Adam Ross Nelson on Getting Started in a Data Science Career

On this episode of data driven, Frank and Andy interview, Adam Ross Nelson. Adam is a consultant where he provides insights on data science. machine learning and data governance. He recently wrote a book to help people get started in data science careers. Hello, and welcome back to data driven, the podcast, where we explore the emerging fields of data science artificial intelligence, and, of course, the ever present data engineering. although I would say now that we're in season

7, it's not really emerging anymore. You can't go really. You can't walk 50 feet. You can't scroll down any social media platform without hearing about AI and any flavor. I I blame chat GPT. and I've also had a lot of people kind of hit me up on how do I become a data scientist? And, you know, there's a short answer. Right? And there's a long answer. And then there's an answer on how to do it that's written in a book in a book. In a book written by our guest today on the entire book. It's an

awesome book. I read parts of it. and, it's the kind of guide I wish I had when I made a transition from software engineering into from well, I I won't just say software engineering. from Silverlight and, Windows 8 application development, right, which is the most embarrassing thing ever. So welcome to the show, Adam. Thank you so much for having me. I'm so glad to hear, to be

here. and thanks for the compliments on the book. you you are one of the few folks who had a chance to see handful of pages or many of the pages before it launched. So I'm glad you also had some time to look take a look at that. That's cool. Is this your first book or second book or third? this is the first solo authored book. I have another one that I edited. from my previous career. So actually, that's another topic, like, changing careers. I had a different career in law.

So there's a book out there. Okay. Yeah. If you dig deep enough, you'll find a book on school law that I co edited. This is my first solo authored book, thrilled about it. I have another one coming out in a on a different topic coming out in September, that one's with the publisher Kogan page. Interesting. Okay. Interesting. So you're gonna be a multiple book author, which, that's awesome. the the So

that's the issue. I didn't know you had transitioned from another career. we had met through Lillian Pearson, and most people know the name Lillian Pearson because she was one of the first people who had a number of LinkedIn learning courses or lynda.com courses. Go back far enough. on how to how to how to transition into data science or or just on data scientists. Yeah. Data science. And she was one of the few for the longest time that was not a mathematician or

whatever. So when I so she she had this kind of this private mastermind type thing. So we signed we signed up. We're part of the same cohort, and that's how I met Adam. And, so so tell me tell me how did you get into the law? And then what was that day? Well, okay. Let's we don't have to you know, in the virtual agreement, we're talking about lawyers. Right? But, But, the, what made you decide to leave law? Like, how did how did you kinda, like,

start with law and then kinda walk like, realizing, yes. This is for me.

Well, I was transitioning into well, in law, I always worked in education. So, in fact, I went to law school thinking I would work, as an attorney for a college of university, most likely. and then I did work for college universities, mostly in administrative roles and policy roles. for our for for many years after a law school. and I had a well, it's an interesting story because Like many

people, sometimes you sort of hit that plateau in your career. Yeah. And I had definitely plateaued in education administration with my law degree, I was in about 6 years, 5, 6 years. I was runner-up 5 times in national job searches for a new job at a different university. and you know your runner-up because when you get invited to interview on campus for most called university

jobs. You go for a whole day, sometimes a day and a half or 2, and then you either get the job or you don't, and they usually only bring two people to campus. So if you go to campus, you know you're a runner-up. and, I I I got to the point where I realized you know, the the really bookish academic folk were not taking me seriously,

as seriously as I really wanted to be here. job search process because they didn't have a PhD. And then the law folk weren't taking me as seriously as I needed them to in order to really advance to that next step in the career, because I wasn't then currently working, as a litigator,

or as a transactional attorney. Gotcha. So I was sort of in this no man's world, plateauing and that's when I decided to get the PhD. And and I I thought I would get the PhD and go back to education administration but then be able to get past that that hump, that hurdle, that plateau. Yeah. But during the PhD program, I just got really good

at stats. so I just I ended up teching up, getting getting good at stats, teching up, and becoming a data scientist And there's a few reasons for that, one of the reasons is I started working on these projects that were predictive analytics We were mostly looking at ways to anticipate which students would need additional academic support. So we're predicting students who would need the help. And, which is a great project, by the way. We should totally come back to that if there's time.

And then I was telling my friends about this, my family about this, coworkers, of course, knew about this, and everybody started calling me a data scientist. And I'm like, no. No. No. No. Right. I deflected because I thought, well, that's, like, I did. I w I wasn't trained to be a data. I went to law school. I had this PhD in education.

education leadership. and then eventually I just sort of acquiesced, and my boss even started calling me the offices, data scientists, even though HR didn't call me a data scientist, everything else was. Yeah. So finally, I just owned it. And then my first real job. Well, what's a real job? What's not a real job? We have to be very

careful with that kind of language. But anyway, my first job where the title was data scientist, was at a national or nonprofit that helped college university or helped students applied to college university. So, again, we were doing I was doing predictive analytics there, just helping students get to college. The biggest project there was we were looking to figure out, for the students who started the application process, but didn't finish. Why? And then, yeah, and then still, it's a predictive

problem. Right? So you have the students who start the process. How can we predict which students are gonna finish, which students are at risk of not finishing the application process and then intervene to help those students. There's the value on that. Yeah. So that's how I got into data science. I've never looked back, but I've the point is I've been through a couple different transitions, career transitions, My very first job ever ever was an English teacher as

a foreign language. I was teaching English in Hungary, Budapest, hungry, And -- Wow. Yeah. Before the show, I should have mentioned that before the show because we were talking about international travel and things like that. Yeah. So, that's why I wrote the book. This book, one of the distinguishing factors for this book, is it's specifically for I think it'll be useful anybody who wants to become a data scientist, but, this one was really written for established professionals, folks for

whom the the job search isn't the first rodeo. Right? You've you've been through one career. You've done well in one career. and now you're ready for one reason or another for a different career. And if you're choosing data science, this is a really a great book. for you. Yeah. Well, it's an interesting topic because we talk to a lot of data people, just, you know, not data scientists, even data engineers, data administrators, data data analysts. And, of course, Yeah. So across the gamut.

And what we found is, I I would say just off the cuff frame, More than half. Didn't start in data. Right? I would say easily more than half. I would say that

tends to be the the exception. Yeah. and it it it you that leads you to, like, there's an eclectic bunch of people in data. Right? And, obviously, now everybody and their cousin wants to be in this field. Right? Like but Sure. But, I mean, at one point, data was not seen as an asset. It was seen as war liability. We covered that in the previous show. Right? the, but, it was just seen as, like, just You gotta store stuff. You gotta do transactional

stuff. Yeah. And I remember I remember the first time the the idea, and this this is gonna age me out, I guess, or in terms of age, out my age. it was 1998, I think it was, or 1999. And there was, She was a DBA. That was her official title, but she was actually really good at doing OLAP cubes and analysis and stuff like that. And at the time, I was, you know, a a young cocky web developer, and I I was like,

what does that mean exactly? Because, well, I tried to see if, you know, Kangaroo breeding patterns in Australia have any impact on, you know, rubber prices in Malaysia or something like that. It was like And I just remember looking at her, like, you ever hear something? Like, I saw your eyes light up. Right? Like, I was like, you ever hear something that that is sounds insane? but could also be brilliant, and you're not

really sure which one it is. That's how I felt. I was like, I was like, don't ask something. But it was it was, you know, and then at that time, that was I don't think and I don't think the business took anything that she did seriously. I think they kinda It was it was it was years before anyone kinda realized this. And the second time I heard anything about this was about Walmart. how if they detect that the weather is gonna change over a certain threshold in a particular geographic area, that

they'll ship more water gatorade and soda. they can lower the price supposedly. This was, like, 2000. That was 2002. And I was like, oh, that's clever. And it was just like, yeah. You know, the the data's already out there. Yeah. And then Yeah. Just put it to work. Just put it to work. Right? And and that's clever because it's not exactly proprietary data. Right? The weather I didn't want to pull the weather

data. And, it it it's one of those things where when I was reintroduced to the idea of data science, you know, like, 14 years later, I was like, oh, wow. So this really has advanced. Yeah. Yeah. Well, 2002 was one of the points I make in in this book and the one in September as well. data science isn't new. Right. Right. But 2002 is also the year where speaking of Walmart big retailers, where Target, made headlines for predicting whether their customers were pregnant.

Oh, that was 2002. I thought that was I thought that was a little later. I did not realize that. 2002. And for those who don't know, those headlines, is, what we're target really sort of let their AI go off the rails is they ended up predicting teenage shoppers, as pregnant. sending home baby related coupons, parents were getting upset about this. And in some cases, they were predicting customers as the is the the urban legend that's built up

around 20 years is. But anyway, in some cases, as the urban legend goes, the Methos goes around this story is Target was predicting customers as pregnant before customers knew they were pregnant. Oh, wow. Right? So Yeah. 2002 was, oddly enough, it's a turning point. If you go back and map out headlines, 2000 I think people by 2002, people kinda, like, chilled out over wedge. Okay? Right. And then they were they were ready to start getting back to value.

Well, there was also the dotcom crash. I think the hangover from the dotcom crash was starting to clear. You know what I mean? Like and the I mean, that's that's what I remember. you know, it was just that being in technology, you know, you know, in the late nineties was an awesome place to be. After the dot com crash, it kinda like a lot of people kinda washed out because there was no jobs. Like, I I remember part of why I left, New York to move to Richmond, which is how I met Andy.

was, part of it was, I mean, there would be, like, one job opening in, like, 60 to 70 applicants. Yeah. Like, it was just ridiculous. And it was just basically, it became, like, the hunger games to get get a just get a job. Like, not even, like, an awesome

job to a decent one. It was just and I remember, you know, just clawing at clawing just to get, like, you know, an, an interview, and then it became, like, you know, it became like a reality show of, you know, like, how many rounds of interviews can we force people to go through or, you know, That was really, I think, the origin of the lead code interview, was was that like, I remember one guy gave me a pen and a pencil and said, here, code out, code out a program that does this.

Wow. Like, like, by hand? Yeah. Like I don't have, like, a syntax checker. I don't have, like, Right. I don't have a tele sensor, you know, whatever it is. And it was just like, you know, I did it because, you know, I had, you know, rent that needed to be paid. but, you know, and even then, like, you know, that one that took the pull from, like, twenty people. So I was told down

to, like, 4 and then I still didn't get the job. So it became kind of this this this but but I mean, it was and and and and with all the the downsizing and the in layoffs and big tech, you know, we're kinda I I don't think it's gonna be who knows. Right? but, I mean, there's definitely definitely I think your book comes at a good time because there are a lot of people out there that are They're probably pondering the next career move. And, you know, data

science is a is an awesome field. If you have them, you might my my my opinion, and I tell people, it's like, if you have the stomach for the math. Yep. Yep. Yeah. Yeah. You know, actually, on that point, one of the pet peeves I see is, when somebody says transitioning into data science is easy, it's no. It's not. it's not easy. It's doable. Right. It's doable. but I think easy is the wrong adjective there. And then also there's some posts that say you don't have to know math to transition to

data science, which also I think is rubbish. You have to know math. I think maybe the amount of math you have to know can sometimes be exaggerated. Yeah. But, yes, spoiler alert, you do have to learn some math. If you're gonna you're probably it depend unless you are an actuarial, engineer, or an an actual statistic, to transition to data science, you're gonna have to learn some new

math. Yeah. Maybe even in those cases too, come to think a bit, because we approach data scientists approach the statistics different than an actuarial, professional, different than a engineer, different than a statistician. That's true. That's true. And but you're right. Like, and and and when you talk to people, I'm very wary of the become a data science kinda courses that have come out, let's say, since 2018. Right? So when I first made the transition starting in 2015,

There was not a lot of material. Right? Actually, it was Lillian. Lillian was one of the few people that was -- Really? -- not a PhD in mathematics. And, you know, you're a PhD. I I would say this, whether you're a PhD or not. PhDs have a very different viewpoint on the world. Right? Because they they've devoted x number of years to learning a particular discipline. Right? Not everyone can or will devote x number of years to to anything. Right?

Like, and all of which should say when I when I would approach existing data scientists, you know, how did you get it? This is keep in mind, this is, some years ago now. you know, they would say, you know, just go back to school. Like, this one was one guy. I was at a Microsoft Research conference and labs. We've talked about this, this, this, this, event. It's it's only available to Microsoft

employees. In my opinion, I think part of me wanted to just go back to Microsoft after after I personally was laid off just so I can go back to MLS. Like, it's that good of a conference. but, you know, the one one guy there who's no longer he's he's actually I don't wanna say his name, but he He's actually a chief data officer, chief data scientist at, I wouldn't call him a startup anymore, but it's probably a startup you heard of. And,

But it's probably not the one you're thinking. Just okay. No. but, the, It's not OpenAI, basically. Okay. but, anyway, so he, he, he's, like, just turned to me and said, oh, yeah, just go back to school. Like, go get a PhD. Like, it was like, oh, just go get a coffee at the local 7:11. It'll be fun. Like, it doesn't work that way. No. Yeah. So so So but, like, in his defense, right, if you look at his kind of his LinkedIn profile, like, he's been,

you know, he got his undergrad at Harvard. I think he got 2 multiple think he actually now has 2 PhDs at MIT. Like, in his circle of friends, that's like me going to to the local supermarket and picking up a thing of milk. Right? Like, I get it. I get it. You know? And and and the so another another person who was also, like, a super duper PhD at this conference. She was super chill. she might actually still be at Microsoft. said, hey. You know, so I asked her. I was like, you know, what should

I do? And he goes, she's like, well, take a few courses in it, particularly statistics. if you like it, then your passion for it will will will will finish the job. Like, it'll take you over. You'll find everything else you need. It really was. It was like it was for for me, it was life changing. And she's like, and if you hate it, well, ask yourself this quest. She was also from Europe. Right? So they they have a different Worklife. Okay. philosophy

there. She's like and if you hate it, ask yourself the question. Do you really wanna do something you hate. Mhmm. And I kinda walked away from that. And I was like, you know, that's interesting. And, So that was, I mean, that that that was Sage advice, and it turns out that, you know, there were parts of statistics that that I really like, probably because I'm a you know, historically, I've been a lot big baseball fan. and there's parts that I really I really don't like. And

But that's like anything. Right? You know, they have to pay you to show up. There's a catch. And, But you're right. So when people ask me, now I have a book, I can recommend them. Right? Like, but, to to if they want tradition to data science, asked me, like, what should I do? And I was like, well, you really should study stats because that's probably about 80% of the lift right there. Sure. Yeah. I I think I agree with that. Yep. And I would say

15% is calculus. And the remainder is probably game theory and linear algebra. It'd be kinda how I break it down. Yeah. I would add, and actually in the book, I've, on the advice of a fellow data scientist that I know who works for a big Big Engineering firm that's over a hundred years old based in Minnesota. You probably figure out what that one is. Play this game cap. We're gonna allude to company. He's a

data scientist there. He really encouraged me to add a section on contributing to sales and business savvy. Oh, wow. Yeah. For this book. Yeah. and and I see that as a mistake that some folks trying to make that transition from some fields, not at all, but but more of the bookish fields, like the academic folks transitioning into data science, there's there's a there's a diminutive association associated with doing sales. I would I I would say it. I would

say it's a flat out stigma. Yeah. It's a stick. That's a better word. Yep. Yep. It's a flat out. And I I I actually just came up the other day in my day job is that, you know, somebody who is a very talented engineer he he's wanting to learn to pitch, like, in how to do sales. Okay. And, like, I think I I don't wanna put thoughts in his head or words in his mouth, but I suspect that that comes from that background wearer. Yeah. He was very hesitant to do that because and I kinda

had my revelation with Like, it is it is a process. Right? And and and, you know, Andy and I have talked about the number of sales gurus that we've that we've listened to. I I can recommend Grant Cardone. He is an acquired taste. I'll put that right out there. Right? I mean, the the the putting in context, though, I first heard of this guy, if anyone can remember meerkat. meerkat was an application, that was the live streaming application. Think it came out during a south by southwest

It was the 1st, like, live streaming thing you could do on your phone. Now everybody can do it. Right? Yeah. But he was, like, the number one meerkat your cat or your cat? I don't know. He was not one user of it. And, like, I installed the app, and I remember because I had just given up on Windows phone. Right? And I got an iPhone, so I can actually all relapse. And your cat was one of the first things I installed. And I kept seeing these notifications on

like Grant Cardone is doing this. And every time I tune in, it was basically him, you know, talking about sales and stuff, being very sales y. Right? Yeah. And and at the time, I thought of that as a pejorative. Yeah. It's easy to think that way. It is easy to think that way. And, I find

myself being a sales apologist internally, like, a lot. Like, like, you know, they'd be like, oh, sales people have no attention. No attention span. I'm like, that's not true. They have no attention been because if they and and and it's about, you know, getting other non sales people to thighs with them. Right? As as much as I load the word empathy, and there's a whole story attached to that. The feeling of empathy is awesome. The way that has been mutated and used in

this empathy industrial complex is what I have the problem with. Okay. but that's a that's a rant for another day. Okay. But, the, the the the, you know, I was just basically saying, like, you know, if if if you're not in sales. You don't understand what it is. Like, if you don't sell, you don't close, your kids don't eat. Like, it is really it really is that type of thing. And you see all the braggadociousness and all kind of the the the hoopla around it. A lot of that is masking a lot of deep

seated insecurities. So you have to kind of but if you ever wanna get a salesperson's attention, show them how you're gonna you're gonna help them make their quota, right? Make their money. Right? And I've kind of done a lot of work in, you know, with with kind of like, you know, oh, they have no attention span. That's not true. They have no patience for nonsense. Right? And that nonsense is kind of like, you know, what you think is an engineer

is co I catch myself doing this whole time, right? because I'm a sales engineer. right, where I'll be like, oh, that's really cool. And I kinda have to pull myself back. Thankfully, with the help of, you know, my my manager's kinda mentoring on that. He goes, he just always tells me, do this. anything you do do through a lens of sales. Yeah. And so I always have to kinda pull myself back and like, okay. Yes. That is a cool tech, but

how do we use it to sell and solve the solution for customer. Right? That's a hard thing to do. and I don't remember how we ended up in this rabbit hole, but I think it's I think that's a good addition to your book because Yeah. If nothing else, if you're changing careers, particularly people who are changing careers. They need to sell the hiring manager on. Why should I pick you? Yeah. Like, why can't I get Johnny or Jamie or, you know, Bob or Barbara who who

who have been doing data stuff for years? Yeah. Why should I take you? Like, you're you you were, I don't know, a lawyer? A lawyer. Right? Like, why should I take you? You were in marketing. or you were in public relations or you were a teacher or you were what? Right? Well, the advice I give in the book is, at the very least, you want to damage rate and awareness of appreciation for and a knowledge of how the company, makes money. Yes. Right. And if you're and and and, and

how data science can contribute to that bottom line. And I also speak a little bit about nonprofits in section 2 because there, we're not taught we're not worried about profits, but we but non profits have revenue. So how can data scientists contribute to the revenue?

And, one of the thing one of the specific use cases that I'm loving recently, I didn't do talk about this in the book, one of the specific use cases I'm just loving recently is using data science to, hone or refine, basically predict the best ask of a potential donor. So development professionals. Yeah. Fundraising professionals. They'll have their database of potential donors, we can use data science to estimate what's the best ask for that donor. Interesting.

And you could and it's a classification problem because there's different kinds of asks. Right? Some people wanna do state giving. Some people wanna just give a one time check and then move on. Some people wanna make pledges for 10 years. so that's a classification problem. And then it's also a regression problem because you have to pick a number. So, anyway, if you're if you're getting for an inter if you're getting ready for an interview, that the level of granularity you need to bring to

the interview. You have to make specific suggestions as to how data science can contribute to the company's revenue or bottom line or both. Yeah. That's good advice in any technical interview. Sure. You know, I mean, really, you you definitely wanna you definitely wanna know how the company makes money, and then you wanna know as much as you can about how the department you're applying to contributes to that. and then you can pitch it

where you're doing what Frank says. You're gonna go pitch yourself with that role and talk about ideas that you may have. You'd definitely don't wanna give away. Yeah. you know, give away the farm on on any of that. There's an old data joke, where in the first frame, the, the the interview WER is asking, do you know, can you tell me how a deadlock works? and the interviewee says, if you hire me, I will. Yeah. And they just sort of demonstrated a deadlock. right there.

Okay. That's a good one. That's a good one. I like that one. Very meta. Very meta. Yeah. You know, Frank, you were talking about, the bread vise, just go to school, just get a degree like you did at coffee. I have a whole chapter on that where I the the subtext is, well, actually, no. Maybe it's not like maybe it's more overt in that chapter I think about it, it's really going through the decision process associated with another degree, a certificate, or or self study or a combination.

it the the solution to that is different for every every person is gonna have their own path. There's no rider runway to make the transition. That's true. And and and it's one of those things where part of part of the way through my transition, there was a, YouTube video. I forget who it was. It's not like a famous YouTube or anything like that, but but she's basically had thing where, you know, how I transitioned in 6 months? It's like

a TED Talk or TEDx Talk or something like that. And, like, it was like, oh, so it is possible to do it, but do it at speed. It's not easy, but, you know, dual. It is doable. Yep. And that's the thing. Like, you know, I think people who, I'm sorry, cut you off. Yeah. No. I think people people will sell snake oil. Oh, you don't need to learn math. Like, yay. And I would I would I would be kind of, like, I would go a little bit too far the

other way maybe. Like, I think, I don't know how many certifications I got that 1st year. I think it was, like, 13 or 14 some odd. Wow. Thank you. and because I just went, like, full on, and it was just kinda like and I'm like, I will read research papers, even though I didn't really have to. Yeah. Right?

Just because, like, I knew I would be occasionally and I would tell I would tell, you know, what's this when I was in Microsoft, you know, it comes in handy now too. you know, I may be in the room with mathematicians or hardcore data scientists. You know what I mean? Like, there's different like, my son's played this video game and, like, there's, like, different classes or characters. Right? Like, it's kinda like a dudgers and dragons from back in the

day. Right? You have a was a mage a warrior, an elk, an elk, an elk, an elk, and then, like, couple other things. But, like, there's different classifications of data scientists. You know what I'm talking about. Right? you know, there's the PhD ones, like the super heavy math people, and then there's kinda like different levels of, you know, well, they were data engineer, and now they kinda now they're this, or they used to be a developer now they're Like, there's different

types of ones. And, like, I would always say, like, the the ones that always carry the most weight in a particular customer account. would probably then be the math, everyone's. And I would always, like, read the crazy math and get into that, you know, as as long as my as as far as my little brain would take me, right, not because because I would say, like, you know, I would say, like, look, I I know I'm not gonna go toe to toe with these people. But if I can step in

the ring, I'll lose. That's fine. But at least I look like I belong there, and I think earn a lot of their respect that way. And then sometimes I think I think that's good advice for career stuff too. Like, you know Absolutely. train hard, study hard, you may not win the fight. Right? It's not life's not a rocky movie. Right? But the fact that you you can be in there and look like you belong there. Yeah. is

half the battle. Well, I was working with a career coaching client who was comparing themselves to Sebastian Raschka. who, is now he's the kind of data scientist who is inventing new math. Right? Like, he's, like, He's if you don't know Sebastian Raskett, several bugs, professor University of Wisconsin, where I teach also, but he's inventing new math. And I said, hold the phone. Sebastian Raska is a different kind of data scientist. He's inventing

new math. You don't need to be able to invent new math to be a data scientist. And in fact, in fact, if you're inventing new math, you're probably gonna be less well positioned in many ways to offer value. because the new math is untested. The new math hasn't been productized. The new math isn't ready for market. What's ready for market, what's been tested, and been productized is good old logistic regression, k nearest neighbors, those support vector machines, those are the that's what

brings value because we know the methods. We we've tested them. Right. And people like him are gonna be bored out of their skull on your average job. Oh, yeah. Yeah. He wouldn't run. He I would agree with you. Actually, now I actually, Nick, I wanna see him and be like, Hey. Have you ever just thought about being a K nearest neighbor's engineer? Like, you're trying trying to get smacked off top of the head. That'd be

hilarious. Like, you know, but I mean, but I mean, you know, one of the things is, and it wasn't in the chapter I read, but but one of the things that I think is a huge problem in technology jobs overall, not just data science, although I think it's it's written large now in data science now that it's the new hotness. the job requirements and the job descriptions. So weird. That's a that's a topic. I I gotta where are you going with this one? Because this No. No.

Like, I mean, like, So so here's a here here's a good example. Right? And I I don't know if you've heard this one before, but I wanna see the look on your face, you know, when when you hear it. I got a call from a recruiter some couple of years ago that they wanted a full stack data scientist. Okay. And the pay -- -- a new word a few years ago? Well, I think the impression was. And I I I kinda pulled the thread on the head recruiter,

mostly out of curiosity, not because I had any interest. but I was like, well, when they say, like, full stack data scientists, like, that could mean it leads 1 or 2 things, probably more. But I took that as 1, you take you you you panel the data from ingestion all the way to pushing the model production, which sounds reasonable, I think. ish, reasonable ish. I see Andy -- I'm shaking my head. -- isn't taking my head back. Not not a scalable model. But well, if it's a 7

figure saddle, Okay. Then that's reasonable. Right. because you're doing 8 jobs. Cho. Also data science is a team sport. It is. Yes. I'm skeptical, I'm skeptical of that, but maybe you could make it work for a little while. But apparently, they wanted someone who would be able to develop the like, they met full stack developer plus data scientist. Yeah. Oh my goodness. That's 2 jobs. Ah, at least. At least. Yeah. which I

was kinda like, you want that? And and I look at job requirements, and this is this is this is, pressing down my mind because we're we're, you know, my team probably next calendar year, we'll we'll end up hiring for people. But, you know, we're kind of like, well, what do we want? We obviously need someone who knows open shift, obviously. but we also want someone who's a data science or data engineering background, and also that's kind of a if you draw that

Venn diagram, it's a very small subset of people. So it's kinda like We've had this kind of this philosophy of, well, you know, I thought about extreme examples. So, you know, it takes somebody like, you know, like that, professor who's who's inventing new mask play. And he he'd be bored out of his mind. Yeah. Like, you know, in in a job like this. No offense to to to what what I do. Right? Like, but before I have a to be clear. Or or anyone on this call, right? Like, right?

Right? Right? So they'd be bored out of their mind. It wouldn't be a challenge. So, like, you know, there's And that's just the same problem I saw it, like, in the early days of the web where you went from where there was a webmaster who did everything to then it kinda broke out into specialties. Yeah. But but I don't but the same problem exists from even before the Internet, you know, imagine those days. but the job requirements were always just like, you

know, really intense. This is a longstanding problem in IT. maybe the other fields too, but but what are your thoughts on that? And, like, you know, and as particularly can be intimidating for career transitioners. Right? Like, I'm thinking, you know, well, you're a baseball fan. You told me that earlier -- Yeah. -- on the show. could you imagine a full stack midfielder?

That's a joke. Right. It just doesn't exist, right? Or or what about, like, a full field midfielder? Like, there's like, that position doesn't exist. Data science is a team sport. You need to field a team as an organization, you need to feel the team to, implement data scientists or data science work. that's just the way that's the way the world in my view. And maybe that feels extreme to some listeners, but, I'm skeptical of Now, I'm not skeptical

of the notion of a full stack data scientist. I think a full stack data scientist can function really well on a team. Right? So maybe there's a data scientist whose job it is to know a little bit of all of the team components, and maybe having has a little bit of experience in all of team components, but there's also a data scientist. There's also a database engineer. There's also a software engineer and then and if you're thinking about more of the phases, there's someone in charge of of

extracting, collecting, cleaning, preparing data. There's someone in charge of modeling refining, testing, and then there's someone else in charge of putting into production. And then don't forget you need someone else in charge of of grooming the work to make sure that models don't decay. Right? So, like I said, I I guess maybe my thought are are I'm not skeptical of the notion of a full stack data scientist, but I think a full stack data scientist in a vacuum is not a strategy

for success. Right. Right. It's totally not scalable. And and what they were like they ended up the recruiter actually shared with me at the pond. Like, you know, we we're having trouble finding somebody. So is the custom you know, so is the end client. And I'm like, no kidding. Yeah. You know? And, like, And I don't wanna beg on tech recruiters because I think they have gotten better, but, like, I remember hearing. It's a tough job. And and my my neighbor is actually a a tech scruder.

And and, you know, HR people I'm gonna I'm gonna play this the the generalization game, but that's okay. I have some stats to back me up you know, IT people tend not to be the most gregarious human beings in the world. Right? That's not crazy. Right? -- crazy talk. they tend not to be. Right? I'm not saying it's impossible, but, you know, but an HR people tend to be They don't know how to re interact, I think, at at at scale yet,

like, how to interact with IT people. So how do you get you know, and and I think combined with, like, these ridiculous tech requirements, you know, or or be rex, right? Like, you know, you have to know this. You have to know that. You have to know that. You know? And if you come hold a thread at any of those. Like, well, does your company do that? No. We

don't have any of that that techno. Why are you asking for it? You know, like, it is it becomes this kind of it becomes a game, and it's it's it I'm not really sure who's winning at said game, but Yeah. It's not the average kind of,

you know, applicant in in IT. Right? Right. I don't know. Like, I just, you know, but I mean, like, is there any advice in the what advice would you give, or or is in the book that to If I'm a career transitional and, you know, all the job requirements is that they have to have 9 to 10 years of experience in you know, working in IT. Right? And my my background is, say, marketing. Right? Like, what what would your advice be?

Well, that is one of the the the tougher things to really suss out for transitioners. and one of the things you can do is a job description might be specific and say so for data science, job description say, I want the company wants 5 years of of experience, or the job description might say, I want that employer wants 5 years of experience in data science. And some, some recruiters, job description writers are intentionally

writing the former. They're just saying 5 years of experience knowing that people, they're also open to folks transitioning into the field. So, like, well, let's take, well, let's take Lillian, for example. Right? So if I was advising Lillian, And back when she was first transitioning into data science, I think I know enough about her resume, I would say, you're gonna apply for jobs that ask for up to 10 years of experience, period, because she had about 10

years of experience as an engineer. Right? And then you're gonna you're gonna tread more cautiously on job descriptions that say, they want specific experience in data science. And then that's one of your research tasks

on on on informational interviews. Right? A lot of there's sort of a lot of, sort of nonspecific advice on information interviews, but one of the really high value questions to ask in an inter inter informational interview is, this question, when your company makes a job description and says, x number of years of experience, are they typically looking for x number of years of experience in that specific role? or X number of years of experience

in general. And and sometimes that can that can be really consistent across an entire organization. Sometimes depending on the branch of the organization, it can differ, but that is one of the most high value questions you can ask in an inter informational interview. It will give you intelligence that will inform your job application decision making process in really important ways. Interesting. That's a really good point. And

I I I love where we're I love where we're going. I love everything we've covered. I know, I have as to make up for, being late, I have a hard stop. So, yeah. And we have we have these questions that we like to ask every guest, madam, and I'm gonna kinda pivot into that. I'll start with the first one. How did you find your way into data, and I think you partially answered this at least. Did data find you, or did you find data? Yeah. It I think day

initially, data was finding me. I just had jobs at work that recalled for data science So I did data science. I solved the problem that was ahead of me in front of me, even though I wasn't a data scientist. And then eventually, I decided, oh, This data science thing is a thing for me. I decided to become more intentional about it. Yeah. That's how that's that was my path. Good answer. That's cool. Alright. So what's your second question? What's your favorite part of your current gig?

But first, what is your current gig? you you mentioned in the virtual green room, you travel, you teach. What what do you consider your gig? what is your favorite part? primarily, I'm a career coach. I

help mid and late career professionals, folks who were like me when I transitioned to data science, transition into data science. So folks who have already been successful in at least one other career, and now they're ready to come into data science. and that's why I wrote this book. How do we become a data scientist, a guide for established professionals? I know you have another question coming up. What what when I'm not at work, what do I enjoy

doing? That would be teaching. So I mentioned actually, I even on the show. I mentioned, I work at University of Wisconsin, teaching statistics, data management. And then every once in a while, do a semester of education law because they really, really need help with that. hard to find, as you can imagine, people to teach that niche. and it was since it was my former career, I say, yeah, I can do that. So, I

stay really fresh. That's one of the ways I stay really fresh is by teaching statistics, data management, to grad students, university of Wisconsin. So that's one of the things I do when I enjoy when I'm when I'm I do for enjoyment when I'm not working, in data science or as a career coach. That's interesting. So have you seen with the rise of, of these technology? Have you seen more interest in that space? Absolutely. the students

are are really asking. They are because they know I became a data scientist, and they know my full time work is data science and career coaching. so maybe it's a function of that, but I I've I was getting those kind of questions before I was a full time coach, to yeah, students know. They just know. They're in grad school, and they know that academia is not necessarily what it used to be, and they wanna know how to get into data science. So I'm spending a lot

of time right now talking with folks on campus. How can we bring some of the more relevant, skills to the classroom. For example, on college campuses, we spend a lot of time teaching Stata. which, if you don't know, is a fantastic software, but it's really niched into economics or camp college university campuses. So how can we continue our honoring our heritage with stata, which again, great software, but also expose students more to R and

Python. For example, this is one of the many examples. Interesting. Interesting. I had not heard of state in, like, 5 years. You're the first person to mention it in, like, 5 years. It is. I I still use it daily. I, like, I'll have data here and Python there, and I go back and forth. Oh, nice. Yeah. Very nice. Well, you answered 2 of the questions. that we, that we had there together. I just I wanted to ask another question since we you've, you've

taken one out. The, One of the popular speakers in the Microsoft data circuit probably 10, 12 years ago was, David DeWitt, Okay. And I understand he was at university of Wisconsin. At least out of Madison, I think it was. Yep. That's where I live. Sorry. Not take that. Well, take that No. Yeah. He was a teacher there. Wisconsin Madison. And then, I'm just I pulled up Wikipedia while you were chatting. He was I started the Wisconsin database group says, but it needs a citation

for that. And it says here he's he moved to MIT. I didn't know that. He was still at u of w when he spoke at the the largest data Microsoft data conference on the planet is called, the Pass Summit. It happens in, Seattle every year. and he did the keynote out there a few years and just blew everybody's mind talking about database theory and some of that. Just curious if you ran into him out there, or if he if he's left, probably no one knows knows him. I haven't.

I'm gonna have to add him to my list of folk to try and connect with, the, yeah, the current well, now as soon as I name one person, people I leave out are gonna be really disappointed. You know, it's not for what it's worth, maybe this is just a chance for me to plug. Go badgers. Big 10 University of Wisconsin, Madison. I mean, one of I had statistics with a former member of the White House Council of Economic Advisors as my

professor. at Wisconsin. Right? So that's a big deal. Right? And and you can say similar things about other professors teaching stats at other important schools. But it it it surprises me, not at all that a superstar like David Duett was at Wisconsin. Yep. Yep. Cool. Okay. I'll I wanna jump back into our questions here. So another complete this, sentence, is I think the coolest thing in tech today is blank. coolest thing in tech today is Oof. This is the tough

one because there's so many choices. I have analysis paralysis and decision paralysis on this one. I you know what? Can we I'm still can we come back to this one? Sure. Absolutely. Yeah. Yeah. Let's come back that one? Well, we haven't gotten feedback that, you know, we should mix up the questions a bit. So, we're doing that right here in real time. Sure. So I'll skip to I look forward to the day when I can use technology to blank. Do nothing. I look forward to the day where I can

completely unplug. I I won't have to worry about email anymore. I won't have to worry about text messages anymore. wanting to worry about social media notifications anymore. I look forward to the day where I can completely get away from technology. I mean, it has been my livelihood now for many years, and I'm grateful for the livelihood that technology has provided me. And I will be happy in tech career, probably for the rest of my professional life. but I also do look forward

to the day where I can unplug. So maybe there's a configurate answer. I'd be interested if anybody else has given a similar answer on the show. Hi, Dev. I think, a lot of it has been around auto around so they could do more things they would enjoy. Although the idea of an Adam GPT bot that you could email back and forth with and converse with, that would be pretty cool, actually. I could be cool. Sorry. alright. Andy, you wanna take the next one? Yeah. I can do that. or

whatever. Yeah. We'll, we'll go to share something different about yourself, but we remind every guest that it's a family podcast. Family show? Yeah. Yeah. I,

so my first job, full time, adult job, after high school, but before college, believe it or not, was teacher of English as a foreign language in Budapest, Hungary, really like telling this story because from then on, it was in the late nineties, a little bit older than I look. It was in the late nineties, and, getting that foundation of managing a classroom, planning, you're planning the fates of other people in this constrained

way because you're in charge of what they're learning. They're in charge of what they're learning too. It's a collaborative thing. huge professional development opportunity for someone in their late teens, which is what I was. when I did that, One more. here's a fun one. I also was, I did a short stint as a professional speaker for mothers against drunk driving. Really interesting. Okay. I yep. I was the guy who came to your high school. I did middle schools

too. We had a different show from middle schools, different talk from middle schools, But I was the guy who came to your show, talked about healthy decisions, a little bit of some life planning, a little bit of relationship stuff, Believe it or not, we didn't touch so much on drugs and alcohol. We talked more about general wellness. And then for, the middle schoolers. We really were in the wellness, in the wellness, topics, to be more age appropriate for the middle schoolers. I spoke to tens

of thousands of students at 100 of schools in that -- Wow. -- roughly a year. I was with them. So Wow. You were doing coaching even then? Yeah. In a way. Yeah. Although I was doing group coaching sessions, for, I think the smallest group was maybe 50 students at a small school. You know, my largest audience, I think it was the, Oh, god. What was the name of this? National it was a National Association meeting of 1 of the 1 of the high

school, Oh, gosh. What was I can't remember the name. Anyway, there were, like, 6000 students in this convention hall. So that was my largest audience ever. that I didn't draw them to the let's be clear. I didn't draw them to the convention center. Motors against what Driving did. but that was also a really powerful experience. I I really enjoyed the time speaking, being a professional speaker. Very cool. That's cool. Yeah. Alright. So we're gonna check-in on that background

thread. Have you, thought about what the coolest thing in technology is? You know, I'm gonna go with the low hanging fruit. I'm really trying not to do this, but I gotta go with generative AI. Yeah. Yeah. It's it's really prescient right now. it's pervading everyone's thoughts. coolest thing in technology right now. Could I also give you the most worrying worrisome thing in technology is related It's all of the folks who are resisting generative AI, just

absolutely gosh. I I I just, I'm I'm I'm I'm worried that folks are gonna resist generative AI in a way that's going to inhibit our ability to adopt AI in thoughtful humanistic, productive, ethical ways. I'm really worried that that's going to get in the way. Yeah. The knee jerk reactions have been interesting. And and and to be clear, like, It's really around the the text generation. Right? Like -- Yeah. -- you know, the the art generation stuff, you know, there were some dust ups because

it won, I think, the Colorado state there. Right? But but nobody flipped the bleep out. Right? and the reason why we we choose the family friendly thing is because I listen to cancel the kids in the car. I'm assuming others will too. So that's why. So they they literally lost everybody lost their lid when you know, when when when in the text generation, I thought that that says something interesting about kind of how we communicate as human beings, personally.

you know, obviously people have been kind of you know, biting their fingernails over deep fakes and stuff, but you're right. Like, you know, the knee jerk reaction of the New York City public school system and again, on another rant soapbox I could go on with the the New York City public education system as a as a wouldn't say an alum because I didn't graduate from there because I went to a different school, but, you know, for them to ban it was was kind of I understand the

reasoning is kind of over overstepping. Right? It's kind of like if I if I have a mosquito on my arm, I I I slap it away. I don't get a mallet or hammer and just start smacking my my my my my my arm. that's kinda what it was. I think Italy now is is trying to ban it. I think banning things is 1 should really be the option of last resort. Yep. Because, I mean, look at this, look around you. Like, you know, there are a lot of things that are banned.

They are specifically illicit narcotics. I wouldn't say they're easy to get, but you can still get them. You -- Well, what I you know, what I think about when I hear stories like that, especially of the of the banning stuff, I'll I'm I'm 59. I'll be 60 in 3 minutes. And so when I went to, went to high school, calculators weren't new, We were about a generation. Yeah. We were a generation beyond the the the ones that were that did that or a

fraction of that work, and they were huge. And we didn't have as far as we didn't have graphing calculators at that time, they did show up when I was in in college. But I went to college about 10 years after I graduated, so we had graphic calculator soon. But that that's what I think about it. The teachers would, you know, the it's an old joke. It's all over social media, but it's true. They would say, you know, in calculus class, the teacher would allow us to do later

tests with the calculators. Once Once he knew we understood the principles. But before then, it was by hand. Mhmm. I learned how to use a slide rule, but not really well. I just gave. It was kind of like Here's a slide rule, and this is how we used to use them. And, you know, you watch that scene in Apollo 13 where he's chained everybody's checking the calculations, and they're all doing the slide rule stuff. So I don't remember how to do slide rules. I didn't do it enough,

but the teacher would ask that question. Are you gonna have a calculator with you the rest of your life? And I'm like, You know, now the joke is I am gonna have to get the way it is. And a and a television studio. Yep. Right. Right. Right. You know,

it's so I and I wonder how much of it is kinda down at that same vein. And I'm not against that. I mean, You know, I I want people to be able to, to do the math. You know, it's as much as you can because there's something about putting a pencil to the piece of paper and walking through the exercise, and

and I'll just I'll just say this. Even though I can't do it, I'll just say this that, you know, type in 6 letters into Excel, with an equal sign in front of it hitting it again per end and having it pop up the parameters is not the same thing. And, you know, we're We're living in an age, and I don't wanna I'm not gonna say I'm not gonna clarify what I'm about to say. I'm gonna be intentionally vague here. But we're living in an age

where things may go away. That's not you know, it's more a distinct possibility than it was 10 years ago. And so what if? you know, what if we lose the ability to do, some tech, or we lose it for a while, you know, math is still gonna be a thing that we need to do. So I I agree with the intention, and I I'll say it this way. I respect the intention. That's a better way

to say it. And and especially when it comes to to that, I'm and having spoken to a parents, we talked in the, you know, the electronic green room about all all the kids and grandchildren I have. The, you know, I could get it as as that point. I'm but being a data engineer, I don't and don't quite connect all of the dots to banning the, the AI stuff. I don't I don't get it. I understand the fear. I I

get that part of it, and I think some of it is is justified. Maybe more than people are, you know, willing to give it credit for. And I'm I'm about to order a t shirt that says I need new conspiracy theories because my things have all come true. Is that from is that from the WIFILs? No. I don't think that I think it's a it's a it's a reporter online. I'm trying to remember which one, but Yeah. That's that's a that's a cool, cool t t

shirt that I need to get as well. But, anyway, it's just, you know, I'll I'll stop. I'll re I could ramble, but Awesome. Well, I wanted to say your experience is, the there's a story behind your story. The story behind your story is that Event, yeah, calculators were a controversy when they first became available. but now calculators are integrated into the curriculum. Right. Right. So so I think about this because the PhD again is in education policy. Right. Right. And policy is pedagogy or

pedagogy. depending on how you wanna right. But anyway, eventually eventually, it's inevitable generative AI will have to be integrated into the current curriculum. Yeah. and there were districts that banned graphing calculators. Yeah. That's right. There were schools and districts that banned graphing calculators just the way generative AI is now banned in some districts. Yeah. It will pass. Hopefully, it will

pass. Yeah. No. I I could see that. And I think that there's I think that one of the things that I learned when I was doing tech policy. And for those in the outside of the Beltway, when we say policy, we're kind of mean lobbying, kind of. Okay. Don't wait. Would you agree with that, Adam? Kind of. Yeah. Well, there's different flavors in the DMV area, but I get it

when you say policy and lobbying. your your your working to influence statute and, administrative regulations and funding and granting from all of the science foundations, etcetera. Yeah. Right. It's kind of

it's not exactly the same thing, but it's in that same orbit. Right? Okay. Though, I I would say, like, I mean, I certainly the the the the food options in the lobbying, world are much better than than anywhere else I've ever worked. But, but that's a story for another show. but yeah. So but, I mean, this is kind of like just something that you only really see around largely around DC, probably other state capitals

and stuff like that. But when we I the other thing I wanna point out is I mentioned the WY Files, the WY Files is a funny YouTube channel. And you have to check it. It's hilarious. Like, they they they the hecklefish is kind of this talking goldfish. which I realize, as I say it out loud, you have to see it. You have to see it. And and there's a pin foil hat on the on on the on on the on the on the fishbowl.

Right. It just it's just funny. And, like, he the fact that he talks is act he's he I guess, 8, the the host is from New York or whatever, but, like, the way that the fish talks hounds exactly like my relatives who who lived in Queens, New York sounded. So he's like -- I I had meetings. I I jumped in late, because I had a meeting run long, and I'm wearing my consulting costing. This is what

I said. But underneath this, there is a crab cat, a fear of the crab cat t shirt, with a diagram of a crab cat. That is a WAV file's merch. sure. And you can check it out on, on YouTube. And it's kind of a play on the X Files. They do fringey stuff. And what's really interesting about it, though, is he's the the host list. He does his research. and he starts with a bunch of things about some conspiracy theory type

thing. And he kind of plays through the conspiracy theory from the conspiracy theorists standpoint. And he doesn't mention -- -- response. He doesn't actually respond yet. -- at the end, he does, like, a debunk. And what's interesting about it is sometimes it's just that. But then other times, he'll get to the end of it and he'll say, you know, I can debug all of this. but I get to this piece and I can't. And and then the other times, he'll

get to that. And he'll say, and it changed my mind. I don't now I don't know. And it's he's a first off, he's an interesting character. Are you watching it? No. But -- Oh. -- I I I am googling for while you're telling me about it. This is -- Oh, okay. Yeah. This is great. I also found a data visualization product called WY Files. Oh, interesting. Yeah. So check that out. Now we gotta check that out too. Yeah. So I always love hecklefish. Hecklefish is awesome. Yes.

Free free shout out there to Wifi. It's not a sponsor, but maybe one day we'll be. I'm gonna throw this in because we keep forgetting it. where can people learn more about you, Adam, and work that you do? So LinkedIn and Twitter are my most active social media platforms. Please connect with me if you have any inter yeah. I, and the listeners love connecting with new people. the, book is available, how to become a data scientist. is available on Amazon. It barnes and Noble pretty much

wherever books are sold. There's an ebook, a hardcover, a paperback, Nice job. And then there's another book coming out in September, which I encourage folks to pre order. You can get that on Amazon. It's called Confident Data Science. Nice. Okay. Adam Ross Nelson, and Covenant Data Science is a tech book. It's -- Cool. -- op code. But the interesting

thing about that book, it, you know what? If you'll have me, Well, I should come back and talk about that book too once it comes out because we should set that up. That would be awesome. It it covers the history of the field. the philosophy of of the field, the there's a I I hit ethics really hard in that book. Ice. And I hit culture really hard in that book. so even though it's a technical book, I hit those non tech aspects really hard, because I don't know any other

tech book that does that. You can't separate them. I mean, you can't. If

you're talking to an LLM, right? And and I see You know, I I keep up with a I keep up with some of this stuff around culture, especially. And I see the the first thing I saw was the thing about bias. And I can't remember that guy's name. I had to I I gifted Frank, a a subscription to his sub stack. And he wrote about that and how it slants. It's it's not skewed. You know, it's not when but he's he's doing a vertical chart on it. He definitely sees a slant in

there. And the way he approached it, which I thought was fair, is that this is a reflection of us. So when people talk, I was here 20 years ago when the internet came out, oh, there's all of this bad stuff on the internet. Right. And I'm like, It's us. People, you're looking at us. Get yourself. Wow. I don't know. Reminds me of that South Park episode. The inter the internet didn't invent. Go ahead. Park episode? No. Where they they they they see the architect of

Walmart. You ever see that one? I don't know this one. Oh, it's a it's a it's a play on the and basically Walmart. There you go. Air met. Yeah. And it was, and, the Walmart, becomes like this self sentient, like, things takes over all the town and stuff like that. And then the kids go to the back. Sorry, spoiler alert, but the episode's been out 10 years. So -- Yeah. -- just for the listeners, And then the the the kids see the kids talk to the Colonel Sanders looking

architect, like, from the matrix. And, and he's like, well, here's the secret if you're ready and, like, they open the door and it's a mirror. Right? It's a reflection of themselves. That's good. the kids the kids look each other and say that. And then, like, the architect jumps in a typical song. See, don't you get the symbolism? Don't you get symbolism? It's like, yeah, we do shut up. Like, it was it that was

a very soft park moment. But it it's that. And that's good. you you feed in how many, you know, how many how many tons of data and text did chat GPT read to be trained? It was It's seeing us. Right. It's spitting back at us us. Thanks for putting it that way. You know, yes, we're biased. We're we're never gonna be neutral. We're never gonna it's not a 0 sum game. We're never gonna go down the middle. And if you'd had done it a 100 years ago, it would have been slanted the other

way. because we were there a 100 years ago. different other ways. Right? Like, there are things that Frank, I lost your audio. Oh, no. Maybe it's me. I still have it. Yep. It is me. Okay. And I hate this. No. It's a it's an interesting point because, you know, and and standards change change the team's fault. This is It's not even it's an Andy fault. Every and it it's not because I it happened to me

on Zoom earlier. Okay. Now Honey, we hear you. Okay. No. It's interesting because if you look at, like, movies, like, a Mel Brooks movie, a mailbox movie could not be made today. Right? Alright. I didn't hear you. Oh, now I can. Okay. Can you hear me? Yeah. I ended up getting 3 things in my speakers, and they're all the same. They're the same headphone brand. And I'm like, what are you doing? And it does it in zoom, It's not just teams. Oh, okay. So we're not

fashion teams? No. No. I mean, it's just, you know, standards change over time. what what constitutes bias or what constitutes the idea of neutral, I think, is is is a moving target. Absolutely. That's a great point. It's, I was gonna make a analogy about Mel Brooks movies, but, you know, like, I think we lost Andy's audio now. No. Am I back? You're back. I was laughing, but but yeah. So so here's a question, Adam. It kinda dovetails nice

to our final question. Is there gonna be an audible book audio book version of this? You know, I I, for those who know a little bit about Book Publishing, there's an ESPN for the audio version. Mhmm. So once we get that recorded, we'll we'll, have So you are gonna do it. Cool. Yeah. Are you gonna read it? Yeah. I I I believe so. I just think that's the way to go. I mean -- I agree. Yeah. I I audio books read by the

authors are just incredible. Although, There are some really good audiobooks out, some new Star Trek that that are in the Bacard, you sub universe of Star Trek, not read by the author. Incredible. oh, and I know you're looking for recommendations, book recommendations. That's probably your next question. Yeah. That's right. Yeah. So I wasn't planning. I did my homework, thought ahead. I wasn't planning on recommending those Star Trek books, but they are absolutely incredible prequels and pickles

and post c post quals? What's the, sequels? SQL. There you go. Yeah. To the Picart show. But -- Okay. Oh, wait. I also wanna recommend one of the shows that this this show today's show has really reminded me of is halt and Catch fire. Do you know it? I do. I was a TV show, wasn't it? Yep. Yeah. And on Audible is, follow-up to halt and Catchfire.

worth your time. Okay. And then my classic book recommendations, I know these are unaudible, are weapons of math destruction, Kathy O'Neil, algorithms of pre oppression, Sophia Noble, and superintelligence by Nick Bostrom. All three of those are also on audible. And there, as far as I'm concerned, any reference list and data science that doesn't include those three books is incomplete. Nice.

Awesome. I love that dovetailing into you now that you're writing about ethics. I I'm really I'm really curious to see, where you come how how you approach Escal AI because having this other background that also involves ethics, the law, Sure. Yeah. I I think you have something to add to that conversation. There may be other stuff. I write extensively about that background in the book as well. Well, not extensively, but I I make sure I mention that because you're right.

There's a connection there. we we could do a whole show on ethics, maybe. That'd be awesome. That'd be awesome. Actually, where

I really cut my teeth on ethics is is in consulting. Because for those of you who've done consulting work, for the listeners, you know you have these conflicted interests. You have your company you have your client, you have your interests, you get pinched in a way. and, anyway, so I I've got I think some really good maybe that's

another book I should put on my to do list. I think I've got some really good advice for consultants who who want to engage specifically proactively avoid ethical dilemma in the consulting setting. So I'll just leave the teaser there. Oh, I like it. Yeah. I do too. Yeah. I'd I'd read that book. I am a consultant. So, we get totally, totally get that. And -- However, you're self employed, so you do have, like, one less character in that. I

do. Sure. Yep. -- thing. I mean, it's still I'm sure there's still a dilemma because And it's you know, I it you know, and there's so there's so many as I kinda think about what you could write about, Adam. there are so many places where you can be pinched. There's not it's not just it's not just customer and the consultant. It can be the the consulting company and the consultant. there can be personal things that come into play in you know,

conflicts of interest to lower. Mhmm. So, yeah, it's it's a it's a difficult thing, and I I Again, love to write that book as soon as you're done with this one. Okay? Yeah. And I'll definitely I'll definitely provide you a quote for that. So with that, we'll let the nice we'll let Bailey finish the show. Thanks for listening to data driven. Have you checked out data driven magazine yet? We are looking for writers for the autumn 2020 3 issue. Please check out data driven

magazine.com for more information. Thanks for listening and be sure to rate and review a on whatever podcasting app you are listening to us on. You know, and there's so there's so many as I kinda Think about what you could write about, Adam. There are so many places where you can be pinched. There's not it's not just It's not just customer and the consultant. It can be the the consulting company and the consultant. there can be personal things that come into play

and, you know, conflicts of interest go lower. Mhmm. So, yeah, it's It's a it's a difficult, thing. And I'd I'd, again, love to write that book as soon as you're done with this one. Okay? Alright. And I'll definitely I'll definitely provide you a quote for that. So with that, we'll let the nice we'll let Bailey, finish the show.