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
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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.