as an expert, you're the top expert, or one of very, very few people who are very, very skilled in that area. Then you start tackling business problems with statistics, machine learning, and artificial intelligence. You might not be called a data scientist yet, but by this point, you're already operating as a data scientist and then eventually, you be the data scientist, you become the data scientist. If it is a career path for you, you'll potentially change roles into a role that's formerly called, specifically called data science. But one of the articles I wrote recently on Medium talks about the seven paths to data scientist and one of the paths talks about a fellow who really doesn't consider himself a data scientist, but he is a data scientist, been a data scientist for years, but he's really happy with this organization and his role as it’s titled as an engineer and he's great. He's good to go. So maybe we'll talk about it a little bit later, too. I think as we were chatting and planning, someone asked about pedigree a little bit and one of the points I like to make is there's no right, or wrong way to do it. There's no right, or wrong way to get there just once you get there, have fun with it. MAE: I love what you said, Adam, about the steps and they're very similar to what I would advise to any traditional coder and have advised is take all of your prior work experience before you become a programmer. It is absolutely relevant and some of the best ways to have a meaningful impact and mitigate one's own imposter syndrome is to get a job where you are programming and you already have some of that domain knowledge and expertise to be able to lend. So you don't have to have been one of the rarefied few, but just having any familiarity with the discipline, or domain of the business you end up getting hired at, or applying to certainly is a way to get in the door a little easier and feel more comfortable once you're there, that you can contribute in lots of ways. ADAM: And it gives you the ability to provide value that other folks who are on a different path, who are going into data science earlier—this is a great path, too don't let me discount that path—but those folks don't have the deep domain knowledge that someone who transitions into data science later in their career provides. MAE: Exactly. Yeah, and the amazing teams have people with all the different versions, right? ADAM: Right. MAE: Like we don't want a team with only one. Yeah. ADAM: That's another thing I like to say about data science is it's a team sport. It has to be a teams – it has to be done in tandem with others. CASEY: I just had a realization that everyone I know in data science, they tend to come from science backgrounds, or maybe a data science bootcamp. But I don't know anyone who moved from web development into data science and that's just so surprising to me. I wonder why. MAE: I crossed the border a little bit, I would say, I worked in the Center for Data Science at RTI in North Carolina and I did do some of the data science there as well as just web programming, but my undergrad is biochem. So I don't break your role. [laughs] MANDO: [chuckles] Yeah. I'm trying to think. I don't think I know any either. At the very least, they all come from a hard science, or mathematics background, which is interesting to me because that's definitely not my experience with web application developers, or just developers in general. There's plenty that come from comp side background, or an MIS background, or something like that, but there's also plenty who come from non-traditional backgrounds as well. Not just bootcamps, but just like, they were a history major and then picked up programming, or whatever and it doesn't seem to be as common, I think in data science. Not to say that you couldn't, but just for my own, or maybe our own experience, it's not quite as common. ADAM: If there's anybody listening with the background that we're talking about, the other backgrounds, I would say, reach out probably to any of us and we'd love to workshop that with you. MAE: Yes! Thank you for saying that. Absolutely. MANDO: Yeah, the more stories we can amplify the better. We know y'all are out there; [chuckles] we just don't know you and we should. MAE: Adam, can you tell us some descriptor that is a hobnobbing thing that we would be able to say to a data scientist? Maybe you can tell us what P values are, or just some little talking point. Do you have any favorite go-tos? ADAM: Well, I suppose if you're looking for dinner party casual conversation and you're looking for some back pocket question, you could ask a data scientist and you're not a data scientist. I would maybe ask a question like this, or a question that I could respond to easily as a data scientist might be something like, “Well, what types of predictions are you looking to make?” and then the data scientists could respond with, “Oh, it's such an interesting question. I don't know if anybody's ever asked me that before!” But the response might be something like, “Well, I'm trying to predict a classification. I'm trying to predict categories,” or “I'm trying to predict income,” or “I'm trying to predict whatever it is that –” I think that would be an interesting way to go. What's another one? CASEY: Oh, I've got one for anyone you know in neuroscience. ADAM: Oh, yeah. MAE: Yay! CASEY: I was just reading a paper and there's this statistics approach I'm sure I did in undergrad stats, but I forgot it. Two-way ANOVA, analysis of variance, and actually, I don't think I know anyone in my lab that could explain i…
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