#455: Land Your First Data Job

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. Welcome to Talk Python To Me, a weekly podcast on Python. This is your host, Michael Kennedy.

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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. This portion of talk Python to me is brought to you by Sentry code breaks. It's a fact of life with Sentry. You can fix it faster. As I've told you all before, we use Sentry on many of our apps and APIs here at

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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 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 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 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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