#455: Land Your First Data Job Transcript

00:00 Are you interested in data science, but you're not quite working in it yet?

00:03 In software, getting that very first job can truly be the hardest one to land.

00:08 On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice

00:13 for getting your first data job. This is Talk Python to Me, episode 455, recorded January 18th,

00:20 2024.

00:21 Welcome to Talk Python to Me, a weekly podcast on Python. This is your host, Michael Kennedy.

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01:39 Hey folks, before we jump in and talk about data science jobs and careers, I want to tell you

01:44 really quickly about some awesome news. Back in February, I gave the keynote at PyCon Philippines.

01:50 It was entitled The State of Python in 2024. Well, that is now out on YouTube. The team at

01:58 PyCon Philippines did a great job. The video came out great. If you want to check out The

02:02 State of Python in 2024, according to me, just click on the link in the show notes to watch

02:07 it over on YouTube. Now let's talk to Avery. Avery, welcome to Talk Python to Me.

02:11 Thanks so much. I'm so excited to be here and be part of the show.

02:15 I'm excited to have you here as well. You know, one of the things that people reach out to me often

02:19 is how do you get into data science? How do you get into programming? How do you get into Python?

02:26 You know, I've been trying, or maybe they got a degree or they took some training program,

02:32 bootcamp or something. And going from zero to one, I think is the biggest career step you have to make.

02:40 That next job and the one after that, it only gets to be smaller steps, not bigger steps.

02:45 And it's really tough because that first big step, you're brand new at it. You have no experience,

02:49 right? It's your first data science job or your first programming job. And so hopefully we can

02:54 give some folks out there a little bit of a hand up to help them make that jump.

02:59 Yeah, totally. I like to show this graphic that says, it's a circle and it's a circle of text. And it says,

03:05 I can't get a job because I don't have experience because, and then it restarts,

03:11 I can't get a job. And that's the tricky part. It's like, how do you get a data science job when

03:15 you have no data science experience? Because to get data science experience, that seems like you have

03:20 to have a job as the prerequisite and vice versa. So it is very tricky. So happy to chime in on that

03:25 today.

03:26 The industry can take it too far. They can take it way too far. So a few years ago, there was a

03:32 really funny tweet that went around back when they call them tweets. I don't know what they're called

03:36 anymore. Sebastian Ramirez, the guy who created FastAPI, saw a job posting. When FastAPI was like a

03:44 year and a half old, it said, you must have four years of experience with FastAPI to apply. He said,

03:50 hey, look, I'm the creator of FastAPI and I'm unqualified for this job. What kind of world are

03:55 we living in?

03:56 Yeah. I don't want to live in that world, but that's unfortunately where we're at. That's so tough.

04:01 And it's hilarious. These job descriptions are getting out of hand. That's for sure.

04:06 Yeah. Well, with AI, it's probably not going to get better.

04:08 We could talk about that more later. But before we get into that, let's just jump in with a little

04:13 bit of background on you before we get to the topic. Tell us a bit about yourself. What do you do?

04:18 How'd you get into Python? Things like that.

04:20 Yeah, absolutely. So I'm currently a data science consultant and also a data science instructor.

04:27 I run some online programs where I teach people to become data analysts mostly is what I'm focused on.

04:34 But I also have this practice where I help companies solve data problems with different techniques.

04:39 I started actually by studying chemical engineering in college in my undergraduate degree.

04:45 And about a semester in, I realized, crap, I hate this. This is not for me.

04:50 But I was a little on a little of a tough. Yeah. Do you agree? Have you felt something similar?

04:54 I did a semester of chemical engineering as well. I thought, I love chemistry. I love math. Put them

04:59 together. Somehow they don't go together. It's like ice cream and eggs or something. No,

05:04 they don't go together for me at least.

05:06 Yeah. It wasn't good for me either. I was just like, oh man, I'm actually not interested

05:10 in refineries or like manufacturing. But I, like you, liked chemistry. I liked math. I thought this

05:16 is perfect. But I quickly realized, oh man, I really liked this whole programming part that I get to do

05:21 in MATLAB at the time when I was an undergrad. And I was on a time crunch to get through college kind of

05:27 quickly through eight semesters. And the other issue I had was I didn't know what to do instead. It was

05:33 like, I don't really want to study computer science. Part of the reason why is they kind of

05:37 had this weed out course at the beginning, which you had to build Excel from scratch, basically like

05:43 some sort of a spreadsheeting tool. And I was like, why would I rebuild something that already exists

05:48 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

05:52 what to do. And luckily I was working as a lab technician at this company, the really cool company

05:58 that makes the sensors that basically have the ability to smell. So they can sniff what's in

06:04 the air and it has applications for finding drugs or bombs and airports and stuff like that.

06:09 And there was a data scientist on staff and that data scientist was awesome. He was like showing me

06:14 all these cool algorithms he was writing for these sensors. And then one day he got up and left and he

06:20 left the company and we tried to hire another data scientist for like six months, but they were really

06:26 expensive. We were a small company and none of them really wanted to move to Utah where I lived in

06:30 Salt Lake City. And so we couldn't, we couldn't really find someone that would be able to do it.

06:33 And so finally I was like, well, I really liked this programming stuff. And I, you know, the data

06:38 scientist showed me a thing or two, maybe I could take a stab at this. And I started, I wrote like my

06:42 first machine learning algorithm and I was like, oh my gosh, I'm addicted to this. And then I never

06:46 looked back and had been data science since basically.

06:49 What a great story. Yeah. I think, I think a lot of people fall into programming that way. And for some reason,

06:55 not unexpectedly, but for some reason, a lot of people fall into Python that way as well. They're

07:00 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

07:05 like an Excel spreadsheet or something and put it together. And you're like, actually, this is cool.

07:10 After a while, like, this is cooler than what I've been doing, or maybe I'll make it a good part of what

07:14 I do. Right.

07:15 Yeah. A hundred percent. Even just making, it was in MATLAB, which is basically engineers version

07:20 of Python or college version of Python 10 years ago. Right. And I made like tic-tac-toe and I remember

07:26 playing tic-tac-toe against the computer. I think that's what it was. Or maybe it was,

07:29 maybe it was Hangman. I can't remember. But I remember like the idea of like being able to play,

07:33 to program games and play against the computer. And I built it. I was like, this is the coolest thing

07:38 ever. I got to, I got to do more of this.

07:40 Absolutely. You know, I think I've done some MATLAB too, when I was younger and it's not that

07:44 different from Python, but it's, I think one of the big differences other than it just being like

07:49 embedded in a big expensive app is it's not a general purpose programming language, right? You

07:54 wouldn't go, you know, that was fun, but let me go build this website in MATLAB or let me create

08:00 Airbnb and MATLAB or, you know, like there's, you just don't want to sort, Azure has this like

08:06 self-prescribed limit to what you can do with it.

08:08 That's one of the coolest parts about Python is it's really a Swiss army knife and you can pretty

08:13 much do, I don't want to say anything, but pretty close to anything in Python, which makes it really

08:19 neat. And obviously one of the huge limitations of MATLAB is one, it costs thousands of dollars,

08:23 but two, you're right. It's not going to do cybersecurity for you. It's not going to build

08:27 websites, but the syntax at the end of the day was, was really quick. It was, it was easy for me to

08:32 transition from MATLAB to Python because the syntax isn't all that different.

08:35 No, it's not all that different. More math focused, but pretty similar. So I think maybe that's a good

08:41 place to start discussing and exploring the topic of your first data science job. And

08:46 wouldn't necessarily plan on starting here, but let's, let's start with before you even necessarily know

08:50 programming language, right? Maybe you've dabbled in MATLAB or you've dabbled in Excel or even dabbled in,

08:57 I don't know, JavaScript or something. This thing we've been talking about with MATLAB and it applies to other areas as well,

09:02 like through programming languages per se, like Julia or something like that, is how,

09:08 if you invest your time into learning one of these things really well, like how broadly industry-wide

09:16 of a skill, high demand skill is that going to be, right? If you learn MATLAB, you put yourself in a box,

09:21 you learn a more general programming language, you kind of have more options afterwards, right?

09:25 Yeah, totally. I think like the more broad of a language you learn, the more useful you are to,

09:31 to more industries in general. But I might take that even a step further and just say, you know,

09:37 learning MATLAB, not a whole lot of companies use MATLAB, but just like landing your first data job,

09:43 going from zero to one is the hardest, learning your first language, zero to one is the hardest as well.

09:48 And then once you have that first language, the next language becomes so much easier. So

09:52 one of the first things I learned was MATLAB. And then I moved to Python and that was easier. And then

09:57 I learned SQL and then I learned R and then I learned JavaScript. And every time I added like a new tool

10:02 to my toolkit, it was quite, not almost, it was easy, but it got easier with each one. I think that's true

10:07 with foreign languages as well. Once you learn one foreign language, then the third and the fourth become

10:12 quite easy. At least that's, that's what I heard. I speak kind of through two and a half languages,

10:17 but like I, there's people who speak like seven and they always say like the sixth and the seventh

10:21 become easier.

10:22 Yeah. You wonder how could you probably, because learning the first one is so hard,

10:25 first foreign language. So you're like, well, how could you possibly take that on for this many

10:29 languages? And it's that it's not the same challenge each time, right?

10:32 Yeah, exactly.

10:32 Yeah. So I think when people are considering getting into data science, they really want to consider

10:38 what language they choose and where they go. Like you're coming out of a

10:42 college program. You might feel like MATLAB or something like that's real popular. And yet

10:47 that's because it's popular amongst professors who forced their students to do it. That doesn't

10:51 necessarily mean that's the world, the broad worldview. What do you think about R? You know, both.

10:57 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

11:03 a lot on LinkedIn, kind of a LinkedIn guy. And so a lot of the times, honestly, just for jokes and kicks

11:08 and giggles, I'll kind of roast R on LinkedIn just to get the trolls angry in the comments.

11:13 I've invented it. It's quite fun. It's quite a fun experience, but I'm not that big of a hater. I

11:18 think that's really interesting about R versus Python is obviously a big debate in the data

11:23 science community is R is kind of that does one thing really well. And it's getting a little less

11:29 of that as like more packages and libraries are added to R, but R does the statistics and machine

11:34 learning very well. But obviously I don't think once again, I don't know any websites, any like

11:39 super functioning websites that are built on R. I don't know any cybersecurity that's really done

11:44 done, done VR. So I think R does what it does well. The syntax sometimes is a lot easier for

11:49 people to go from Excel, which a lot of people are more familiar with in the finance or banking world,

11:54 for example. The syntax in R is a little bit more similar to those Excel formulas than it is to Python.

12:00 So I think sometimes people have a little bit more success just because, oh, this kind of feels like

12:04 our formulas are sorry. This feels like Excel formulas. And so people really get there. I think what

12:10 you're kind of alluding to is if you're going to learn one skill, you might as well learn the one

12:14 skill that's applicable to the most, the widest net, right? And so that way you're fishing in the

12:20 biggest lake you possibly could versus in a smaller pond of R. I think that's worth looking at. And one

12:27 of the things I actually really enjoy doing, because you know, you mentioned, oh, you might think MATLAB

12:31 is popular because that's what the professors taught you. And there's actually not a whole lot of

12:35 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

12:41 analyst YouTuber. I was going to say YouTuber on YouTube, but that's kind of redundant on YouTube. And

12:45 one of the things he's done is he's actually built this tool where he's web scraping thousands of jobs,

12:50 different data jobs every week, and then displaying and analyzing the skills required for those jobs.

12:56 So it's actually like a data driven way of saying, if you want to be a data scientist, what skills should you

13:01 actually be focusing on as you go, as opposed to just listening to what a professor will say,

13:07 or what a LinkedIn influencer will say, or what your bootcamp will say. Like actually getting some

13:13 data on, I think is pretty neat. That is super cool. And I'm not familiar with Luke. So we're going to dig

13:19 him up and put him in the show notes for later so people can check that out. For sure. Do you remember

13:23 any of the trends you've recently talked about? It's datanerd.tech, I think is the website there.

13:27 I look at it mostly for data analysts because that's who I work with the most. So I know the

13:33 data analyst data very well. SQL is number one at 50%. I think Python is number two at like 30%.

13:40 I think Python might've jumped it. Well, this is for all data positions right here. So the job title,

13:47 you can choose. So which one do you think I should pick here? Data?

13:51 Maybe data scientist. Data scientist. Yeah. Right.

13:53 What's that? Yeah, you're right. Wow.

13:56 Whoa, Python 69%. Look at that. That's huge. So like, that's even, that's even what? 20% more than SQL,

14:04 which a lot of people are like, if you were going to be a data scientist, you have to know SQL.

14:07 Yeah. If you look at the job descriptions, Python's mentioned a lot more. So if you're going to learn,

14:11 if you're brand new and you're going to learn one, you might as well start with Python. Because that's

14:15 probably the most in demand skill that there is right now for a data scientist.

14:18 Yeah. And it's pretty easy, right? It's not like, well, why don't you just learn C++ for

14:22 embedded devices? You're like, you know what? Maybe I'll pick something else to start with. Right. But

14:26 you know, Python's pretty easy. I agree with you. I think Python's great. I actually think,

14:31 I think SQL is probably easier to learn if I'm being honest, because really, especially for like

14:35 data science stuff, there's only about like 20 commands that you need to know in SQL. But it's,

14:40 once again, SQL's a lot more, there's no websites built on SQL. I'll tell you that much. So

14:44 it's a lot more limited on what it can do.

14:46 It's a skill, but not the language. It's not enough on its own, generally. I mean,

14:52 you can do reports and quite a bit with it. But you know, it's like, when you see these programming

14:57 popularity, like what's the most popular language? Oh, look, CSS is the third most popular. That's not

15:02 a language. That's a thing that you use with other languages, right? Like use it with all the other

15:06 languages. That's why it's high up. But that doesn't mean it's high in demand. Exactly. It's just like

15:11 table stakes, you know? Yeah.

15:12 So you kind of got to distinguish table stakes from like picking an area, I think.

15:17 That's totally true. And really, I think Pythonistas could make the argument that there's

15:22 really nothing in SQL that you couldn't do in Python. That's a little somewhat true,

15:28 true depending on data size and stuff like that. But regardless, there is ways that you can do

15:32 most of the SQL commands in Python one way or another. Yeah. Yeah.

15:36 It could be when I first became a data scientist, I didn't even know SQL and I was doing SQL commands

15:41 or I was doing the aggregations or the where functions or the window functions using Python.

15:47 So you definitely can. As long as your data is not like super big, then you'll totally be fine.

15:51 Right. Like some kind of generator or even slices or yeah, things like that, right? List comprehensions,

15:57 set comprehensions, all that kind of stuff. Kind of like, gosh, I really wish, a little bit of a

16:02 sidebar, but I wish like list comprehensions and all those things had just a few more SQL features,

16:08 right? Like in a list comprehension, I say, give me this thing, maybe give me this property of this class

16:14 modified, like give me the user's name, uppercase. Right. So that's like select. And then for thing

16:20 in collection, that's like from table or whatever. Right. And then you have the where clause with the

16:27 if statement, but boy, wouldn't it be cool to have like a sort also in there and other things like that,

16:33 you know? Oh, well, totally. It's so close. The cool thing is, is if you want that sort,

16:38 it's what one extra line. Like it's, it's not, it's not too bad. So it, Python, I mean,

16:43 I don't want to say this necessarily to hate all, to make all the data scientists and SQL lovers

16:47 mad, but, but really Python can do a lot of the things that SQL that's for sure.

16:52 Yeah, that's for sure.

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18:51 Probably the biggest, it's a bit of a diversion, but the biggest similarity to that I've seen in

18:57 the languages is C#'s link where they actually have almost all the query operators, including

19:04 joins and stuff like that built into the programming language. I'd love to see more of that kind of

19:09 inspiration into Python, but you know, that's all right. It's still really good. I've got a lot of

19:13 cool SQL-like features, but you're right. Once you are no longer working with data and memory,

19:17 or you want indexes, right? Like this concept of indexes is not sufficiently well understood. I think

19:24 every time I hit a website that takes five seconds to load, I'm like, somebody is not doing all the

19:28 things they should be doing. I just know it. That's totally true. What about SQL? You know,

19:34 let's talk about that for a bit, right? The SQL, the query language or databases and other things,

19:40 there's ways to SQL query, not just relational databases. But you said you got away with not

19:47 quite learning that, but do you think if you could start over, maybe making an effort to learn that

19:52 would be really valuable? Like how, how important is this a thing in the beginning of your career?

19:56 The interesting thing, you know, about landing a data job is your skills only plays, I say,

20:03 a third of the role. Your portfolio or the way that you portray your skills and your network,

20:09 I think are the other two thirds and they're actually more important than your skills. And that's

20:12 kind of how I got away with not knowing SQL and not even being, to be honest, that good at

20:17 Python at the time was because I used my network to be in the situation to get my lab technician job

20:24 in the first place. And then once again, I use that same network, in this case, my coworkers,

20:28 to land that first data scientist position after we couldn't hire anyone. And if I would have been

20:33 applying externally for that role, chances are I wouldn't have gotten that role. I probably didn't

20:39 know enough at the time to land that type of a role, but because they knew I was hardworking,

20:43 they knew I wasn't like a total idiot and I really liked to learn. They took that chance on me. It

20:49 paid off really well for them because at the time I was still in college. And so I wasn't getting paid

20:53 that much. And I was getting, I was not getting paid like a data scientist, but I was getting results like

20:58 a data scientist for them. So I think it was, it paid off for both of us. But I think if that was an

21:03 external job and I applied for it, I probably didn't have enough skills for it. So I definitely think

21:07 learning SQL, if you want to land data science job, isn't a bad place to start, especially because,

21:12 like I said, there, I mean, any programming language, I like to think of like the iceberg,

21:17 kind of like the Titanic, right? There's the parts that you see, and then there's the parts that you,

21:21 that you don't even know that you, that are there. And, and really you could spend the rest of your life

21:26 trying to master SQL or the rest of your life trying to learn Python. But the cool thing is,

21:31 is a lot of the time you only need that top little bit that's sitting at the,

21:34 the top of the surface of the water to actually get stuff done. And so for SQL, I think that's like

21:40 20 commands. And I think you could learn it honestly in like a month, you could learn those,

21:45 those 20 commands pretty easily, but it worked out for me. And I, I didn't have to use it that much

21:49 at the time until I was probably about almost three years into my job. And I actually had switched jobs

21:54 to a bigger company. The other thing that I was working for a smaller company where we didn't have a ton

21:58 of data. So we could use CSVs kind of as our, our database, which is not great practice. But when I,

22:05 when I eventually became a data scientist at Exxon mobile, I was going to say they didn't use Excel as

22:09 a database, but they still did. But the point is they had much larger SQL databases with hundreds of

22:14 thousands, actually millions of rows of data that I had to query.

22:17 Yeah. Then you gotta be really, you need to understand it at a much deeper level. You're

22:22 like, if you do a query like this, it's going to be super slow. But if you do it like that,

22:26 it can use the composite index for the sort and then blah, blah, blah, blah. All right. Then

22:30 you're getting to the bottom of the, the iceberg in SQL, or maybe not the bottom,

22:33 maybe like the middle chunk under the water, but there's so much to learn for both of them.

22:37 Amir at the audience asks, you know, like when you talk data job, like what kind of jobs are out

22:42 there? Right. So we talked to both about how we did chemical engineering and then we saw like

22:46 chemical factories, like, yeah, I don't really want to work here anymore. I'm out.

22:49 So thinking about like, well, what are the kinds of jobs you do? I think that's really important because

22:55 it's easy to get focused in on like the FANG companies. Like I want to work for like some super

23:02 big tech company. I want to move to San Francisco and like that, that, that, right. Like there's not just

23:07 plenty of other jobs, but the opportunities, just like you described, and as well as

23:12 like my first job, I worked at a company that had like eight people and it was awesome. Right.

23:17 They didn't expect me to be, you know, running Kubernetes clusters and doing all sorts of great.

23:22 They're just like, I need you to make this thing happen. Can you do like, I'm pretty new,

23:26 but that thing I can make that happen. Like, let's go. Right. And I feel like the, the possibilities

23:30 to get in, especially with these maybe more niche type of industries and companies might even be easier

23:37 for a first job. People seem to be really obsessed with, with the FANG. And I don't know if that's

23:41 like a societal thing, or if it's just, those are the companies that we use a lot. And so we're

23:46 excited about them, but yeah, there's so many more data jobs outside of FANG than there are inside of

23:51 FANG, even though there's, there's quite a bit inside of FANG. And oftentimes those roles can be

23:57 much more interesting and you can do a lot bigger of an impact. When, when I was working at the small

24:02 company, VaporSense, I like, I had so much power. I didn't even realize it. I had such a big effect on the

24:08 company. I was presenting to, you know, Fortune 500 companies and what I did really made a difference.

24:14 And when it came to the point where ExxonMobil offered me to go be a data scientist for Exxon,

24:20 I said, Oh, I want to go work for the big company with the nice desk and the nice laptop and, you know,

24:26 try something new. And when I got there, I really, I had some pretty cool opportunities when I was at

24:31 ExxonMobil, but ultimately I left pretty shortly after two years of being there because I just felt like a

24:36 cog in the machine and I didn't feel like I was actually making a difference. And that was really

24:40 important to my work satisfaction of like, is what I'm doing being used? Is it being used to better the

24:45 world? Do I feel valued? And the answer was kind of no for me when I was there. So there's definitely

24:50 a trade-off between the small companies and the big companies, but also to go back to your original

24:54 question, there's so many freaking roles in the data world that you're not even thinking of that. Like,

24:59 I'm not even thinking of, I saw a new one the other day when I was helping one of my students.

25:04 It was like, it wasn't data janitor, but it was something like that where I was like, I don't

25:08 even know what that role is, but there's, there's so many roles. When I was, when I was a data scientist,

25:12 VaporSense, the small company, my actual title was junior chemometrician, which basically means

25:19 you're doing data science with chemistry. When I was at ExxonMobil, when I was first there,

25:24 I was doing data science, but my actual title was optimization engineer. And so there's so many

25:29 titles that we don't even think to search of, or even to look up, but those are all data science

25:33 roles. I was doing machine learning every day in both those roles. And you would maybe never guess

25:37 from those titles. Yeah. You would never guess. No, that's awesome. What machine learning libraries,

25:42 frameworks were you using? At VaporSense, once again, because it's a smaller company,

25:46 I had a lot more say in what I was doing. We were building a bunch of machine, we were building

25:51 classification models to basically to take the data from our sensors and sniff if something was in the

25:56 air. Sometimes that was a yes, no, like, oh yes, there is ammonia in the semiconductor factory and

26:02 that's bad. So that's a yes classification kind of binary, right? Other times it was, what drug is this?

26:09 Is this meth or is this heroin? One of the use cases we had was, this is binary once again, but is this

26:15 recreational marijuana or medicinal marijuana? And can we tell the difference between, between those?

26:20 So we are usually using classification models, usually built in scikit-learn in Python, the majority of the

26:26 time there. When I was at Exxon, we had a lot less say, like the data scientists had a lot less say in

26:32 the decision making process. We were doing a lot of multivariate linear regression with a lot of crazy

26:39 hacks and transformations kind of in the meantime for one of my positions there. And then the other time,

26:44 the other position I did there, we were doing a lot of auto ML using PyCaret and letting it kind of

26:50 decide what type of models to do. So. Okay. The unsupervised learning type stuff, huh?

26:54 It was awesome. It was really fun to, to, I love PyCaret because it's like, okay, go make 25 models and

27:00 tell me which one's the best. It's like, takes, makes my job easy, I guess.

27:03 We're going to be creative with sheer numbers. That's how we're going to come up with a solution.

27:08 Got it. Exactly.

27:09 Well, Diego is asking like, what are some of the common stats methods as in mathematical type stuff

27:16 you would use? So one of the things I know that some people getting into programming think is you've

27:22 got to be really good at math to be a programmer. I think you've got to be really good at logical

27:26 thinking, but you need to almost zero math be like a web developer. You know, we're talking percents

27:33 for CSS, incrementing numbers from one to two to two to three for IDs and stuff like that. But for

27:40 data science, maybe there's a little bit more like, where do you see that kind of background?

27:45 I like what you said, you have to think logically, but maybe the math isn't as important. And I think

27:49 it's actually somewhat similar in data science. I will say you probably need a little bit more math

27:54 than a web developer, but I think it's a lot less than most people think. And it's probably less

27:59 about being able to do the math and maybe more about understanding the mathematical concepts.

28:04 And what I mean by that is a lot of, a lot of, so I also have a master's degree in data analytics.

28:10 A lot of master's degrees in data science and data analytics will say you need calculus and linear

28:15 algebra as kind of a background for your math. And that kind of stops people. I don't want to do any

28:19 calculus. I don't want to do any linear algebra. And while both those concepts do exist in data

28:24 science principles, the majority of the time, the computer, Python is doing the math. You just have

28:30 to be able to interpret the results of the math and kind of know what different directions, like this is

28:36 going down, an optimization problem, you know, okay, that's the derivative, you know, getting closer to

28:40 zero. Like it's really less about knowing how to do the math by hand and more just understanding what the

28:46 math the computer is actually doing. So I think it's actually a lot easier than most people say.

28:50 That being said, knowing how to do a derivative or taking integral, those concepts, I think is

28:55 probably underlying pretty important. But other than that, like a lot of the times I'm doing linear

29:00 regression because it's, it's awesome. It gets the job done. A lot of the time I'm doing hypothesis

29:06 testing and statistics, which you have to look like at a P score, nothing all that crazy. At Exxon,

29:12 I had to do a lot of linear programming, but that's honestly, that's like the exception versus the rule.

29:17 There's not a whole lot of linear programming for most data science, most data scientists. So

29:22 I really don't think the math is, is all that hard. Now, of course, that's coming from someone who

29:27 got a chemical engineering degree, who had to take all the calculus, all the linear algebra. So I did go

29:32 through those courses. I haven't really done it from scratch from like a lot of my students are teachers,

29:37 for example, who never took those courses in college. So I can't speak from that perspective,

29:41 but a lot of my students are able to figure it out at the end of the day and transfer. So it happens.

29:45 Yeah, yeah, for sure. I think there, you make a good point. I think it's about knowing,

29:50 okay, this formula or this algorithm or this test means this thing. It applies in this situation.

29:56 It doesn't apply in that situation. Here's what you're trying to get from it, right? Like,

30:00 I know I need to do a fast Fourier transform. So, and this is what it tells me when I get out the other

30:06 side. But do I need to be able to sit down and recreate the integral and the calculus behind it

30:14 and do that on like a home, like as a homework example, like, give me a function and I'll do the

30:18 Fourier transform and I'll actually do the symbolic integration. Like, no, you probably don't need that,

30:23 right? But you need to know, I do the Fourier transform in this situation and this is why. And then I just say,

30:30 call the function, do it, right? And interpret the results. Really, that's what being a data

30:35 scientist is all about is, yeah, what does the business use case, what's the desired business

30:40 use case? How do I relate that use case to the data? What technique can I use to get the outcome

30:45 that I need? Computer, go do it. Interpret results, present to stakeholders. That's a data scientist,

30:52 right? I think one of the challenges with that is going to be, not that it's not good, but I think

30:57 it's going to be challenging because how do you learn when to use a certain statistical test or when to do

31:04 some kind of funky transformation, like a Fourier transform without more traditional mathematical

31:10 backgrounds? And all the academics will not just go, oh, we're just going to give you like

31:14 five minute overview and they'll help you understand. They're like, nope, we're going to start with this

31:19 axiom or this theorem from differential equations. I'm going to work up. You're like, no, no, no, no,

31:24 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,

31:30 how do I get value from a couple of the mathematical things without being sucked into like, yeah, now I'm in

31:36 differential equations at Harvard online and I don't understand how I got there.

31:39 It's such a big problem and I'm so glad you brought this up and I'll be vulnerable because yeah, I felt

31:45 the same, the same way. And I was like, there has to be a better way. And so about, what was it? Three

31:49 years ago now, two and a half years ago, three years ago, I said, oh my gosh, I'm going to solve this

31:53 problem and I'm going to start my own data science bootcamp. And so I spent about six months making the

31:58 curriculum, making all the videos. I opened it up. I got some students in there and I ran it for about

32:03 six months and I looked at the results and man, we weren't getting anyone into data science jobs.

32:08 And I thought, ah, what the heck am I doing wrong? I had this brilliant idea of like, we're going to be

32:12 less theory, more project, more hands-on. And I realized, man, the truth is people just learn better

32:19 at work. That's where you learn that whole technique that you just like, how does someone learn that?

32:24 The answer is by getting experience and learning it at work. And when I looked back and I said, okay,

32:28 well, we have had students get jobs. What jobs did they get? And it turns out most of them were

32:33 getting like business intelligence, intelligence engineer jobs or data analysts or financial

32:38 analyst jobs that were a little bit below a data scientist job. And I realized, oh man,

32:43 if we can just help people go from zero to one and get their foot in the door, they can go from one to

32:49 five much quicker at work because work is just, I don't know, it's this magical place, right? Like,

32:54 like you said, they, whatever you were working at earlier, and they're like, hey, can you do this

32:58 Kubernetes thing? They just kind of throw you in the fire and you're like, figure it out. And that's

33:03 somehow you do, I don't know what it is about work, but you figure it out and that's where you learn.

33:07 So that's kind of what I've, why I changed my curriculum to be more focused on, you know, okay,

33:11 maybe people aren't going to become data scientists, but can we get them to zero to one quickly?

33:15 And then they can get paid to learn the rest of the data science stuff when they're actually in that

33:19 first position. How much do you know about what you actually want to do in the industry

33:23 before you've done it as well? Right? Like, you're like, oh, I thought everybody said machine

33:28 learning was awesome. And I've used chat CPT and I loved it, but it turns out actually like API is

33:34 better, but I've never had a chance to build an API. So until I started, I didn't even learn that one,

33:39 it was a thing to that. It was cool or vice versa, right? Whatever. But until you get kind of in,

33:45 you don't even know, like, actually this part is where I really am enjoying it. And so just getting that

33:50 first step, that's a big deal. A hundred percent. You don't know what you don't know until you know

33:55 it. That's why, I mean, really when it comes to, if we like, we go back to just SQL or just Python,

34:00 you could spend, I tell people this, if you tried to master Python before you applied to a job,

34:06 you'd be like 80 years old before you ever applied to a job. Same with SQL, same with machine learning.

34:12 The cool thing about data is we're never going to know it all. And so just learn the bare minimum to get

34:17 your foot in the door. And then you have this place where you're going to get paid to learn what

34:21 you want to learn. Eventually, if you learn, oh, I love APIs. I promise you that there's a company out

34:26 there that will hire you and you can learn APIs on the job. Like that's going to happen. But that first

34:31 step is no true. There's a company out there that it doesn't know it needs APIs, but you could help them.

34:36 And you know, they don't have huge expectations because this is the thing they just learned they needed.

34:40 Right. A hundred percent. Yeah. It's wild, right?

34:42 This portion of Talk Python to Me is brought to you by Posit, the makers of Shiny, formerly RStudio,

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36:13 Posit Connect: Let's talk about some career advice. I mean, I know you talked about being

36:18 connected on LinkedIn pretty well and certainly having some kind of social network is important. And they

36:23 maybe, it's not that you would call it not social, but a real world network of actual human beings that

36:29 you're, you know, physically know somehow. Posit Connect: What's that? I don't know what that is.

36:32 Posit Connect: I know. Like, we gave that up back in 2020, I thought.

36:35 Posit Connect: Yeah. Posit Connect: Anyway, like, there was some stat that I saw somewhere that,

36:38 you know, over half of the jobs are filled filled before even becomes a job posting, right? Maybe

36:44 some of the best ones is like, hey, who knows somebody who can do this? We need some, like your

36:49 data science example, data scientist example. They quit like, oh, we need somebody. Does anybody know

36:54 good data science? I don't want to just go put it out on the open job market and have to have a hundred

36:58 interviews and who knows what I'm going to get. Like, if you can recommend somebody, let's start there,

37:03 right? So being in that group to be recommended, it's important.

37:07 Posit Connect: It's the key. There was a really interesting survey done on LinkedIn and they said,

37:12 it was kind of, it was done by the same person and Jordan Nelson, by the way, he said, "How do you

37:16 approach getting a job?" And then the next day he said, "How did you get your last job?" And 80% of

37:22 people, they use what I call the spray and pray method, which basically means you go and you apply to as

37:27 many jobs as you possibly can and hope for the best. Cross your fingers. That was 80% of what

37:32 people were doing. And then on the next poll, the next day, it was a total, I think of what, 70% were

37:38 either headhunted, recruited or referred. And so it's like the Pareto principle here where, you know,

37:43 80% of the effort is only getting you 20% of the results. And really 20% of the effort gets 80% of the

37:50 results. So it's okay. We know networking and getting recruited is really important, but how do we do it?

37:55 It's easier said than done. And like you said- In the industry, how do I make friends who are,

37:59 right? It's like, well, my neighbors don't do it. So I guess I'm out.

38:02 That's the tricky thing is, is yeah, if you're not in the industry yet, how do you get recruited

38:06 into it or how do you know someone? And what I've come to learn is it actually doesn't even matter.

38:11 So like, for instance, let's take, let's take your neighbor, right? Your neighbor is probably not a data

38:16 scientist. Maybe you're lucky and they are, and they can refer you to a company. But what's really cool is

38:20 I've learned that companies really come to trust their employees and their employees'

38:24 recommendations. And so even if your neighbor, let's say is a web developer, or maybe even less

38:30 technical, let's just say your recruiter is in finance, right? And if there's an opening,

38:35 like a data science opening at that company, a lot of the times they will actually take their

38:39 employee referrals much more seriously than any sort of cold application that they get.

38:44 And so a lot of the times I've had students who just know someone that works at the company,

38:49 they saw a job opening pop up. They're quickly, they message their friends. Hey,

38:53 do you know a recruiter or a hiring manager? I could talk more about this role. Could you do

38:57 an internal referral for me? And they were able to land jobs that they probably wouldn't have.

39:01 No, they definitely wouldn't have without that internal referral. So it is tricky. It's the old

39:06 cliche. It's not, it's not what you know, it's who you know.

39:08 I think there's still plenty of ways, COVID notwithstanding. I think that these days,

39:12 there's plenty of ways to get those connections, right? But maybe people don't know, like meetup.com

39:18 is really good. If you live in a non-tiny city, there's many, many things going on that around data

39:25 science, around Python, around other data engineering, whatever, right? You could go to those things.

39:31 They're typically even free. Often they are free with food. They even feed you, right? And make connections,

39:37 or regional conferences or national conferences, right? Like we probably, many people have heard

39:42 of PyCon, right? There's US PyCon, there's EuroPython, and then there's, but that's, those are the ones

39:48 that are often talked about, but there's 10, 20 little smaller regional ones in the US and many more that

39:54 I'm not aware of throughout the world. Probably one of those within driving distance, right? That you could

39:59 go to make connections and just also kind of take the temperature of actually what, what you see on the

40:05 internet versus what you see and actually talking to real people. So I'd also say, just get out there.

40:10 A hundred percent. Those places have the people who probably want to hire you because they're local,

40:17 right? Which is one thing that's, that's trouble on LinkedIn. I'm, I'm big on networking on LinkedIn,

40:21 but a lot of the times you're going to be networking with people who in all likelihood might never have a

40:26 role that's even open to you. But the people that you're like, for instance, we have,

40:30 I'm in Utah and we have Silicon Slopes that has like a tech meetup. We have a local Python

40:36 meetup chapter. We have the big data and developers conference that that's free every year with tons

40:41 of food. And the people who go there are people from companies around there that have the openings

40:47 that you're trying to find. And they want to hire people like you who are in the area. So at least

40:51 you can maybe come to the office once a week or maybe once a month or whatever. Right. And so really,

40:55 like you said, going to those meetups, it's tough because networking is always difficult,

41:00 either online or in person, but at least in those situations, you know, Hey, these are people that

41:05 are tied to real companies that exist around me that do make data higher. So I have a chance.

41:10 Definitely a much higher chance than just shooting out a resume. All right. Well, let's see.

41:13 We talked about job hunting already. What about like applications and resumes? What are your thoughts on

41:20 that? I think once again, with the applications, the more targeted that you can make it, the better,

41:26 right? So if you can really hone in on, I really want this job, I'm going to cold message five people

41:32 at this company and see if I can get that internal referral one way or another, make a real connection

41:37 with them. I think that's really key. And then with resumes, resumes are more of an art than they are a

41:42 science. I feel like they are so difficult to figure out. And these ATSs that are trying to match you

41:49 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

41:53 scientist behind those, we need to have a conversation with them because it's, it's a little tricky

41:57 sometimes. But one of the coolest concepts I've been introduced to recently, and I have a whole

42:02 episode on my podcast about it is A, B testing your resume. And basically the idea is a resume's job

42:10 is just to get you a screener interview or like a beginner interview, basically. Right. That's all an

42:15 interview. Like no one's seeing a resume and then hiring you. They're always going to interview.

42:19 So if you think about it, a resume's job, the only job it has is to convince someone to get on the phone

42:25 and talk to you. And it's just a piece of paper. And guess what? You can put whatever you want on

42:29 that piece of paper. Now I'm not saying to lie, but I'm just saying you could theoretically make a

42:35 perfect resume for whatever job you're trying to go for and send it out there and see what happens.

42:39 Right. But I'm not saying to do that. I'm not saying to lie. My point in saying this is that the resume

42:43 is just to get you the interview. And if you're not getting interviews, something's probably wrong

42:48 with your resume. And so, you know, tweak something, apply to 10 more jobs, see what happens. Tweak

42:54 something, apply 10 more jobs, see what happens. Until you finally have the right combination, skills

42:59 of experiences of different keywords. Because a lot of the time you're just trying to beat the ATS. And

43:04 that's the sad part about it is it's like, how do I prove to this random computer algorithm that they should

43:10 talk to me on the phone? That's a hard game to beat. And there's a whole bunch of advice

43:14 from all these different people. What I've come to learn is it's different for every company. It's

43:18 different for every person. You kind of kind of a numbers game till you get lucky and you figure it

43:22 out. That's good advice. I guess two thoughts. One is I know that speaking specifically to anyone,

43:28 one. But in general, women wait until they match all the requirements of a position where a guy's like,

43:34 I know three of those things. I'm taking a flyer. I'm sending it. I would just like to encourage the

43:41 women out there to just send it as well. I 100% agree with that. And I think if you reach 60% of

43:47 the requirements, I think you have a chance. Like it's a lot of the times those are wish lists and not

43:52 actual requirements. And depending on, are you local to the area? Do you have a domain experience

43:58 in this company? Like there's lots of other factors. What about contributing to open source

44:02 or having GitHub repos that can be like projects that you can show off or what's your advice there?

44:08 I'm a huge proponent of projects in the portfolio. I think if you don't have experience with something,

44:13 you create your own by building a project. And if you can do that with open source,

44:18 I think you should totally do that because I've benefited so much from open source. I have not

44:23 given back as much as I should to open source development and projects. I definitely should do that.

44:29 But if you can find a project that you're passionate about that you can help with, I think you should

44:33 totally do that. Even if it's not open source and you're just building a project to showcase your skills,

44:38 I'm all about that. I think you can do projects that are super fun, maybe that are good for your

44:43 community or good for your life. I'm a huge fan of personal projects. I've put a Fitbit on

44:48 my dog before and looked at her steps. I've found the healthiest meal at McDonald's. I've looked at,

44:54 like visualized my weight over time and tried to create like different, like forecasting models and

44:59 stuff like that. There's so much data in our lives that you can use to make really cool projects.

45:03 Oh, absolutely. You talked about, okay, you get your first job and that's where you kind of really

45:08 learn. But if you don't have your first job, you can effectively simulate that. Say, I would have gone

45:14 on to a job and been given a project to analyze something. I'm just interested in this thing. I've got

45:18 two hours a day until I get a job that I can be inspired about this and just get going on it. Maybe

45:24 create a website and publish your results and it can draw more people in to actually see that, right?

45:29 And start to appreciate it. They could even ask like, all right, who's behind this cool project?

45:34 Maybe I want them to come work for me. Little did they know you're doing all this work because you got

45:39 some spare time and you're trying to build up your experience and a self-guided study, right?

45:43 Yeah. If you can build a cool project and flip the job hunt where you're not applying for jobs, but jobs

45:49 start to apply for you, you're in such a good position and doing really cool projects can help you get there.

45:54 Now it's hard to do cool projects. It's hard to publish projects, which is one of the things

45:59 that people really struggle with. For all you Python listeners out there, let me just tell you,

46:04 Streamlit is absolutely amazing because it makes the deployment process so easy. It's free. It's a

46:12 little tricky to deploy at first, but compared to what you used to have to do it back in the day,

46:17 I'm saying back in the day, like four years ago, basically. But it was really hard to deploy

46:23 something where you could send someone a URL. Hey, check out my web application, machine learning

46:27 application. Streamlit is such a cool app that makes it so easy and so intuitive to make these

46:33 cool little apps that you could just put on your resume, put on your portfolio, send to recruiters. I'm

46:38 such a fan of the Streamlit app. I love it.

46:40 Yeah, it's super cool. There's a couple of those and Streamlit is definitely one of the really nice

46:44 ones there. There's also some hosting behind Streamlit as well these days, right? You don't even

46:51 have to set up a server or anything and just create it and put it up there.

46:54 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

46:59 is more customizable than Streamlit and can do quite a bit more, but it's a lot more work to deploy it.

47:06 It's more like programming.

47:07 Yeah, it is more programming.

47:09 Programming the UI rather than just the behind the scenes.

47:12 Yeah.

47:12 You have to do both and you have to know a little bit about systems and data engineering and stuff

47:18 like that versus Streamlit kind of takes that, abstracts that away. But yeah, back in the day,

47:22 I used to make Dash web applications and deploy them on Heroku back when they had a free tier of

47:27 hosting and they've taken that away. So I don't even know what the go-to free hosting platform is

47:32 nowadays. I just, I moved most of my things to Streamlit and it's so nice.

47:35 Yeah. We got Shiny for Python now, which is also nice.

47:38 I haven't checked that out. How is it?

47:40 I haven't done too much with it either, but Joe and the team over there are doing pretty cool stuff,

47:45 like adding more dynamic interactive stuff to Jupyter, like running inside Jupyter and things. Yeah,

47:50 pretty cool.

47:51 I'll have to check it out.

47:52 I think they also do a bunch of hosting stuff over there as well, is why it came to mind.

47:56 What other advice you got for folks out there?

47:58 So AI is AI, not studying AI or learning to use AI, machine learning, but is there a benefit of trying

48:05 to use ChatGPT to help you get this job or is there a danger? I'm thinking, for example, have ChatGPT

48:12 write me an awesome resume and then the tools are like, well, we've detected this is AI generated and

48:18 it's out. You know what I mean? What do you see happening there?

48:21 A lot of people see AI as like an all or nothing tool as in it's either you, the human doing the

48:29 work or it's the AI doing the work. But whenever, I don't know about you, but whenever I'm using

48:33 ChatGPT for anything, it's very rare it's copy and paste for me or at least not iterative where I'm

48:40 doing multiple prompts, prompt after prompt after prompt, trying to tweak it exactly what I want.

48:45 And so the way I look at ChatGPT and other gen AI that will be coming out, that's only inevitable,

48:50 is instead of looking at does this replace me? Does this, like for instance, am I going to build

48:55 my whole resume using ChatGPT? Can ChatGPT build, you know, take a data scientist's job and build the

49:02 whole model for them? I like to see it more as like a hammer. It's like a tool for the data scientist or

49:07 a tool for the job searcher to use in conjunction with your screwdriver or anything else. It's like

49:13 something to be wielded by a human, not replaced for the human, if that makes sense.

49:18 You know, it's really good for stuff like, hey, I know a regular expression will do this.

49:22 Yeah.

49:23 The last time I studied, I completely forgot what this is about. And I know it's gnarly,

49:27 but if I just ask, here's an example, here's what I want. Boom. And traditionally what you would end up

49:32 doing is you'd be on Stack Overflow. Yeah.

49:35 You'd be all over the internet. You'd be trying to piece it together from external information anyway.

49:38 And so code is something that's a little bit more in the wheelhouse of the generative AI,

49:44 because it can't really make it up as much. I know it could like do something insecure and you didn't

49:50 know it was or whatever, but it's not like asking for legal advice where it makes up cases that didn't

49:55 exist. Like it gives you code. You put it in the runtime of the compiler and it runs or it doesn't.

50:00 And the output comes like you did.

50:01 Yeah. It works or not.

50:02 Yeah. So it's pretty, pretty effective for that. But yeah, for resumes, I would be more like,

50:08 let me ask it. What are the in demand things? And if I know these three skills, what other skills should

50:14 I know to get a, you could sort of use it in an explorative way to then come up with what you

50:20 might write for yourself, right? Something like this.

50:22 I find it really useful for brainstorming like action verbs on your resume bullets. Like I think

50:27 it's really good at that. What's 10 different ways to say lead. So I don't say lead five times on my

50:33 resume and I use some different action bullets. I think it's great at that. I personally, it's pretty

50:38 rare that I start any Python code from scratch nowadays. I'm either starting hopefully from a

50:44 template that I've already written, or I'm starting from a ChatGPT. Like this is what I kind of want

50:49 to accomplish, right? Like the outline for it. Like one of the things I hate doing is I make a lot of

50:55 streamlet apps. I probably make a streamlet app a month right now. And I hate starting from scratch

50:59 with streamlet. It's super easy to start from scratch, but I'll say, Hey, ChatGPT, I want to

51:03 build a streamlet app. This is like the component I want here. This is the component I want here.

51:06 This is the component I want here. And it's almost like a warmup for me as a programmer. And it will

51:12 create something that works. It's not what I want. And I spend the next five hours trying to make it

51:18 what I want, you know, without ChatGPT, but it kind of gives me a warm start to my programming process.

51:23 So I really like it. I think it's something that everyone should use. And I think if you're thinking

51:29 about getting into any sort of programming, you know, whether it's data science or web development,

51:34 I think you should be a little bit less worried about it taking your job and job security. I think

51:40 you should almost be more excited that, wow, the bar has never been lowered to break into tech.

51:45 Like this is a step up gift from the programming gods that I get to use to break into tech.

51:51 Another thing to keep in mind is I imagine a lot of people listening to this podcast are not just

51:56 starting a college program, right? They're coming from possibly other experiences, other specialties.

52:03 You know, what's really good for job security, knowing the intersection of two things, the

52:07 intersection of chemistry and programming, the intersection of geology and programming for Exxon,

52:13 potentially, right? Like those things take you from a pool of a thousand to a pool of tens,

52:19 tens, right? And so what's awesome about that is it means two things. You don't throw away,

52:24 if you got a degree in something else like biology or whatever, you don't throw away like,

52:28 well, that was wasted four years. That's out. And it slices the pool of people who could apply for

52:33 certain jobs way, way smaller, right? Sounds like you agree.

52:36 Oh, a thousand percent. I'll just tell a quick little anecdote. When I was at ExxonMobil,

52:41 there's a lot of things I did not like at ExxonMobil, but this is something I really liked. It's about

52:46 once a quarter, they would do a crowdsourced data science competition for the whole organization,

52:51 like around the entire world. And they would say, this is a business problem we're trying to solve,

52:56 you know, and at Exxon, we have data scientists all over the world and like all sorts of different

53:00 teams and things like that. So I like did not know all the data scientists at Exxon. And they'd say,

53:05 this is the problem we're facing. Here's the data go, right? And I loved participating in these. It was

53:10 like right up my, my wheelhouse of like, I really enjoy exploration and all this stuff.

53:15 At the time I was getting my master's degree, but I didn't have my master's degree.

53:18 And I was competing against, so I'm just a chemical engineering grad, right? And I'm competing against

53:24 people with PhDs in computer science and in data science and all these like people who have way

53:29 more experience than me. And I actually won a few of these competitions. Thank you. I appreciate it.

53:35 And it's not because I was a better programmer or a better data scientist. It's because I majored in

53:40 chemical engineering and I knew the business problem, the domain extremely well. And I kind

53:46 of knew the programming and the data science stuff, but the combination of them made me very valuable.

53:51 Like one of the best examples I have is we're looking at crude oil properties. And I remember

53:56 like there was a forum where you'd like ask your questions. And one of the, one of the data scientists

54:01 asked, Hey, is sulfur bad? There's lots of sulfur in this. Is it bad? And like to a chemical engineer,

54:06 that's like the most obvious thing. No, you, yes. Sulfur is very bad in crude oil. That's very,

54:11 no, no, that's like such a fundamental thing to me and to him or her. That was like groundbreaking.

54:17 And so, yeah, your domain can become your superpower in your career.

54:20 Yeah. And it makes it way harder for ChatGPT and other types of tools to just automate you out of a

54:26 job because you bring in all these skills together, which is awesome. But it also makes it easier for

54:31 you to get the job. It makes it easier for you to continue your momentum of whatever you've been up

54:35 to. It's just, it's good all around. Yeah. I think it's more fun too, because once again,

54:39 like when I was trying to decide if I should study computer science, I was like, man, I don't really want to

54:45 to build an Excel workbook for building an Excel workbook sake. That's still true for me today.

54:52 I don't want to do data science for data science sake. I only like machine learning or data science

54:57 when I'm doing it to solve a really fun problem I'm passionate about. That's where it's more fun.

55:01 So if you can be excited about the domain and excited about the algorithms, I think that's a

55:05 great place to be. Absolutely agree. All right. We're getting short on time,

55:09 but maybe tell us a bit about your data career jumpstart. You've referred to it a couple of times.

55:14 Yeah. I have a company called data career jumpstart. I just try to do a lot of education. So the

55:18 education happens on LinkedIn happens on YouTube. And I actually forgot to mention this at the

55:23 beginning, but I have my own podcast called the data career podcast, where I help people land their

55:28 first data job. We're about at a hundred episodes. So not quite the groundwork that you've put in.

55:33 That's still a ton. That's awesome.

55:35 Yeah, we're getting there. And then, yeah, I also have a bootcamp where I try to affordably help

55:40 people land their first data analyst position by teaching them the skills, the networking and the

55:46 project and portfolio building that they need to do something.

55:50 Like the long version of this show.

55:51 Yeah. Basically. Well, yeah. Just take what we talked about today, expand on it,

55:56 make it like 350 unique lessons. And that's exactly what it is.

56:00 Yeah. Very cool. All right. Well, we're about out of time. So maybe just every final call to action,

56:06 people maybe are inspired. I see Dave go out in the audience. That's an awesome talk. Very much so.

56:11 What's next. It's easy to be inspired, but you got to take action.

56:15 Yeah. I love that. I think it's always fun to listen to podcasts, but you probably benefit way

56:21 more from the action you take after a podcast. So for you guys who are maybe interested in a data

56:26 analytics or a data science career, explore that. If you're like, yes, I'm in, make a plan, make a

56:32 roadmap. If you need help, I have a webinar that will help you make a roadmap. What skills should you

56:36 learn? How should you be networking and stuff like that? But really probably if you're just getting

56:40 started trying to figure out what skills you should learn, like what are the top skills that you should

56:44 be learning and then learning those skills and then not only learning those skills, but take action and

56:48 learning and build some sort of a project that we talked about that you could put on a portfolio,

56:52 make a streamlet app or something like that. That's probably the best action you could possibly take.

56:57 If you need any ideas, advice, feel free to check out my website, datacareerjumpstar.com or the

57:02 podcast data career podcast. Hopefully there's a lots of free resources for you guys to check that out.

57:07 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.

57:13 That's one of the huge, huge differentiators is like, you might be inspired, but you just got

57:18 to start taking those steps and it becomes a snowball. So thanks for sharing all your experience and your

57:23 advice. Hopefully some people out there are taking action and yeah, I'll put everything we talked about

57:28 in the show notes, of course. So thanks for being here, Avery.

57:30 Yeah. Thank you. Thanks for having me. I appreciate it.

57:32 You bet. Bye all.

57:33 This has been another episode of Talk Python to Me.

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59:01 to our YouTube channel at talkpython.fm/youtube. This is your host, Michael Kennedy. Thanks so much

59:08 for listening. I really appreciate it. Now get out there and write some Python code.

59:20 I'll see you next time. I'll see you next time.