Are you interested in data science, but you're not quite working in it yet?
In software, getting that very first job can truly be the hardest one to land.
On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice
for getting your first data job. This is Talk Python to Me, episode 455, recorded January 18th,
2024.
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Hey folks, before we jump in and talk about data science jobs and careers, I want to tell you
really quickly about some awesome news. Back in February, I gave the keynote at PyCon Philippines.
It was entitled The State of Python in 2024. Well, that is now out on YouTube. The team at
PyCon Philippines did a great job. The video came out great. If you want to check out The
State of Python in 2024, according to me, just click on the link in the show notes to watch
it over on YouTube. Now let's talk to Avery. Avery, welcome to Talk Python to Me.
Thanks so much. I'm so excited to be here and be part of the show.
I'm excited to have you here as well. You know, one of the things that people reach out to me often
is how do you get into data science? How do you get into programming? How do you get into Python?
You know, I've been trying, or maybe they got a degree or they took some training program,
bootcamp or something. And going from zero to one, I think is the biggest career step you have to make.
That next job and the one after that, it only gets to be smaller steps, not bigger steps.
And it's really tough because that first big step, you're brand new at it. You have no experience,
right? It's your first data science job or your first programming job. And so hopefully we can
give some folks out there a little bit of a hand up to help them make that jump.
Yeah, totally. I like to show this graphic that says, it's a circle and it's a circle of text. And it says,
I can't get a job because I don't have experience because, and then it restarts,
I can't get a job. And that's the tricky part. It's like, how do you get a data science job when
you have no data science experience? Because to get data science experience, that seems like you have
to have a job as the prerequisite and vice versa. So it is very tricky. So happy to chime in on that
today.
The industry can take it too far. They can take it way too far. So a few years ago, there was a
really funny tweet that went around back when they call them tweets. I don't know what they're called
anymore. Sebastian Ramirez, the guy who created FastAPI, saw a job posting. When FastAPI was like a
year and a half old, it said, you must have four years of experience with FastAPI to apply. He said,
hey, look, I'm the creator of FastAPI and I'm unqualified for this job. What kind of world are
we living in?
Yeah. I don't want to live in that world, but that's unfortunately where we're at. That's so tough.
And it's hilarious. These job descriptions are getting out of hand. That's for sure.
Yeah. Well, with AI, it's probably not going to get better.
We could talk about that more later. But before we get into that, let's just jump in with a little
bit of background on you before we get to the topic. Tell us a bit about yourself. What do you do?
How'd you get into Python? Things like that.
Yeah, absolutely. So I'm currently a data science consultant and also a data science instructor.
I run some online programs where I teach people to become data analysts mostly is what I'm focused on.
But I also have this practice where I help companies solve data problems with different techniques.
I started actually by studying chemical engineering in college in my undergraduate degree.
And about a semester in, I realized, crap, I hate this. This is not for me.
But I was a little on a little of a tough. Yeah. Do you agree? Have you felt something similar?
I did a semester of chemical engineering as well. I thought, I love chemistry. I love math. Put them
together. Somehow they don't go together. It's like ice cream and eggs or something. No,
they don't go together for me at least.
Yeah. It wasn't good for me either. I was just like, oh man, I'm actually not interested
in refineries or like manufacturing. But I, like you, liked chemistry. I liked math. I thought this
is perfect. But I quickly realized, oh man, I really liked this whole programming part that I get to do
in MATLAB at the time when I was an undergrad. And I was on a time crunch to get through college kind of
quickly through eight semesters. And the other issue I had was I didn't know what to do instead. It was
like, I don't really want to study computer science. Part of the reason why is they kind of
had this weed out course at the beginning, which you had to build Excel from scratch, basically like
some sort of a spreadsheeting tool. And I was like, why would I rebuild something that already exists
that I don't even like using in the first place? I wasn't really into it. So I didn't, I didn't know
what to do. And luckily I was working as a lab technician at this company, the really cool company
that makes the sensors that basically have the ability to smell. So they can sniff what's in
the air and it has applications for finding drugs or bombs and airports and stuff like that.
And there was a data scientist on staff and that data scientist was awesome. He was like showing me
all these cool algorithms he was writing for these sensors. And then one day he got up and left and he
left the company and we tried to hire another data scientist for like six months, but they were really
expensive. We were a small company and none of them really wanted to move to Utah where I lived in
Salt Lake City. And so we couldn't, we couldn't really find someone that would be able to do it.
And so finally I was like, well, I really liked this programming stuff. And I, you know, the data
scientist showed me a thing or two, maybe I could take a stab at this. And I started, I wrote like my
first machine learning algorithm and I was like, oh my gosh, I'm addicted to this. And then I never
looked back and had been data science since basically.
What a great story. Yeah. I think, I think a lot of people fall into programming that way. And for some reason,
not unexpectedly, but for some reason, a lot of people fall into Python that way as well. They're
like, you know, I have a job and I got this thing I got to do. I just need a little bit more than maybe
like an Excel spreadsheet or something and put it together. And you're like, actually, this is cool.
After a while, like, this is cooler than what I've been doing, or maybe I'll make it a good part of what
I do. Right.
Yeah. A hundred percent. Even just making, it was in MATLAB, which is basically engineers version
of Python or college version of Python 10 years ago. Right. And I made like tic-tac-toe and I remember
playing tic-tac-toe against the computer. I think that's what it was. Or maybe it was,
maybe it was Hangman. I can't remember. But I remember like the idea of like being able to play,
to program games and play against the computer. And I built it. I was like, this is the coolest thing
ever. I got to, I got to do more of this.
Absolutely. You know, I think I've done some MATLAB too, when I was younger and it's not that
different from Python, but it's, I think one of the big differences other than it just being like
embedded in a big expensive app is it's not a general purpose programming language, right? You
wouldn't go, you know, that was fun, but let me go build this website in MATLAB or let me create
Airbnb and MATLAB or, you know, like there's, you just don't want to sort, Azure has this like
self-prescribed limit to what you can do with it.
That's one of the coolest parts about Python is it's really a Swiss army knife and you can pretty
much do, I don't want to say anything, but pretty close to anything in Python, which makes it really
neat. And obviously one of the huge limitations of MATLAB is one, it costs thousands of dollars,
but two, you're right. It's not going to do cybersecurity for you. It's not going to build
websites, but the syntax at the end of the day was, was really quick. It was, it was easy for me to
transition from MATLAB to Python because the syntax isn't all that different.
No, it's not all that different. More math focused, but pretty similar. So I think maybe that's a good
place to start discussing and exploring the topic of your first data science job. And
wouldn't necessarily plan on starting here, but let's, let's start with before you even necessarily know
programming language, right? Maybe you've dabbled in MATLAB or you've dabbled in Excel or even dabbled in,
I don't know, JavaScript or something. This thing we've been talking about with MATLAB and it applies to other areas as well,
like through programming languages per se, like Julia or something like that, is how,
if you invest your time into learning one of these things really well, like how broadly industry-wide
of a skill, high demand skill is that going to be, right? If you learn MATLAB, you put yourself in a box,
you learn a more general programming language, you kind of have more options afterwards, right?
Yeah, totally. I think like the more broad of a language you learn, the more useful you are to,
to more industries in general. But I might take that even a step further and just say, you know,
learning MATLAB, not a whole lot of companies use MATLAB, but just like landing your first data job,
going from zero to one is the hardest, learning your first language, zero to one is the hardest as well.
And then once you have that first language, the next language becomes so much easier. So
one of the first things I learned was MATLAB. And then I moved to Python and that was easier. And then
I learned SQL and then I learned R and then I learned JavaScript. And every time I added like a new tool
to my toolkit, it was quite, not almost, it was easy, but it got easier with each one. I think that's true
with foreign languages as well. Once you learn one foreign language, then the third and the fourth become
quite easy. At least that's, that's what I heard. I speak kind of through two and a half languages,
but like I, there's people who speak like seven and they always say like the sixth and the seventh
become easier.
Yeah. You wonder how could you probably, because learning the first one is so hard,
first foreign language. So you're like, well, how could you possibly take that on for this many
languages? And it's that it's not the same challenge each time, right?
Yeah, exactly.
Yeah. So I think when people are considering getting into data science, they really want to consider
what language they choose and where they go. Like you're coming out of a
college program. You might feel like MATLAB or something like that's real popular. And yet
that's because it's popular amongst professors who forced their students to do it. That doesn't
necessarily mean that's the world, the broad worldview. What do you think about R? You know, both.
I like R. I'm not, I sometimes troll R on LinkedIn. So I guess that's another thing I should say is I post
a lot on LinkedIn, kind of a LinkedIn guy. And so a lot of the times, honestly, just for jokes and kicks
and giggles, I'll kind of roast R on LinkedIn just to get the trolls angry in the comments.
I've invented it. It's quite fun. It's quite a fun experience, but I'm not that big of a hater. I
think that's really interesting about R versus Python is obviously a big debate in the data
science community is R is kind of that does one thing really well. And it's getting a little less
of that as like more packages and libraries are added to R, but R does the statistics and machine
learning very well. But obviously I don't think once again, I don't know any websites, any like
super functioning websites that are built on R. I don't know any cybersecurity that's really done
done, done VR. So I think R does what it does well. The syntax sometimes is a lot easier for
people to go from Excel, which a lot of people are more familiar with in the finance or banking world,
for example. The syntax in R is a little bit more similar to those Excel formulas than it is to Python.
So I think sometimes people have a little bit more success just because, oh, this kind of feels like
our formulas are sorry. This feels like Excel formulas. And so people really get there. I think what
you're kind of alluding to is if you're going to learn one skill, you might as well learn the one
skill that's applicable to the most, the widest net, right? And so that way you're fishing in the
biggest lake you possibly could versus in a smaller pond of R. I think that's worth looking at. And one
of the things I actually really enjoy doing, because you know, you mentioned, oh, you might think MATLAB
is popular because that's what the professors taught you. And there's actually not a whole lot of
data out there about, well, what should you learn? So I don't know if you know who Luke Bruce is. He's a data
analyst YouTuber. I was going to say YouTuber on YouTube, but that's kind of redundant on YouTube. And
one of the things he's done is he's actually built this tool where he's web scraping thousands of jobs,
different data jobs every week, and then displaying and analyzing the skills required for those jobs.
So it's actually like a data driven way of saying, if you want to be a data scientist, what skills should you
actually be focusing on as you go, as opposed to just listening to what a professor will say,
or what a LinkedIn influencer will say, or what your bootcamp will say. Like actually getting some
data on, I think is pretty neat. That is super cool. And I'm not familiar with Luke. So we're going to dig
him up and put him in the show notes for later so people can check that out. For sure. Do you remember
any of the trends you've recently talked about? It's datanerd.tech, I think is the website there.
I look at it mostly for data analysts because that's who I work with the most. So I know the
data analyst data very well. SQL is number one at 50%. I think Python is number two at like 30%.
I think Python might've jumped it. Well, this is for all data positions right here. So the job title,
you can choose. So which one do you think I should pick here? Data?
Maybe data scientist. Data scientist. Yeah. Right.
What's that? Yeah, you're right. Wow.
Whoa, Python 69%. Look at that. That's huge. So like, that's even, that's even what? 20% more than SQL,
which a lot of people are like, if you were going to be a data scientist, you have to know SQL.
Yeah. If you look at the job descriptions, Python's mentioned a lot more. So if you're going to learn,
if you're brand new and you're going to learn one, you might as well start with Python. Because that's
probably the most in demand skill that there is right now for a data scientist.
Yeah. And it's pretty easy, right? It's not like, well, why don't you just learn C++ for
embedded devices? You're like, you know what? Maybe I'll pick something else to start with. Right. But
you know, Python's pretty easy. I agree with you. I think Python's great. I actually think,
I think SQL is probably easier to learn if I'm being honest, because really, especially for like
data science stuff, there's only about like 20 commands that you need to know in SQL. But it's,
once again, SQL's a lot more, there's no websites built on SQL. I'll tell you that much. So
it's a lot more limited on what it can do.
It's a skill, but not the language. It's not enough on its own, generally. I mean,
you can do reports and quite a bit with it. But you know, it's like, when you see these programming
popularity, like what's the most popular language? Oh, look, CSS is the third most popular. That's not
a language. That's a thing that you use with other languages, right? Like use it with all the other
languages. That's why it's high up. But that doesn't mean it's high in demand. Exactly. It's just like
table stakes, you know? Yeah.
So you kind of got to distinguish table stakes from like picking an area, I think.
That's totally true. And really, I think Pythonistas could make the argument that there's
really nothing in SQL that you couldn't do in Python. That's a little somewhat true,
true depending on data size and stuff like that. But regardless, there is ways that you can do
most of the SQL commands in Python one way or another. Yeah. Yeah.
It could be when I first became a data scientist, I didn't even know SQL and I was doing SQL commands
or I was doing the aggregations or the where functions or the window functions using Python.
So you definitely can. As long as your data is not like super big, then you'll totally be fine.
Right. Like some kind of generator or even slices or yeah, things like that, right? List comprehensions,
set comprehensions, all that kind of stuff. Kind of like, gosh, I really wish, a little bit of a
sidebar, but I wish like list comprehensions and all those things had just a few more SQL features,
right? Like in a list comprehension, I say, give me this thing, maybe give me this property of this class
modified, like give me the user's name, uppercase. Right. So that's like select. And then for thing
in collection, that's like from table or whatever. Right. And then you have the where clause with the
if statement, but boy, wouldn't it be cool to have like a sort also in there and other things like that,
you know? Oh, well, totally. It's so close. The cool thing is, is if you want that sort,
it's what one extra line. Like it's, it's not, it's not too bad. So it, Python, I mean,
I don't want to say this necessarily to hate all, to make all the data scientists and SQL lovers
mad, but, but really Python can do a lot of the things that SQL that's for sure.
Yeah, that's for sure.
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Probably the biggest, it's a bit of a diversion, but the biggest similarity to that I've seen in
the languages is C#'s link where they actually have almost all the query operators, including
joins and stuff like that built into the programming language. I'd love to see more of that kind of
inspiration into Python, but you know, that's all right. It's still really good. I've got a lot of
cool SQL-like features, but you're right. Once you are no longer working with data and memory,
or you want indexes, right? Like this concept of indexes is not sufficiently well understood. I think
every time I hit a website that takes five seconds to load, I'm like, somebody is not doing all the
things they should be doing. I just know it. That's totally true. What about SQL? You know,
let's talk about that for a bit, right? The SQL, the query language or databases and other things,
there's ways to SQL query, not just relational databases. But you said you got away with not
quite learning that, but do you think if you could start over, maybe making an effort to learn that
would be really valuable? Like how, how important is this a thing in the beginning of your career?
The interesting thing, you know, about landing a data job is your skills only plays, I say,
a third of the role. Your portfolio or the way that you portray your skills and your network,
I think are the other two thirds and they're actually more important than your skills. And that's
kind of how I got away with not knowing SQL and not even being, to be honest, that good at
Python at the time was because I used my network to be in the situation to get my lab technician job
in the first place. And then once again, I use that same network, in this case, my coworkers,
to land that first data scientist position after we couldn't hire anyone. And if I would have been
applying externally for that role, chances are I wouldn't have gotten that role. I probably didn't
know enough at the time to land that type of a role, but because they knew I was hardworking,
they knew I wasn't like a total idiot and I really liked to learn. They took that chance on me. It
paid off really well for them because at the time I was still in college. And so I wasn't getting paid
that much. And I was getting, I was not getting paid like a data scientist, but I was getting results like
a data scientist for them. So I think it was, it paid off for both of us. But I think if that was an
external job and I applied for it, I probably didn't have enough skills for it. So I definitely think
learning SQL, if you want to land data science job, isn't a bad place to start, especially because,
like I said, there, I mean, any programming language, I like to think of like the iceberg,
kind of like the Titanic, right? There's the parts that you see, and then there's the parts that you,
that you don't even know that you, that are there. And, and really you could spend the rest of your life
trying to master SQL or the rest of your life trying to learn Python. But the cool thing is,
is a lot of the time you only need that top little bit that's sitting at the,
the top of the surface of the water to actually get stuff done. And so for SQL, I think that's like
20 commands. And I think you could learn it honestly in like a month, you could learn those,
those 20 commands pretty easily, but it worked out for me. And I, I didn't have to use it that much
at the time until I was probably about almost three years into my job. And I actually had switched jobs
to a bigger company. The other thing that I was working for a smaller company where we didn't have a ton
of data. So we could use CSVs kind of as our, our database, which is not great practice. But when I,
when I eventually became a data scientist at Exxon mobile, I was going to say they didn't use Excel as
a database, but they still did. But the point is they had much larger SQL databases with hundreds of
thousands, actually millions of rows of data that I had to query.
Yeah. Then you gotta be really, you need to understand it at a much deeper level. You're
like, if you do a query like this, it's going to be super slow. But if you do it like that,
it can use the composite index for the sort and then blah, blah, blah, blah. All right. Then
you're getting to the bottom of the, the iceberg in SQL, or maybe not the bottom,
maybe like the middle chunk under the water, but there's so much to learn for both of them.
Amir at the audience asks, you know, like when you talk data job, like what kind of jobs are out
there? Right. So we talked to both about how we did chemical engineering and then we saw like
chemical factories, like, yeah, I don't really want to work here anymore. I'm out.
So thinking about like, well, what are the kinds of jobs you do? I think that's really important because
it's easy to get focused in on like the FANG companies. Like I want to work for like some super
big tech company. I want to move to San Francisco and like that, that, that, right. Like there's not just
plenty of other jobs, but the opportunities, just like you described, and as well as
like my first job, I worked at a company that had like eight people and it was awesome. Right.
They didn't expect me to be, you know, running Kubernetes clusters and doing all sorts of great.
They're just like, I need you to make this thing happen. Can you do like, I'm pretty new,
but that thing I can make that happen. Like, let's go. Right. And I feel like the, the possibilities
to get in, especially with these maybe more niche type of industries and companies might even be easier
for a first job. People seem to be really obsessed with, with the FANG. And I don't know if that's
like a societal thing, or if it's just, those are the companies that we use a lot. And so we're
excited about them, but yeah, there's so many more data jobs outside of FANG than there are inside of
FANG, even though there's, there's quite a bit inside of FANG. And oftentimes those roles can be
much more interesting and you can do a lot bigger of an impact. When, when I was working at the small
company, VaporSense, I like, I had so much power. I didn't even realize it. I had such a big effect on the
company. I was presenting to, you know, Fortune 500 companies and what I did really made a difference.
And when it came to the point where ExxonMobil offered me to go be a data scientist for Exxon,
I said, Oh, I want to go work for the big company with the nice desk and the nice laptop and, you know,
try something new. And when I got there, I really, I had some pretty cool opportunities when I was at
ExxonMobil, but ultimately I left pretty shortly after two years of being there because I just felt like a
cog in the machine and I didn't feel like I was actually making a difference. And that was really
important to my work satisfaction of like, is what I'm doing being used? Is it being used to better the
world? Do I feel valued? And the answer was kind of no for me when I was there. So there's definitely
a trade-off between the small companies and the big companies, but also to go back to your original
question, there's so many freaking roles in the data world that you're not even thinking of that. Like,
I'm not even thinking of, I saw a new one the other day when I was helping one of my students.
It was like, it wasn't data janitor, but it was something like that where I was like, I don't
even know what that role is, but there's, there's so many roles. When I was, when I was a data scientist,
VaporSense, the small company, my actual title was junior chemometrician, which basically means
you're doing data science with chemistry. When I was at ExxonMobil, when I was first there,
I was doing data science, but my actual title was optimization engineer. And so there's so many
titles that we don't even think to search of, or even to look up, but those are all data science
roles. I was doing machine learning every day in both those roles. And you would maybe never guess
from those titles. Yeah. You would never guess. No, that's awesome. What machine learning libraries,
frameworks were you using? At VaporSense, once again, because it's a smaller company,
I had a lot more say in what I was doing. We were building a bunch of machine, we were building
classification models to basically to take the data from our sensors and sniff if something was in the
air. Sometimes that was a yes, no, like, oh yes, there is ammonia in the semiconductor factory and
that's bad. So that's a yes classification kind of binary, right? Other times it was, what drug is this?
Is this meth or is this heroin? One of the use cases we had was, this is binary once again, but is this
recreational marijuana or medicinal marijuana? And can we tell the difference between, between those?
So we are usually using classification models, usually built in scikit-learn in Python, the majority of the
time there. When I was at Exxon, we had a lot less say, like the data scientists had a lot less say in
the decision making process. We were doing a lot of multivariate linear regression with a lot of crazy
hacks and transformations kind of in the meantime for one of my positions there. And then the other time,
the other position I did there, we were doing a lot of auto ML using PyCaret and letting it kind of
decide what type of models to do. So. Okay. The unsupervised learning type stuff, huh?
It was awesome. It was really fun to, to, I love PyCaret because it's like, okay, go make 25 models and
tell me which one's the best. It's like, takes, makes my job easy, I guess.
We're going to be creative with sheer numbers. That's how we're going to come up with a solution.
Got it. Exactly.
Well, Diego is asking like, what are some of the common stats methods as in mathematical type stuff
you would use? So one of the things I know that some people getting into programming think is you've
got to be really good at math to be a programmer. I think you've got to be really good at logical
thinking, but you need to almost zero math be like a web developer. You know, we're talking percents
for CSS, incrementing numbers from one to two to two to three for IDs and stuff like that. But for
data science, maybe there's a little bit more like, where do you see that kind of background?
I like what you said, you have to think logically, but maybe the math isn't as important. And I think
it's actually somewhat similar in data science. I will say you probably need a little bit more math
than a web developer, but I think it's a lot less than most people think. And it's probably less
about being able to do the math and maybe more about understanding the mathematical concepts.
And what I mean by that is a lot of, a lot of, so I also have a master's degree in data analytics.
A lot of master's degrees in data science and data analytics will say you need calculus and linear
algebra as kind of a background for your math. And that kind of stops people. I don't want to do any
calculus. I don't want to do any linear algebra. And while both those concepts do exist in data
science principles, the majority of the time, the computer, Python is doing the math. You just have
to be able to interpret the results of the math and kind of know what different directions, like this is
going down, an optimization problem, you know, okay, that's the derivative, you know, getting closer to
zero. Like it's really less about knowing how to do the math by hand and more just understanding what the
math the computer is actually doing. So I think it's actually a lot easier than most people say.
That being said, knowing how to do a derivative or taking integral, those concepts, I think is
probably underlying pretty important. But other than that, like a lot of the times I'm doing linear
regression because it's, it's awesome. It gets the job done. A lot of the time I'm doing hypothesis
testing and statistics, which you have to look like at a P score, nothing all that crazy. At Exxon,
I had to do a lot of linear programming, but that's honestly, that's like the exception versus the rule.
There's not a whole lot of linear programming for most data science, most data scientists. So
I really don't think the math is, is all that hard. Now, of course, that's coming from someone who
got a chemical engineering degree, who had to take all the calculus, all the linear algebra. So I did go
through those courses. I haven't really done it from scratch from like a lot of my students are teachers,
for example, who never took those courses in college. So I can't speak from that perspective,
but a lot of my students are able to figure it out at the end of the day and transfer. So it happens.
Yeah, yeah, for sure. I think there, you make a good point. I think it's about knowing,
okay, this formula or this algorithm or this test means this thing. It applies in this situation.
It doesn't apply in that situation. Here's what you're trying to get from it, right? Like,
I know I need to do a fast Fourier transform. So, and this is what it tells me when I get out the other
side. But do I need to be able to sit down and recreate the integral and the calculus behind it
and do that on like a home, like as a homework example, like, give me a function and I'll do the
Fourier transform and I'll actually do the symbolic integration. Like, no, you probably don't need that,
right? But you need to know, I do the Fourier transform in this situation and this is why. And then I just say,
call the function, do it, right? And interpret the results. Really, that's what being a data
scientist is all about is, yeah, what does the business use case, what's the desired business
use case? How do I relate that use case to the data? What technique can I use to get the outcome
that I need? Computer, go do it. Interpret results, present to stakeholders. That's a data scientist,
right? I think one of the challenges with that is going to be, not that it's not good, but I think
it's going to be challenging because how do you learn when to use a certain statistical test or when to do
some kind of funky transformation, like a Fourier transform without more traditional mathematical
backgrounds? And all the academics will not just go, oh, we're just going to give you like
five minute overview and they'll help you understand. They're like, nope, we're going to start with this
axiom or this theorem from differential equations. I'm going to work up. You're like, no, no, no, no,
no, I don't need that. I don't, I'm not on a four-year plan. I'm on a four-week plan. How do I,
how do I get value from a couple of the mathematical things without being sucked into like, yeah, now I'm in
differential equations at Harvard online and I don't understand how I got there.
It's such a big problem and I'm so glad you brought this up and I'll be vulnerable because yeah, I felt
the same, the same way. And I was like, there has to be a better way. And so about, what was it? Three
years ago now, two and a half years ago, three years ago, I said, oh my gosh, I'm going to solve this
problem and I'm going to start my own data science bootcamp. And so I spent about six months making the
curriculum, making all the videos. I opened it up. I got some students in there and I ran it for about
six months and I looked at the results and man, we weren't getting anyone into data science jobs.
And I thought, ah, what the heck am I doing wrong? I had this brilliant idea of like, we're going to be
less theory, more project, more hands-on. And I realized, man, the truth is people just learn better
at work. That's where you learn that whole technique that you just like, how does someone learn that?
The answer is by getting experience and learning it at work. And when I looked back and I said, okay,
well, we have had students get jobs. What jobs did they get? And it turns out most of them were
getting like business intelligence, intelligence engineer jobs or data analysts or financial
analyst jobs that were a little bit below a data scientist job. And I realized, oh man,
if we can just help people go from zero to one and get their foot in the door, they can go from one to
five much quicker at work because work is just, I don't know, it's this magical place, right? Like,
like you said, they, whatever you were working at earlier, and they're like, hey, can you do this
Kubernetes thing? They just kind of throw you in the fire and you're like, figure it out. And that's
somehow you do, I don't know what it is about work, but you figure it out and that's where you learn.
So that's kind of what I've, why I changed my curriculum to be more focused on, you know, okay,
maybe people aren't going to become data scientists, but can we get them to zero to one quickly?
And then they can get paid to learn the rest of the data science stuff when they're actually in that
first position. How much do you know about what you actually want to do in the industry
before you've done it as well? Right? Like, you're like, oh, I thought everybody said machine
learning was awesome. And I've used chat CPT and I loved it, but it turns out actually like API is
better, but I've never had a chance to build an API. So until I started, I didn't even learn that one,
it was a thing to that. It was cool or vice versa, right? Whatever. But until you get kind of in,
you don't even know, like, actually this part is where I really am enjoying it. And so just getting that
first step, that's a big deal. A hundred percent. You don't know what you don't know until you know
it. That's why, I mean, really when it comes to, if we like, we go back to just SQL or just Python,
you could spend, I tell people this, if you tried to master Python before you applied to a job,
you'd be like 80 years old before you ever applied to a job. Same with SQL, same with machine learning.
The cool thing about data is we're never going to know it all. And so just learn the bare minimum to get
your foot in the door. And then you have this place where you're going to get paid to learn what
you want to learn. Eventually, if you learn, oh, I love APIs. I promise you that there's a company out
there that will hire you and you can learn APIs on the job. Like that's going to happen. But that first
step is no true. There's a company out there that it doesn't know it needs APIs, but you could help them.
And you know, they don't have huge expectations because this is the thing they just learned they needed.
Right. A hundred percent. Yeah. It's wild, right?
This portion of Talk Python to Me is brought to you by Posit, the makers of Shiny, formerly RStudio,
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Posit Connect: Let's talk about some career advice. I mean, I know you talked about being
connected on LinkedIn pretty well and certainly having some kind of social network is important. And they
maybe, it's not that you would call it not social, but a real world network of actual human beings that
you're, you know, physically know somehow. Posit Connect: What's that? I don't know what that is.
Posit Connect: I know. Like, we gave that up back in 2020, I thought.
Posit Connect: Yeah. Posit Connect: Anyway, like, there was some stat that I saw somewhere that,
you know, over half of the jobs are filled filled before even becomes a job posting, right? Maybe
some of the best ones is like, hey, who knows somebody who can do this? We need some, like your
data science example, data scientist example. They quit like, oh, we need somebody. Does anybody know
good data science? I don't want to just go put it out on the open job market and have to have a hundred
interviews and who knows what I'm going to get. Like, if you can recommend somebody, let's start there,
right? So being in that group to be recommended, it's important.
Posit Connect: It's the key. There was a really interesting survey done on LinkedIn and they said,
it was kind of, it was done by the same person and Jordan Nelson, by the way, he said, "How do you
approach getting a job?" And then the next day he said, "How did you get your last job?" And 80% of
people, they use what I call the spray and pray method, which basically means you go and you apply to as
many jobs as you possibly can and hope for the best. Cross your fingers. That was 80% of what
people were doing. And then on the next poll, the next day, it was a total, I think of what, 70% were
either headhunted, recruited or referred. And so it's like the Pareto principle here where, you know,
80% of the effort is only getting you 20% of the results. And really 20% of the effort gets 80% of the
results. So it's okay. We know networking and getting recruited is really important, but how do we do it?
It's easier said than done. And like you said- In the industry, how do I make friends who are,
right? It's like, well, my neighbors don't do it. So I guess I'm out.
That's the tricky thing is, is yeah, if you're not in the industry yet, how do you get recruited
into it or how do you know someone? And what I've come to learn is it actually doesn't even matter.
So like, for instance, let's take, let's take your neighbor, right? Your neighbor is probably not a data
scientist. Maybe you're lucky and they are, and they can refer you to a company. But what's really cool is
I've learned that companies really come to trust their employees and their employees'
recommendations. And so even if your neighbor, let's say is a web developer, or maybe even less
technical, let's just say your recruiter is in finance, right? And if there's an opening,
like a data science opening at that company, a lot of the times they will actually take their
employee referrals much more seriously than any sort of cold application that they get.
And so a lot of the times I've had students who just know someone that works at the company,
they saw a job opening pop up. They're quickly, they message their friends. Hey,
do you know a recruiter or a hiring manager? I could talk more about this role. Could you do
an internal referral for me? And they were able to land jobs that they probably wouldn't have.
No, they definitely wouldn't have without that internal referral. So it is tricky. It's the old
cliche. It's not, it's not what you know, it's who you know.
I think there's still plenty of ways, COVID notwithstanding. I think that these days,
there's plenty of ways to get those connections, right? But maybe people don't know, like meetup.com
is really good. If you live in a non-tiny city, there's many, many things going on that around data
science, around Python, around other data engineering, whatever, right? You could go to those things.
They're typically even free. Often they are free with food. They even feed you, right? And make connections,
or regional conferences or national conferences, right? Like we probably, many people have heard
of PyCon, right? There's US PyCon, there's EuroPython, and then there's, but that's, those are the ones
that are often talked about, but there's 10, 20 little smaller regional ones in the US and many more that
I'm not aware of throughout the world. Probably one of those within driving distance, right? That you could
go to make connections and just also kind of take the temperature of actually what, what you see on the
internet versus what you see and actually talking to real people. So I'd also say, just get out there.
A hundred percent. Those places have the people who probably want to hire you because they're local,
right? Which is one thing that's, that's trouble on LinkedIn. I'm, I'm big on networking on LinkedIn,
but a lot of the times you're going to be networking with people who in all likelihood might never have a
role that's even open to you. But the people that you're like, for instance, we have,
I'm in Utah and we have Silicon Slopes that has like a tech meetup. We have a local Python
meetup chapter. We have the big data and developers conference that that's free every year with tons
of food. And the people who go there are people from companies around there that have the openings
that you're trying to find. And they want to hire people like you who are in the area. So at least
you can maybe come to the office once a week or maybe once a month or whatever. Right. And so really,
like you said, going to those meetups, it's tough because networking is always difficult,
either online or in person, but at least in those situations, you know, Hey, these are people that
are tied to real companies that exist around me that do make data higher. So I have a chance.
Definitely a much higher chance than just shooting out a resume. All right. Well, let's see.
We talked about job hunting already. What about like applications and resumes? What are your thoughts on
that? I think once again, with the applications, the more targeted that you can make it, the better,
right? So if you can really hone in on, I really want this job, I'm going to cold message five people
at this company and see if I can get that internal referral one way or another, make a real connection
with them. I think that's really key. And then with resumes, resumes are more of an art than they are a
science. I feel like they are so difficult to figure out. And these ATSs that are trying to match you
and see if you're a good fit. I've tried a lot of them and a lot of them suck. Whoever's the data
scientist behind those, we need to have a conversation with them because it's, it's a little tricky
sometimes. But one of the coolest concepts I've been introduced to recently, and I have a whole
episode on my podcast about it is A, B testing your resume. And basically the idea is a resume's job
is just to get you a screener interview or like a beginner interview, basically. Right. That's all an
interview. Like no one's seeing a resume and then hiring you. They're always going to interview.
So if you think about it, a resume's job, the only job it has is to convince someone to get on the phone
and talk to you. And it's just a piece of paper. And guess what? You can put whatever you want on
that piece of paper. Now I'm not saying to lie, but I'm just saying you could theoretically make a
perfect resume for whatever job you're trying to go for and send it out there and see what happens.
Right. But I'm not saying to do that. I'm not saying to lie. My point in saying this is that the resume
is just to get you the interview. And if you're not getting interviews, something's probably wrong
with your resume. And so, you know, tweak something, apply to 10 more jobs, see what happens. Tweak
something, apply 10 more jobs, see what happens. Until you finally have the right combination, skills
of experiences of different keywords. Because a lot of the time you're just trying to beat the ATS. And
that's the sad part about it is it's like, how do I prove to this random computer algorithm that they should
talk to me on the phone? That's a hard game to beat. And there's a whole bunch of advice
from all these different people. What I've come to learn is it's different for every company. It's
different for every person. You kind of kind of a numbers game till you get lucky and you figure it
out. That's good advice. I guess two thoughts. One is I know that speaking specifically to anyone,
one. But in general, women wait until they match all the requirements of a position where a guy's like,
I know three of those things. I'm taking a flyer. I'm sending it. I would just like to encourage the
women out there to just send it as well. I 100% agree with that. And I think if you reach 60% of
the requirements, I think you have a chance. Like it's a lot of the times those are wish lists and not
actual requirements. And depending on, are you local to the area? Do you have a domain experience
in this company? Like there's lots of other factors. What about contributing to open source
or having GitHub repos that can be like projects that you can show off or what's your advice there?
I'm a huge proponent of projects in the portfolio. I think if you don't have experience with something,
you create your own by building a project. And if you can do that with open source,
I think you should totally do that because I've benefited so much from open source. I have not
given back as much as I should to open source development and projects. I definitely should do that.
But if you can find a project that you're passionate about that you can help with, I think you should
totally do that. Even if it's not open source and you're just building a project to showcase your skills,
I'm all about that. I think you can do projects that are super fun, maybe that are good for your
community or good for your life. I'm a huge fan of personal projects. I've put a Fitbit on
my dog before and looked at her steps. I've found the healthiest meal at McDonald's. I've looked at,
like visualized my weight over time and tried to create like different, like forecasting models and
stuff like that. There's so much data in our lives that you can use to make really cool projects.
Oh, absolutely. You talked about, okay, you get your first job and that's where you kind of really
learn. But if you don't have your first job, you can effectively simulate that. Say, I would have gone
on to a job and been given a project to analyze something. I'm just interested in this thing. I've got
two hours a day until I get a job that I can be inspired about this and just get going on it. Maybe
create a website and publish your results and it can draw more people in to actually see that, right?
And start to appreciate it. They could even ask like, all right, who's behind this cool project?
Maybe I want them to come work for me. Little did they know you're doing all this work because you got
some spare time and you're trying to build up your experience and a self-guided study, right?
Yeah. If you can build a cool project and flip the job hunt where you're not applying for jobs, but jobs
start to apply for you, you're in such a good position and doing really cool projects can help you get there.
Now it's hard to do cool projects. It's hard to publish projects, which is one of the things
that people really struggle with. For all you Python listeners out there, let me just tell you,
Streamlit is absolutely amazing because it makes the deployment process so easy. It's free. It's a
little tricky to deploy at first, but compared to what you used to have to do it back in the day,
I'm saying back in the day, like four years ago, basically. But it was really hard to deploy
something where you could send someone a URL. Hey, check out my web application, machine learning
application. Streamlit is such a cool app that makes it so easy and so intuitive to make these
cool little apps that you could just put on your resume, put on your portfolio, send to recruiters. I'm
such a fan of the Streamlit app. I love it.
Yeah, it's super cool. There's a couple of those and Streamlit is definitely one of the really nice
ones there. There's also some hosting behind Streamlit as well these days, right? You don't even
have to set up a server or anything and just create it and put it up there.
That's what I'm saying. Back in the day, I used Dash a lot and I'm still a big fan of Dash. Dash
is more customizable than Streamlit and can do quite a bit more, but it's a lot more work to deploy it.
It's more like programming.
Yeah, it is more programming.
Programming the UI rather than just the behind the scenes.
Yeah.
You have to do both and you have to know a little bit about systems and data engineering and stuff
like that versus Streamlit kind of takes that, abstracts that away. But yeah, back in the day,
I used to make Dash web applications and deploy them on Heroku back when they had a free tier of
hosting and they've taken that away. So I don't even know what the go-to free hosting platform is
nowadays. I just, I moved most of my things to Streamlit and it's so nice.
Yeah. We got Shiny for Python now, which is also nice.
I haven't checked that out. How is it?
I haven't done too much with it either, but Joe and the team over there are doing pretty cool stuff,
like adding more dynamic interactive stuff to Jupyter, like running inside Jupyter and things. Yeah,
pretty cool.
I'll have to check it out.
I think they also do a bunch of hosting stuff over there as well, is why it came to mind.
What other advice you got for folks out there?
So AI is AI, not studying AI or learning to use AI, machine learning, but is there a benefit of trying
to use ChatGPT to help you get this job or is there a danger? I'm thinking, for example, have ChatGPT
write me an awesome resume and then the tools are like, well, we've detected this is AI generated and
it's out. You know what I mean? What do you see happening there?
A lot of people see AI as like an all or nothing tool as in it's either you, the human doing the
work or it's the AI doing the work. But whenever, I don't know about you, but whenever I'm using
ChatGPT for anything, it's very rare it's copy and paste for me or at least not iterative where I'm
doing multiple prompts, prompt after prompt after prompt, trying to tweak it exactly what I want.
And so the way I look at ChatGPT and other gen AI that will be coming out, that's only inevitable,
is instead of looking at does this replace me? Does this, like for instance, am I going to build
my whole resume using ChatGPT? Can ChatGPT build, you know, take a data scientist's job and build the
whole model for them? I like to see it more as like a hammer. It's like a tool for the data scientist or
a tool for the job searcher to use in conjunction with your screwdriver or anything else. It's like
something to be wielded by a human, not replaced for the human, if that makes sense.
You know, it's really good for stuff like, hey, I know a regular expression will do this.
Yeah.
The last time I studied, I completely forgot what this is about. And I know it's gnarly,
but if I just ask, here's an example, here's what I want. Boom. And traditionally what you would end up
doing is you'd be on Stack Overflow. Yeah.
You'd be all over the internet. You'd be trying to piece it together from external information anyway.
And so code is something that's a little bit more in the wheelhouse of the generative AI,
because it can't really make it up as much. I know it could like do something insecure and you didn't
know it was or whatever, but it's not like asking for legal advice where it makes up cases that didn't
exist. Like it gives you code. You put it in the runtime of the compiler and it runs or it doesn't.
And the output comes like you did.
Yeah. It works or not.
Yeah. So it's pretty, pretty effective for that. But yeah, for resumes, I would be more like,
let me ask it. What are the in demand things? And if I know these three skills, what other skills should
I know to get a, you could sort of use it in an explorative way to then come up with what you
might write for yourself, right? Something like this.
I find it really useful for brainstorming like action verbs on your resume bullets. Like I think
it's really good at that. What's 10 different ways to say lead. So I don't say lead five times on my
resume and I use some different action bullets. I think it's great at that. I personally, it's pretty
rare that I start any Python code from scratch nowadays. I'm either starting hopefully from a
template that I've already written, or I'm starting from a ChatGPT. Like this is what I kind of want
to accomplish, right? Like the outline for it. Like one of the things I hate doing is I make a lot of
streamlet apps. I probably make a streamlet app a month right now. And I hate starting from scratch
with streamlet. It's super easy to start from scratch, but I'll say, Hey, ChatGPT, I want to
build a streamlet app. This is like the component I want here. This is the component I want here.
This is the component I want here. And it's almost like a warmup for me as a programmer. And it will
create something that works. It's not what I want. And I spend the next five hours trying to make it
what I want, you know, without ChatGPT, but it kind of gives me a warm start to my programming process.
So I really like it. I think it's something that everyone should use. And I think if you're thinking
about getting into any sort of programming, you know, whether it's data science or web development,
I think you should be a little bit less worried about it taking your job and job security. I think
you should almost be more excited that, wow, the bar has never been lowered to break into tech.
Like this is a step up gift from the programming gods that I get to use to break into tech.
Another thing to keep in mind is I imagine a lot of people listening to this podcast are not just
starting a college program, right? They're coming from possibly other experiences, other specialties.
You know, what's really good for job security, knowing the intersection of two things, the
intersection of chemistry and programming, the intersection of geology and programming for Exxon,
potentially, right? Like those things take you from a pool of a thousand to a pool of tens,
tens, right? And so what's awesome about that is it means two things. You don't throw away,
if you got a degree in something else like biology or whatever, you don't throw away like,
well, that was wasted four years. That's out. And it slices the pool of people who could apply for
certain jobs way, way smaller, right? Sounds like you agree.
Oh, a thousand percent. I'll just tell a quick little anecdote. When I was at ExxonMobil,
there's a lot of things I did not like at ExxonMobil, but this is something I really liked. It's about
once a quarter, they would do a crowdsourced data science competition for the whole organization,
like around the entire world. And they would say, this is a business problem we're trying to solve,
you know, and at Exxon, we have data scientists all over the world and like all sorts of different
teams and things like that. So I like did not know all the data scientists at Exxon. And they'd say,
this is the problem we're facing. Here's the data go, right? And I loved participating in these. It was
like right up my, my wheelhouse of like, I really enjoy exploration and all this stuff.
At the time I was getting my master's degree, but I didn't have my master's degree.
And I was competing against, so I'm just a chemical engineering grad, right? And I'm competing against
people with PhDs in computer science and in data science and all these like people who have way
more experience than me. And I actually won a few of these competitions. Thank you. I appreciate it.
And it's not because I was a better programmer or a better data scientist. It's because I majored in
chemical engineering and I knew the business problem, the domain extremely well. And I kind
of knew the programming and the data science stuff, but the combination of them made me very valuable.
Like one of the best examples I have is we're looking at crude oil properties. And I remember
like there was a forum where you'd like ask your questions. And one of the, one of the data scientists
asked, Hey, is sulfur bad? There's lots of sulfur in this. Is it bad? And like to a chemical engineer,
that's like the most obvious thing. No, you, yes. Sulfur is very bad in crude oil. That's very,
no, no, that's like such a fundamental thing to me and to him or her. That was like groundbreaking.
And so, yeah, your domain can become your superpower in your career.
Yeah. And it makes it way harder for ChatGPT and other types of tools to just automate you out of a
job because you bring in all these skills together, which is awesome. But it also makes it easier for
you to get the job. It makes it easier for you to continue your momentum of whatever you've been up
to. It's just, it's good all around. Yeah. I think it's more fun too, because once again,
like when I was trying to decide if I should study computer science, I was like, man, I don't really want to
to build an Excel workbook for building an Excel workbook sake. That's still true for me today.
I don't want to do data science for data science sake. I only like machine learning or data science
when I'm doing it to solve a really fun problem I'm passionate about. That's where it's more fun.
So if you can be excited about the domain and excited about the algorithms, I think that's a
great place to be. Absolutely agree. All right. We're getting short on time,
but maybe tell us a bit about your data career jumpstart. You've referred to it a couple of times.
Yeah. I have a company called data career jumpstart. I just try to do a lot of education. So the
education happens on LinkedIn happens on YouTube. And I actually forgot to mention this at the
beginning, but I have my own podcast called the data career podcast, where I help people land their
first data job. We're about at a hundred episodes. So not quite the groundwork that you've put in.
That's still a ton. That's awesome.
Yeah, we're getting there. And then, yeah, I also have a bootcamp where I try to affordably help
people land their first data analyst position by teaching them the skills, the networking and the
project and portfolio building that they need to do something.
Like the long version of this show.
Yeah. Basically. Well, yeah. Just take what we talked about today, expand on it,
make it like 350 unique lessons. And that's exactly what it is.
Yeah. Very cool. All right. Well, we're about out of time. So maybe just every final call to action,
people maybe are inspired. I see Dave go out in the audience. That's an awesome talk. Very much so.
What's next. It's easy to be inspired, but you got to take action.
Yeah. I love that. I think it's always fun to listen to podcasts, but you probably benefit way
more from the action you take after a podcast. So for you guys who are maybe interested in a data
analytics or a data science career, explore that. If you're like, yes, I'm in, make a plan, make a
roadmap. If you need help, I have a webinar that will help you make a roadmap. What skills should you
learn? How should you be networking and stuff like that? But really probably if you're just getting
started trying to figure out what skills you should learn, like what are the top skills that you should
be learning and then learning those skills and then not only learning those skills, but take action and
learning and build some sort of a project that we talked about that you could put on a portfolio,
make a streamlet app or something like that. That's probably the best action you could possibly take.
If you need any ideas, advice, feel free to check out my website, datacareerjumpstar.com or the
podcast data career podcast. Hopefully there's a lots of free resources for you guys to check that out.
If you've never seen streamlet before, I have some YouTube videos about
streamlet that you guys can check out, but I love it. Just take action somehow, do something.
That's one of the huge, huge differentiators is like, you might be inspired, but you just got
to start taking those steps and it becomes a snowball. So thanks for sharing all your experience and your
advice. Hopefully some people out there are taking action and yeah, I'll put everything we talked about
in the show notes, of course. So thanks for being here, Avery.
Yeah. Thank you. Thanks for having me. I appreciate it.
You bet. Bye all.
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