WEBVTT 00:00:00.001 --> 00:00:03.500 Are you interested in data science, but you're not quite working in it yet? 00:00:03.500 --> 00:00:08.340 In software, getting that very first job can truly be the hardest one to land. 00:00:08.340 --> 00:00:13.560 On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice 00:00:13.560 --> 00:00:20.000 for getting your first data job. This is Talk Python To Me, episode 455, recorded January 18th, 00:00:20.000 --> 00:00:21.080 2024. 00:00:21.080 --> 00:00:40.560 Welcome to Talk Python To Me, a weekly podcast on Python. This is your host, Michael Kennedy. 00:00:40.560 --> 00:00:45.640 Follow me on Mastodon, where I'm @mkennedy, and follow the podcast using @talkpython, 00:00:45.640 --> 00:00:51.620 both on fosstodon.org. Keep up with the show and listen to over seven years of past episodes 00:00:51.620 --> 00:00:57.940 at talkpython.fm. We've started streaming most of our episodes live on YouTube. 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Try Posit Connect 00:01:34.400 --> 00:01:39.180 for free by going to talkpython.fm/Posit, P-O-S-I-T. 00:01:39.180 --> 00:01:44.800 Hey folks, before we jump in and talk about data science jobs and careers, I want to tell you 00:01:44.800 --> 00:01:50.680 really quickly about some awesome news. Back in February, I gave the keynote at PyCon Philippines. 00:01:50.680 --> 00:01:58.140 It was entitled The State of Python in 2024. Well, that is now out on YouTube. The team at 00:01:58.140 --> 00:02:02.340 PyCon Philippines did a great job. The video came out great. If you want to check out The 00:02:02.340 --> 00:02:07.060 State of Python in 2024, according to me, just click on the link in the show notes to watch 00:02:07.060 --> 00:02:11.740 it over on YouTube. Now let's talk to Avery. Avery, welcome to Talk Python To Me. 00:02:11.740 --> 00:02:15.160 Thanks so much. I'm so excited to be here and be part of the show. 00:02:15.160 --> 00:02:19.780 I'm excited to have you here as well. You know, one of the things that people reach out to me often 00:02:19.780 --> 00:02:26.300 is how do you get into data science? How do you get into programming? How do you get into Python? 00:02:26.300 --> 00:02:32.940 You know, I've been trying, or maybe they got a degree or they took some training program, 00:02:32.940 --> 00:02:39.940 bootcamp or something. And going from zero to one, I think is the biggest career step you have to make. 00:02:40.080 --> 00:02:45.340 That next job and the one after that, it only gets to be smaller steps, not bigger steps. 00:02:45.340 --> 00:02:49.840 And it's really tough because that first big step, you're brand new at it. You have no experience, 00:02:49.840 --> 00:02:54.860 right? It's your first data science job or your first programming job. And so hopefully we can 00:02:54.860 --> 00:02:59.380 give some folks out there a little bit of a hand up to help them make that jump. 00:02:59.380 --> 00:03:05.800 Yeah, totally. I like to show this graphic that says, it's a circle and it's a circle of text. And it says, 00:03:05.800 --> 00:03:11.580 I can't get a job because I don't have experience because, and then it restarts, 00:03:11.580 --> 00:03:15.980 I can't get a job. And that's the tricky part. It's like, how do you get a data science job when 00:03:15.980 --> 00:03:20.720 you have no data science experience? Because to get data science experience, that seems like you have 00:03:20.720 --> 00:03:25.920 to have a job as the prerequisite and vice versa. So it is very tricky. So happy to chime in on that 00:03:25.920 --> 00:03:26.100 today. 00:03:26.100 --> 00:03:32.340 The industry can take it too far. They can take it way too far. So a few years ago, there was a 00:03:32.340 --> 00:03:36.280 really funny tweet that went around back when they call them tweets. I don't know what they're called 00:03:36.280 --> 00:03:44.620 anymore. Sebastian Ramirez, the guy who created FastAPI, saw a job posting. When FastAPI was like a 00:03:44.620 --> 00:03:50.160 year and a half old, it said, you must have four years of experience with FastAPI to apply. He said, 00:03:50.160 --> 00:03:55.900 hey, look, I'm the creator of FastAPI and I'm unqualified for this job. What kind of world are 00:03:55.900 --> 00:03:56.520 we living in? 00:03:56.520 --> 00:04:01.980 Yeah. I don't want to live in that world, but that's unfortunately where we're at. That's so tough. 00:04:01.980 --> 00:04:06.220 And it's hilarious. These job descriptions are getting out of hand. That's for sure. 00:04:06.220 --> 00:04:08.420 Yeah. Well, with AI, it's probably not going to get better. 00:04:08.420 --> 00:04:13.400 We could talk about that more later. But before we get into that, let's just jump in with a little 00:04:13.400 --> 00:04:18.600 bit of background on you before we get to the topic. Tell us a bit about yourself. What do you do? 00:04:18.940 --> 00:04:20.160 How'd you get into Python? Things like that. 00:04:20.160 --> 00:04:27.620 Yeah, absolutely. So I'm currently a data science consultant and also a data science instructor. 00:04:27.620 --> 00:04:34.140 I run some online programs where I teach people to become data analysts mostly is what I'm focused on. 00:04:34.140 --> 00:04:39.260 But I also have this practice where I help companies solve data problems with different techniques. 00:04:39.260 --> 00:04:44.480 I started actually by studying chemical engineering in college in my undergraduate degree. 00:04:45.000 --> 00:04:50.660 And about a semester in, I realized, crap, I hate this. This is not for me. 00:04:50.660 --> 00:04:54.280 But I was a little on a little of a tough. Yeah. Do you agree? Have you felt something similar? 00:04:54.460 --> 00:04:59.160 I did a semester of chemical engineering as well. I thought, I love chemistry. I love math. Put them 00:04:59.160 --> 00:05:04.560 together. Somehow they don't go together. It's like ice cream and eggs or something. No, 00:05:04.560 --> 00:05:06.140 they don't go together for me at least. 00:05:06.140 --> 00:05:10.180 Yeah. It wasn't good for me either. I was just like, oh man, I'm actually not interested 00:05:10.180 --> 00:05:16.480 in refineries or like manufacturing. But I, like you, liked chemistry. I liked math. I thought this 00:05:16.480 --> 00:05:21.060 is perfect. But I quickly realized, oh man, I really liked this whole programming part that I get to do 00:05:21.060 --> 00:05:27.300 in MATLAB at the time when I was an undergrad. And I was on a time crunch to get through college kind of 00:05:27.300 --> 00:05:33.720 quickly through eight semesters. And the other issue I had was I didn't know what to do instead. It was 00:05:33.720 --> 00:05:37.940 like, I don't really want to study computer science. Part of the reason why is they kind of 00:05:37.940 --> 00:05:43.840 had this weed out course at the beginning, which you had to build Excel from scratch, basically like 00:05:43.840 --> 00:05:48.620 some sort of a spreadsheeting tool. And I was like, why would I rebuild something that already exists 00:05:48.620 --> 00:05:52.440 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 00:05:52.440 --> 00:05:58.920 what to do. And luckily I was working as a lab technician at this company, the really cool company 00:05:58.920 --> 00:06:04.880 that makes the sensors that basically have the ability to smell. So they can sniff what's in 00:06:04.880 --> 00:06:09.300 the air and it has applications for finding drugs or bombs and airports and stuff like that. 00:06:09.300 --> 00:06:14.780 And there was a data scientist on staff and that data scientist was awesome. He was like showing me 00:06:14.780 --> 00:06:20.280 all these cool algorithms he was writing for these sensors. And then one day he got up and left and he 00:06:20.280 --> 00:06:26.000 left the company and we tried to hire another data scientist for like six months, but they were really 00:06:26.000 --> 00:06:30.520 expensive. We were a small company and none of them really wanted to move to Utah where I lived in 00:06:30.520 --> 00:06:33.800 Salt Lake City. And so we couldn't, we couldn't really find someone that would be able to do it. 00:06:33.800 --> 00:06:38.120 And so finally I was like, well, I really liked this programming stuff. And I, you know, the data 00:06:38.120 --> 00:06:42.500 scientist showed me a thing or two, maybe I could take a stab at this. And I started, I wrote like my 00:06:42.500 --> 00:06:46.980 first machine learning algorithm and I was like, oh my gosh, I'm addicted to this. And then I never 00:06:46.980 --> 00:06:49.440 looked back and had been data science since basically. 00:06:49.440 --> 00:06:55.100 What a great story. Yeah. I think, I think a lot of people fall into programming that way. And for some reason, 00:06:55.460 --> 00:07:00.720 not unexpectedly, but for some reason, a lot of people fall into Python that way as well. They're 00:07:00.720 --> 00:07:05.900 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 00:07:05.900 --> 00:07:10.200 like an Excel spreadsheet or something and put it together. And you're like, actually, this is cool. 00:07:10.200 --> 00:07:14.700 After a while, like, this is cooler than what I've been doing, or maybe I'll make it a good part of what 00:07:14.700 --> 00:07:15.300 I do. Right. 00:07:15.300 --> 00:07:20.860 Yeah. A hundred percent. Even just making, it was in MATLAB, which is basically engineers version 00:07:20.860 --> 00:07:26.380 of Python or college version of Python 10 years ago. Right. And I made like tic-tac-toe and I remember 00:07:26.380 --> 00:07:29.620 playing tic-tac-toe against the computer. I think that's what it was. Or maybe it was, 00:07:29.620 --> 00:07:33.540 maybe it was Hangman. I can't remember. But I remember like the idea of like being able to play, 00:07:33.540 --> 00:07:38.800 to program games and play against the computer. And I built it. I was like, this is the coolest thing 00:07:38.800 --> 00:07:40.280 ever. I got to, I got to do more of this. 00:07:40.460 --> 00:07:44.780 Absolutely. You know, I think I've done some MATLAB too, when I was younger and it's not that 00:07:44.780 --> 00:07:49.660 different from Python, but it's, I think one of the big differences other than it just being like 00:07:49.660 --> 00:07:54.460 embedded in a big expensive app is it's not a general purpose programming language, right? You 00:07:54.460 --> 00:08:00.380 wouldn't go, you know, that was fun, but let me go build this website in MATLAB or let me create 00:08:00.380 --> 00:08:06.220 Airbnb and MATLAB or, you know, like there's, you just don't want to sort, Azure has this like 00:08:06.220 --> 00:08:08.860 self-prescribed limit to what you can do with it. 00:08:08.860 --> 00:08:13.740 That's one of the coolest parts about Python is it's really a Swiss army knife and you can pretty 00:08:13.740 --> 00:08:19.100 much do, I don't want to say anything, but pretty close to anything in Python, which makes it really 00:08:19.100 --> 00:08:23.660 neat. And obviously one of the huge limitations of MATLAB is one, it costs thousands of dollars, 00:08:23.660 --> 00:08:27.020 but two, you're right. It's not going to do cybersecurity for you. It's not going to build 00:08:27.020 --> 00:08:32.300 websites, but the syntax at the end of the day was, was really quick. It was, it was easy for me to 00:08:32.300 --> 00:08:35.980 transition from MATLAB to Python because the syntax isn't all that different. 00:08:35.980 --> 00:08:41.260 No, it's not all that different. More math focused, but pretty similar. So I think maybe that's a good 00:08:41.260 --> 00:08:46.220 place to start discussing and exploring the topic of your first data science job. And 00:08:46.220 --> 00:08:50.860 wouldn't necessarily plan on starting here, but let's, let's start with before you even necessarily know 00:08:50.860 --> 00:08:57.660 programming language, right? Maybe you've dabbled in MATLAB or you've dabbled in Excel or even dabbled in, 00:08:57.660 --> 00:09:02.700 I don't know, JavaScript or something. This thing we've been talking about with MATLAB and it applies to other areas as well, 00:09:02.700 --> 00:09:08.940 like through programming languages per se, like Julia or something like that, is how, 00:09:08.940 --> 00:09:16.220 if you invest your time into learning one of these things really well, like how broadly industry-wide 00:09:16.220 --> 00:09:21.420 of a skill, high demand skill is that going to be, right? If you learn MATLAB, you put yourself in a box, 00:09:21.420 --> 00:09:25.420 you learn a more general programming language, you kind of have more options afterwards, right? 00:09:25.420 --> 00:09:31.260 Yeah, totally. I think like the more broad of a language you learn, the more useful you are to, 00:09:31.260 --> 00:09:37.180 to more industries in general. But I might take that even a step further and just say, you know, 00:09:37.180 --> 00:09:43.340 learning MATLAB, not a whole lot of companies use MATLAB, but just like landing your first data job, 00:09:43.340 --> 00:09:48.460 going from zero to one is the hardest, learning your first language, zero to one is the hardest as well. 00:09:48.460 --> 00:09:52.940 And then once you have that first language, the next language becomes so much easier. So 00:09:52.940 --> 00:09:57.740 one of the first things I learned was MATLAB. And then I moved to Python and that was easier. And then 00:09:57.740 --> 00:10:02.540 I learned SQL and then I learned R and then I learned JavaScript. And every time I added like a new tool 00:10:02.540 --> 00:10:07.820 to my toolkit, it was quite, not almost, it was easy, but it got easier with each one. I think that's true 00:10:07.820 --> 00:10:12.940 with foreign languages as well. Once you learn one foreign language, then the third and the fourth become 00:10:12.940 --> 00:10:17.980 quite easy. At least that's, that's what I heard. I speak kind of through two and a half languages, 00:10:17.980 --> 00:10:21.500 but like I, there's people who speak like seven and they always say like the sixth and the seventh 00:10:21.500 --> 00:10:22.060 become easier. 00:10:22.060 --> 00:10:25.340 Yeah. You wonder how could you probably, because learning the first one is so hard, 00:10:25.340 --> 00:10:29.420 first foreign language. So you're like, well, how could you possibly take that on for this many 00:10:29.420 --> 00:10:32.300 languages? And it's that it's not the same challenge each time, right? 00:10:32.300 --> 00:10:32.860 Yeah, exactly. 00:10:32.860 --> 00:10:38.140 Yeah. So I think when people are considering getting into data science, they really want to consider 00:10:38.140 --> 00:10:42.540 what language they choose and where they go. Like you're coming out of a 00:10:42.540 --> 00:10:47.020 college program. You might feel like MATLAB or something like that's real popular. And yet 00:10:47.020 --> 00:10:51.740 that's because it's popular amongst professors who forced their students to do it. That doesn't 00:10:51.740 --> 00:10:57.020 necessarily mean that's the world, the broad worldview. What do you think about R? You know, both. 00:10:57.020 --> 00:11:03.020 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 00:11:03.020 --> 00:11:08.700 a lot on LinkedIn, kind of a LinkedIn guy. And so a lot of the times, honestly, just for jokes and kicks 00:11:08.700 --> 00:11:13.180 and giggles, I'll kind of roast R on LinkedIn just to get the trolls angry in the comments. 00:11:13.180 --> 00:11:18.780 I've invented it. It's quite fun. It's quite a fun experience, but I'm not that big of a hater. I 00:11:18.780 --> 00:11:23.180 think that's really interesting about R versus Python is obviously a big debate in the data 00:11:23.180 --> 00:11:29.420 science community is R is kind of that does one thing really well. And it's getting a little less 00:11:29.420 --> 00:11:34.860 of that as like more packages and libraries are added to R, but R does the statistics and machine 00:11:34.860 --> 00:11:39.660 learning very well. But obviously I don't think once again, I don't know any websites, any like 00:11:39.660 --> 00:11:44.060 super functioning websites that are built on R. I don't know any cybersecurity that's really done 00:11:44.060 --> 00:11:49.580 done, done VR. So I think R does what it does well. The syntax sometimes is a lot easier for 00:11:49.580 --> 00:11:54.540 people to go from Excel, which a lot of people are more familiar with in the finance or banking world, 00:11:54.540 --> 00:12:00.460 for example. The syntax in R is a little bit more similar to those Excel formulas than it is to Python. 00:12:00.460 --> 00:12:04.540 So I think sometimes people have a little bit more success just because, oh, this kind of feels like 00:12:04.540 --> 00:12:10.140 our formulas are sorry. This feels like Excel formulas. And so people really get there. I think what 00:12:10.140 --> 00:12:14.700 you're kind of alluding to is if you're going to learn one skill, you might as well learn the one 00:12:14.700 --> 00:12:20.220 skill that's applicable to the most, the widest net, right? And so that way you're fishing in the 00:12:20.220 --> 00:12:27.260 biggest lake you possibly could versus in a smaller pond of R. I think that's worth looking at. And one 00:12:27.260 --> 00:12:31.180 of the things I actually really enjoy doing, because you know, you mentioned, oh, you might think MATLAB 00:12:31.180 --> 00:12:35.580 is popular because that's what the professors taught you. And there's actually not a whole lot of 00:12:35.580 --> 00:12:41.100 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 00:12:41.100 --> 00:12:45.580 analyst YouTuber. I was going to say YouTuber on YouTube, but that's kind of redundant on YouTube. And 00:12:45.580 --> 00:12:50.940 one of the things he's done is he's actually built this tool where he's web scraping thousands of jobs, 00:12:50.940 --> 00:12:56.780 different data jobs every week, and then displaying and analyzing the skills required for those jobs. 00:12:56.780 --> 00:13:01.340 So it's actually like a data driven way of saying, if you want to be a data scientist, what skills should you 00:13:01.340 --> 00:13:07.660 actually be focusing on as you go, as opposed to just listening to what a professor will say, 00:13:07.660 --> 00:13:13.820 or what a LinkedIn influencer will say, or what your bootcamp will say. Like actually getting some 00:13:13.820 --> 00:13:19.660 data on, I think is pretty neat. That is super cool. And I'm not familiar with Luke. So we're going to dig 00:13:19.660 --> 00:13:23.420 him up and put him in the show notes for later so people can check that out. For sure. Do you remember 00:13:23.420 --> 00:13:27.980 any of the trends you've recently talked about? It's datanerd.tech, I think is the website there. 00:13:27.980 --> 00:13:33.180 I look at it mostly for data analysts because that's who I work with the most. So I know the 00:13:33.180 --> 00:13:40.460 data analyst data very well. SQL is number one at 50%. I think Python is number two at like 30%. 00:13:40.460 --> 00:13:47.180 I think Python might've jumped it. Well, this is for all data positions right here. So the job title, 00:13:47.180 --> 00:13:51.180 you can choose. So which one do you think I should pick here? Data? 00:13:51.180 --> 00:13:53.820 Maybe data scientist. Data scientist. Yeah. Right. 00:13:53.820 --> 00:13:56.380 What's that? Yeah, you're right. Wow. 00:13:56.380 --> 00:14:04.060 Whoa, Python 69%. Look at that. That's huge. So like, that's even, that's even what? 20% more than SQL, 00:14:04.060 --> 00:14:07.020 which a lot of people are like, if you were going to be a data scientist, you have to know SQL. 00:14:07.020 --> 00:14:11.660 Yeah. If you look at the job descriptions, Python's mentioned a lot more. So if you're going to learn, 00:14:11.660 --> 00:14:15.420 if you're brand new and you're going to learn one, you might as well start with Python. Because that's 00:14:15.420 --> 00:14:18.460 probably the most in demand skill that there is right now for a data scientist. 00:14:18.460 --> 00:14:22.860 Yeah. And it's pretty easy, right? It's not like, well, why don't you just learn C++ for 00:14:22.860 --> 00:14:26.700 embedded devices? You're like, you know what? Maybe I'll pick something else to start with. Right. But 00:14:26.700 --> 00:14:31.020 you know, Python's pretty easy. I agree with you. I think Python's great. I actually think, 00:14:31.020 --> 00:14:35.980 I think SQL is probably easier to learn if I'm being honest, because really, especially for like 00:14:35.980 --> 00:14:40.380 data science stuff, there's only about like 20 commands that you need to know in SQL. But it's, 00:14:40.380 --> 00:14:44.780 once again, SQL's a lot more, there's no websites built on SQL. I'll tell you that much. So 00:14:44.780 --> 00:14:46.380 it's a lot more limited on what it can do. 00:14:46.380 --> 00:14:52.060 It's a skill, but not the language. It's not enough on its own, generally. I mean, 00:14:52.060 --> 00:14:57.100 you can do reports and quite a bit with it. But you know, it's like, when you see these programming 00:14:57.100 --> 00:15:02.700 popularity, like what's the most popular language? Oh, look, CSS is the third most popular. That's not 00:15:02.700 --> 00:15:06.700 a language. That's a thing that you use with other languages, right? Like use it with all the other 00:15:06.700 --> 00:15:11.180 languages. That's why it's high up. But that doesn't mean it's high in demand. Exactly. It's just like 00:15:11.180 --> 00:15:12.780 table stakes, you know? Yeah. 00:15:12.780 --> 00:15:17.020 So you kind of got to distinguish table stakes from like picking an area, I think. 00:15:17.020 --> 00:15:22.700 That's totally true. And really, I think Pythonistas could make the argument that there's 00:15:22.700 --> 00:15:28.060 really nothing in SQL that you couldn't do in Python. That's a little somewhat true, 00:15:28.060 --> 00:15:32.940 true depending on data size and stuff like that. But regardless, there is ways that you can do 00:15:32.940 --> 00:15:36.140 most of the SQL commands in Python one way or another. Yeah. Yeah. 00:15:36.140 --> 00:15:41.980 It could be when I first became a data scientist, I didn't even know SQL and I was doing SQL commands 00:15:41.980 --> 00:15:47.020 or I was doing the aggregations or the where functions or the window functions using Python. 00:15:47.020 --> 00:15:51.260 So you definitely can. As long as your data is not like super big, then you'll totally be fine. 00:15:51.260 --> 00:15:57.660 Right. Like some kind of generator or even slices or yeah, things like that, right? List comprehensions, 00:15:57.660 --> 00:16:02.940 set comprehensions, all that kind of stuff. Kind of like, gosh, I really wish, a little bit of a 00:16:02.940 --> 00:16:08.620 sidebar, but I wish like list comprehensions and all those things had just a few more SQL features, 00:16:08.620 --> 00:16:14.060 right? Like in a list comprehension, I say, give me this thing, maybe give me this property of this class 00:16:14.620 --> 00:16:20.620 modified, like give me the user's name, uppercase. Right. So that's like select. And then for thing 00:16:20.620 --> 00:16:27.020 in collection, that's like from table or whatever. Right. And then you have the where clause with the 00:16:27.020 --> 00:16:33.820 if statement, but boy, wouldn't it be cool to have like a sort also in there and other things like that, 00:16:33.820 --> 00:16:38.460 you know? Oh, well, totally. It's so close. The cool thing is, is if you want that sort, 00:16:38.460 --> 00:16:43.740 it's what one extra line. Like it's, it's not, it's not too bad. So it, Python, I mean, 00:16:43.740 --> 00:16:47.820 I don't want to say this necessarily to hate all, to make all the data scientists and SQL lovers 00:16:47.820 --> 00:16:52.540 mad, but, but really Python can do a lot of the things that SQL that's for sure. 00:16:52.540 --> 00:16:53.340 Yeah, that's for sure. 00:16:53.740 --> 00:17:00.380 This portion of talk Python to me is brought to you by Sentry code breaks. It's a fact of life with Sentry. 00:17:00.380 --> 00:17:06.860 You can fix it faster. As I've told you all before, we use Sentry on many of our apps and APIs here at 00:17:06.860 --> 00:17:12.780 Talk Python. I recently used Sentry to help me track down one of the weirdest bugs I've run into in a long 00:17:12.780 --> 00:17:19.340 time. Here's what happened. When signing up for our mailing list, it would crash under a non-common execution 00:17:19.340 --> 00:17:25.100 past, like situations where someone was already subscribed or entered an invalid email address or 00:17:25.100 --> 00:17:32.460 something like this. The bizarre part was that our logging of that unusual condition itself was crashing. 00:17:32.460 --> 00:17:39.020 How is it possible for her log to crash? It's basically a glorified print statement. Well, Sentry to the 00:17:39.020 --> 00:17:44.300 rescue. I'm looking at the crash report right now, and I see way more information than you'd expect to 00:17:44.300 --> 00:17:48.860 find in any log statement. And because it's production, debuggers are out of the question. 00:17:48.860 --> 00:17:56.700 I see the traceback, of course, but also the browser version, client OS, server OS, server OS version, 00:17:56.700 --> 00:18:01.660 whether it's production or Q and A, the email and name of the person signing up. That's the person who 00:18:01.660 --> 00:18:06.220 actually experienced the crash. Dictionaries of data on the call stack and so much more. What was the 00:18:06.220 --> 00:18:14.300 problem? I initialized the logger with the string info for the level rather than the enumeration dot info, 00:18:14.300 --> 00:18:20.220 which was an integer based enum. So the logging statement would crash saying that I could not use 00:18:20.220 --> 00:18:27.820 less than or equal to between strings and ints. Crazy town. But with Sentry, I captured it, fixed it, 00:18:27.820 --> 00:18:33.900 and I even helped the user who experienced that crash. Don't fly blind. Fix code faster with Sentry. 00:18:33.900 --> 00:18:39.340 Create your Sentry account now at talkpython.fm/sentry. And if you sign up with the code 00:18:39.340 --> 00:18:46.060 TALKPYTHON, all capital, no spaces, it's good for two free months of Sentry's business plan, 00:18:46.060 --> 00:18:49.900 which will give you up to 20 times as many monthly events as well as other features. 00:18:51.900 --> 00:18:57.660 Probably the biggest, it's a bit of a diversion, but the biggest similarity to that I've seen in 00:18:57.660 --> 00:19:04.460 the languages is C#'s link where they actually have almost all the query operators, including 00:19:04.460 --> 00:19:09.580 joins and stuff like that built into the programming language. I'd love to see more of that kind of 00:19:09.580 --> 00:19:13.420 inspiration into Python, but you know, that's all right. It's still really good. I've got a lot of 00:19:13.420 --> 00:19:17.900 cool SQL-like features, but you're right. Once you are no longer working with data and memory, 00:19:17.900 --> 00:19:24.700 or you want indexes, right? Like this concept of indexes is not sufficiently well understood. I think 00:19:24.700 --> 00:19:28.940 every time I hit a website that takes five seconds to load, I'm like, somebody is not doing all the 00:19:28.940 --> 00:19:34.220 things they should be doing. I just know it. That's totally true. What about SQL? You know, 00:19:34.220 --> 00:19:40.460 let's talk about that for a bit, right? The SQL, the query language or databases and other things, 00:19:40.460 --> 00:19:47.580 there's ways to SQL query, not just relational databases. But you said you got away with not 00:19:47.580 --> 00:19:52.300 quite learning that, but do you think if you could start over, maybe making an effort to learn that 00:19:52.300 --> 00:19:56.780 would be really valuable? Like how, how important is this a thing in the beginning of your career? 00:19:56.780 --> 00:20:03.100 The interesting thing, you know, about landing a data job is your skills only plays, I say, 00:20:03.100 --> 00:20:09.020 a third of the role. Your portfolio or the way that you portray your skills and your network, 00:20:09.020 --> 00:20:12.460 I think are the other two thirds and they're actually more important than your skills. And that's 00:20:12.460 --> 00:20:17.260 kind of how I got away with not knowing SQL and not even being, to be honest, that good at 00:20:17.260 --> 00:20:24.060 Python at the time was because I used my network to be in the situation to get my lab technician job 00:20:24.060 --> 00:20:28.700 in the first place. And then once again, I use that same network, in this case, my coworkers, 00:20:28.700 --> 00:20:33.980 to land that first data scientist position after we couldn't hire anyone. And if I would have been 00:20:33.980 --> 00:20:39.420 applying externally for that role, chances are I wouldn't have gotten that role. I probably didn't 00:20:39.420 --> 00:20:43.900 know enough at the time to land that type of a role, but because they knew I was hardworking, 00:20:43.900 --> 00:20:49.180 they knew I wasn't like a total idiot and I really liked to learn. They took that chance on me. It 00:20:49.180 --> 00:20:53.900 paid off really well for them because at the time I was still in college. And so I wasn't getting paid 00:20:53.900 --> 00:20:58.460 that much. And I was getting, I was not getting paid like a data scientist, but I was getting results like 00:20:58.460 --> 00:21:03.260 a data scientist for them. So I think it was, it paid off for both of us. But I think if that was an 00:21:03.260 --> 00:21:07.500 external job and I applied for it, I probably didn't have enough skills for it. So I definitely think 00:21:07.500 --> 00:21:12.620 learning SQL, if you want to land data science job, isn't a bad place to start, especially because, 00:21:12.620 --> 00:21:17.420 like I said, there, I mean, any programming language, I like to think of like the iceberg, 00:21:17.420 --> 00:21:21.740 kind of like the Titanic, right? There's the parts that you see, and then there's the parts that you, 00:21:21.740 --> 00:21:26.860 that you don't even know that you, that are there. And, and really you could spend the rest of your life 00:21:26.860 --> 00:21:31.500 trying to master SQL or the rest of your life trying to learn Python. But the cool thing is, 00:21:31.500 --> 00:21:34.620 is a lot of the time you only need that top little bit that's sitting at the, 00:21:34.620 --> 00:21:40.060 the top of the surface of the water to actually get stuff done. And so for SQL, I think that's like 00:21:40.060 --> 00:21:45.420 20 commands. And I think you could learn it honestly in like a month, you could learn those, 00:21:45.420 --> 00:21:49.980 those 20 commands pretty easily, but it worked out for me. And I didn't have to use it that much 00:21:49.980 --> 00:21:54.620 at the time until I was probably about almost three years into my job. And I actually had switched jobs 00:21:54.620 --> 00:21:58.940 to a bigger company. The other thing that I was working for a smaller company where we didn't have a ton 00:21:58.940 --> 00:22:05.020 of data. So we could use CSVs kind of as our, our database, which is not great practice. But when I, 00:22:05.020 --> 00:22:09.340 when I eventually became a data scientist at Exxon mobile, I was going to say they didn't use Excel as 00:22:09.340 --> 00:22:14.460 a database, but they still did. But the point is they had much larger SQL databases with hundreds of 00:22:14.460 --> 00:22:17.740 thousands, actually millions of rows of data that I had to query. 00:22:17.740 --> 00:22:22.620 Yeah. Then you gotta be really, you need to understand it at a much deeper level. You're 00:22:22.620 --> 00:22:26.700 like, if you do a query like this, it's going to be super slow. But if you do it like that, 00:22:26.700 --> 00:22:30.060 it can use the composite index for the sort and then blah, blah, blah, blah. All right. Then 00:22:30.060 --> 00:22:33.740 you're getting to the bottom of the iceberg in SQL, or maybe not the bottom, 00:22:33.740 --> 00:22:37.500 maybe like the middle chunk under the water, but there's so much to learn for both of them. 00:22:37.500 --> 00:22:42.300 Amir at the audience asks, you know, like when you talk data job, like what kind of jobs are out 00:22:42.300 --> 00:22:46.700 there? Right. So we talked to both about how we did chemical engineering and then we saw like 00:22:46.700 --> 00:22:49.420 chemical factories, like, yeah, I don't really want to work here anymore. I'm out. 00:22:49.420 --> 00:22:55.740 So thinking about like, well, what are the kinds of jobs you do? I think that's really important because 00:22:55.740 --> 00:23:02.220 it's easy to get focused in on like the FANG companies. Like I want to work for like some super 00:23:02.220 --> 00:23:07.660 big tech company. I want to move to San Francisco and like that, that, that, right. Like there's not just 00:23:07.660 --> 00:23:12.140 plenty of other jobs, but the opportunities, just like you described, and as well as 00:23:12.140 --> 00:23:17.260 like my first job, I worked at a company that had like eight people and it was awesome. Right. 00:23:17.260 --> 00:23:22.620 They didn't expect me to be, you know, running Kubernetes clusters and doing all sorts of great. 00:23:22.620 --> 00:23:26.140 They're just like, I need you to make this thing happen. Can you do like, I'm pretty new, 00:23:26.140 --> 00:23:30.700 but that thing I can make that happen. Like, let's go. Right. And I feel like the possibilities 00:23:30.700 --> 00:23:37.180 to get in, especially with these maybe more niche type of industries and companies might even be easier 00:23:37.180 --> 00:23:41.980 for a first job. People seem to be really obsessed with, with the FANG. And I don't know if that's 00:23:41.980 --> 00:23:46.300 like a societal thing, or if it's just, those are the companies that we use a lot. And so we're 00:23:46.300 --> 00:23:51.500 excited about them, but yeah, there's so many more data jobs outside of FANG than there are inside of 00:23:51.500 --> 00:23:57.100 FANG, even though there's, there's quite a bit inside of FANG. And oftentimes those roles can be 00:23:57.100 --> 00:24:02.300 much more interesting and you can do a lot bigger of an impact. When, when I was working at the small 00:24:02.300 --> 00:24:08.540 company, VaporSense, I like, I had so much power. I didn't even realize it. I had such a big effect on the 00:24:08.540 --> 00:24:14.700 company. I was presenting to, you know, Fortune 500 companies and what I did really made a difference. 00:24:14.700 --> 00:24:20.460 And when it came to the point where ExxonMobil offered me to go be a data scientist for Exxon, 00:24:20.460 --> 00:24:26.220 I said, Oh, I want to go work for the big company with the nice desk and the nice laptop and, you know, 00:24:26.220 --> 00:24:31.820 try something new. And when I got there, I really, I had some pretty cool opportunities when I was at 00:24:31.820 --> 00:24:36.620 ExxonMobil, but ultimately I left pretty shortly after two years of being there because I just felt like a 00:24:36.620 --> 00:24:40.620 cog in the machine and I didn't feel like I was actually making a difference. And that was really 00:24:40.620 --> 00:24:45.820 important to my work satisfaction of like, is what I'm doing being used? Is it being used to better the 00:24:45.820 --> 00:24:50.300 world? Do I feel valued? And the answer was kind of no for me when I was there. So there's definitely 00:24:50.300 --> 00:24:54.220 a trade-off between the small companies and the big companies, but also to go back to your original 00:24:54.220 --> 00:24:59.820 question, there's so many freaking roles in the data world that you're not even thinking of that. Like, 00:24:59.820 --> 00:25:04.140 I'm not even thinking of, I saw a new one the other day when I was helping one of my students. 00:25:04.140 --> 00:25:08.060 It was like, it wasn't data janitor, but it was something like that where I was like, I don't 00:25:08.060 --> 00:25:12.860 even know what that role is, but there's, there's so many roles. When I was, when I was a data scientist, 00:25:12.860 --> 00:25:19.180 VaporSense, the small company, my actual title was junior chemometrician, which basically means 00:25:19.180 --> 00:25:24.060 you're doing data science with chemistry. When I was at ExxonMobil, when I was first there, 00:25:24.060 --> 00:25:29.020 I was doing data science, but my actual title was optimization engineer. And so there's so many 00:25:29.020 --> 00:25:33.660 titles that we don't even think to search of, or even to look up, but those are all data science 00:25:33.660 --> 00:25:37.820 roles. I was doing machine learning every day in both those roles. And you would maybe never guess 00:25:37.820 --> 00:25:42.700 from those titles. Yeah. You would never guess. No, that's awesome. What machine learning libraries, 00:25:42.700 --> 00:25:46.220 frameworks were you using? At VaporSense, once again, because it's a smaller company, 00:25:46.220 --> 00:25:51.020 I had a lot more say in what I was doing. We were building a bunch of machine, we were building 00:25:51.020 --> 00:25:56.700 classification models to basically to take the data from our sensors and sniff if something was in the 00:25:56.700 --> 00:26:02.940 air. Sometimes that was a yes, no, like, oh yes, there is ammonia in the semiconductor factory and 00:26:02.940 --> 00:26:09.420 that's bad. So that's a yes classification kind of binary, right? Other times it was, what drug is this? 00:26:09.420 --> 00:26:15.420 Is this meth or is this heroin? One of the use cases we had was, this is binary once again, but is this 00:26:15.420 --> 00:26:20.380 recreational marijuana or medicinal marijuana? And can we tell the difference between, between those? 00:26:20.380 --> 00:26:26.780 So we are usually using classification models, usually built in scikit-learn in Python, the majority of the 00:26:26.780 --> 00:26:32.700 time there. When I was at Exxon, we had a lot less say, like the data scientists had a lot less say in 00:26:32.700 --> 00:26:39.340 the decision making process. We were doing a lot of multivariate linear regression with a lot of crazy 00:26:39.340 --> 00:26:44.220 hacks and transformations kind of in the meantime for one of my positions there. And then the other time, 00:26:44.220 --> 00:26:50.220 the other position I did there, we were doing a lot of auto ML using PyCaret and letting it kind of 00:26:50.220 --> 00:26:54.700 decide what type of models to do. So. Okay. The unsupervised learning type stuff, huh? 00:26:54.700 --> 00:27:00.460 It was awesome. It was really fun to, to, I love PyCaret because it's like, okay, go make 25 models and 00:27:00.460 --> 00:27:03.820 tell me which one's the best. It's like, takes, makes my job easy, I guess. 00:27:03.820 --> 00:27:08.940 We're going to be creative with sheer numbers. That's how we're going to come up with a solution. 00:27:08.940 --> 00:27:09.980 Got it. Exactly. 00:27:09.980 --> 00:27:16.060 Well, Diego is asking like, what are some of the common stats methods as in mathematical type stuff 00:27:16.060 --> 00:27:22.140 you would use? So one of the things I know that some people getting into programming think is you've 00:27:22.140 --> 00:27:26.860 got to be really good at math to be a programmer. I think you've got to be really good at logical 00:27:26.860 --> 00:27:33.020 thinking, but you need to almost zero math be like a web developer. You know, we're talking percents 00:27:33.020 --> 00:27:40.060 for CSS, incrementing numbers from one to two to two to three for IDs and stuff like that. But for 00:27:40.060 --> 00:27:45.100 data science, maybe there's a little bit more like, where do you see that kind of background? 00:27:45.100 --> 00:27:49.500 I like what you said, you have to think logically, but maybe the math isn't as important. And I think 00:27:49.500 --> 00:27:54.380 it's actually somewhat similar in data science. I will say you probably need a little bit more math 00:27:54.380 --> 00:27:59.340 than a web developer, but I think it's a lot less than most people think. And it's probably less 00:27:59.340 --> 00:28:04.540 about being able to do the math and maybe more about understanding the mathematical concepts. 00:28:04.540 --> 00:28:10.300 And what I mean by that is a lot of, a lot of, so I also have a master's degree in data analytics. 00:28:10.300 --> 00:28:15.660 A lot of master's degrees in data science and data analytics will say you need calculus and linear 00:28:15.660 --> 00:28:19.980 algebra as kind of a background for your math. And that kind of stops people. I don't want to do any 00:28:19.980 --> 00:28:24.540 calculus. I don't want to do any linear algebra. And while both those concepts do exist in data 00:28:24.540 --> 00:28:30.300 science principles, the majority of the time, the computer, Python is doing the math. You just have 00:28:30.300 --> 00:28:36.460 to be able to interpret the results of the math and kind of know what different directions, like this is 00:28:36.460 --> 00:28:40.860 going down, an optimization problem, you know, okay, that's the derivative, you know, getting closer to 00:28:40.860 --> 00:28:46.380 zero. Like it's really less about knowing how to do the math by hand and more just understanding what the 00:28:46.380 --> 00:28:50.460 math the computer is actually doing. So I think it's actually a lot easier than most people say. 00:28:50.460 --> 00:28:55.500 That being said, knowing how to do a derivative or taking integral, those concepts, I think is 00:28:55.500 --> 00:29:00.860 probably underlying pretty important. But other than that, like a lot of the times I'm doing linear 00:29:00.860 --> 00:29:06.780 regression because it's, it's awesome. It gets the job done. A lot of the time I'm doing hypothesis 00:29:06.780 --> 00:29:12.700 testing and statistics, which you have to look like at a P score, nothing all that crazy. At Exxon, 00:29:12.700 --> 00:29:17.660 I had to do a lot of linear programming, but that's honestly, that's like the exception versus the rule. 00:29:17.660 --> 00:29:22.300 There's not a whole lot of linear programming for most data science, most data scientists. So 00:29:22.300 --> 00:29:27.180 I really don't think the math is, is all that hard. Now, of course, that's coming from someone who 00:29:27.180 --> 00:29:32.780 got a chemical engineering degree, who had to take all the calculus, all the linear algebra. So I did go 00:29:32.780 --> 00:29:37.660 through those courses. I haven't really done it from scratch from like a lot of my students are teachers, 00:29:37.660 --> 00:29:41.820 for example, who never took those courses in college. So I can't speak from that perspective, 00:29:41.820 --> 00:29:45.740 but a lot of my students are able to figure it out at the end of the day and transfer. So it happens. 00:29:45.740 --> 00:29:50.460 Yeah, yeah, for sure. I think there, you make a good point. I think it's about knowing, 00:29:50.460 --> 00:29:56.780 okay, this formula or this algorithm or this test means this thing. It applies in this situation. 00:29:56.780 --> 00:30:00.860 It doesn't apply in that situation. Here's what you're trying to get from it, right? Like, 00:30:00.860 --> 00:30:06.620 I know I need to do a fast Fourier transform. So, and this is what it tells me when I get out the other 00:30:06.620 --> 00:30:14.540 side. But do I need to be able to sit down and recreate the integral and the calculus behind it 00:30:14.540 --> 00:30:18.940 and do that on like a home, like as a homework example, like, give me a function and I'll do the 00:30:18.940 --> 00:30:23.900 Fourier transform and I'll actually do the symbolic integration. Like, no, you probably don't need that, 00:30:23.900 --> 00:30:29.580 right? But you need to know, I do the Fourier transform in this situation and this is why. And then I just say, 00:30:30.300 --> 00:30:35.020 call the function, do it, right? And interpret the results. Really, that's what being a data 00:30:35.020 --> 00:30:40.060 scientist is all about is, yeah, what does the business use case, what's the desired business 00:30:40.060 --> 00:30:45.980 use case? How do I relate that use case to the data? What technique can I use to get the outcome 00:30:45.980 --> 00:30:52.540 that I need? Computer, go do it. Interpret results, present to stakeholders. That's a data scientist, 00:30:52.540 --> 00:30:57.500 right? I think one of the challenges with that is going to be, not that it's not good, but I think 00:30:57.500 --> 00:31:04.700 it's going to be challenging because how do you learn when to use a certain statistical test or when to do 00:31:04.700 --> 00:31:10.780 some kind of funky transformation, like a Fourier transform without more traditional mathematical 00:31:10.780 --> 00:31:14.940 backgrounds? And all the academics will not just go, oh, we're just going to give you like 00:31:14.940 --> 00:31:19.340 five minute overview and they'll help you understand. They're like, nope, we're going to start with this 00:31:19.340 --> 00:31:24.620 axiom or this theorem from differential equations. I'm going to work up. You're like, no, no, no, no, 00:31:24.620 --> 00:31:30.140 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, 00:31:30.140 --> 00:31:36.620 how do I get value from a couple of the mathematical things without being sucked into like, yeah, now I'm in 00:31:36.620 --> 00:31:39.820 differential equations at Harvard online and I don't understand how I got there. 00:31:39.820 --> 00:31:45.180 It's such a big problem and I'm so glad you brought this up and I'll be vulnerable because yeah, I felt 00:31:45.180 --> 00:31:49.580 the same, the same way. And I was like, there has to be a better way. And so about, what was it? Three 00:31:49.580 --> 00:31:53.260 years ago now, two and a half years ago, three years ago, I said, oh my gosh, I'm going to solve this 00:31:53.260 --> 00:31:58.220 problem and I'm going to start my own data science bootcamp. And so I spent about six months making the 00:31:58.220 --> 00:32:03.500 curriculum, making all the videos. I opened it up. I got some students in there and I ran it for about 00:32:03.500 --> 00:32:08.620 six months and I looked at the results and man, we weren't getting anyone into data science jobs. 00:32:08.620 --> 00:32:12.940 And I thought, ah, what the heck am I doing wrong? I had this brilliant idea of like, we're going to be 00:32:12.940 --> 00:32:19.820 less theory, more project, more hands-on. And I realized, man, the truth is people just learn better 00:32:19.820 --> 00:32:24.220 at work. That's where you learn that whole technique that you just like, how does someone learn that? 00:32:24.220 --> 00:32:28.860 The answer is by getting experience and learning it at work. And when I looked back and I said, okay, 00:32:28.860 --> 00:32:33.500 well, we have had students get jobs. What jobs did they get? And it turns out most of them were 00:32:33.500 --> 00:32:38.460 getting like business intelligence, intelligence engineer jobs or data analysts or financial 00:32:38.460 --> 00:32:43.660 analyst jobs that were a little bit below a data scientist job. And I realized, oh man, 00:32:43.660 --> 00:32:49.020 if we can just help people go from zero to one and get their foot in the door, they can go from one to 00:32:49.020 --> 00:32:54.940 five much quicker at work because work is just, I don't know, it's this magical place, right? Like, 00:32:54.940 --> 00:32:58.940 like you said, they, whatever you were working at earlier, and they're like, hey, can you do this 00:32:58.940 --> 00:33:03.100 Kubernetes thing? They just kind of throw you in the fire and you're like, figure it out. And that's 00:33:03.100 --> 00:33:07.180 somehow you do, I don't know what it is about work, but you figure it out and that's where you learn. 00:33:07.180 --> 00:33:11.100 So that's kind of what I've, why I changed my curriculum to be more focused on, you know, okay, 00:33:11.100 --> 00:33:15.340 maybe people aren't going to become data scientists, but can we get them to zero to one quickly? 00:33:15.340 --> 00:33:19.740 And then they can get paid to learn the rest of the data science stuff when they're actually in that 00:33:19.740 --> 00:33:23.740 first position. How much do you know about what you actually want to do in the industry 00:33:23.740 --> 00:33:28.860 before you've done it as well? Right? Like, you're like, oh, I thought everybody said machine 00:33:28.860 --> 00:33:34.700 learning was awesome. And I've used chat CPT and I loved it, but it turns out actually like API is 00:33:34.700 --> 00:33:39.900 better, but I've never had a chance to build an API. So until I started, I didn't even learn that one, 00:33:39.900 --> 00:33:45.100 it was a thing to that. It was cool or vice versa, right? Whatever. But until you get kind of in, 00:33:45.100 --> 00:33:50.700 you don't even know, like, actually this part is where I really am enjoying it. And so just getting that 00:33:50.700 --> 00:33:55.500 first step, that's a big deal. A hundred percent. You don't know what you don't know until you know 00:33:55.500 --> 00:34:00.780 it. That's why, I mean, really when it comes to, if we like, we go back to just SQL or just Python, 00:34:00.780 --> 00:34:06.860 you could spend, I tell people this, if you tried to master Python before you applied to a job, 00:34:06.860 --> 00:34:12.140 you'd be like 80 years old before you ever applied to a job. Same with SQL, same with machine learning. 00:34:12.140 --> 00:34:17.180 The cool thing about data is we're never going to know it all. And so just learn the bare minimum to get 00:34:17.180 --> 00:34:21.900 your foot in the door. And then you have this place where you're going to get paid to learn what 00:34:21.900 --> 00:34:26.460 you want to learn. Eventually, if you learn, oh, I love APIs. I promise you that there's a company out 00:34:26.460 --> 00:34:31.100 there that will hire you and you can learn APIs on the job. Like that's going to happen. But that first 00:34:31.100 --> 00:34:36.300 step is no true. There's a company out there that it doesn't know it needs APIs, but you could help them. 00:34:36.300 --> 00:34:40.300 And you know, they don't have huge expectations because this is the thing they just learned they needed. 00:34:40.300 --> 00:34:42.700 Right. A hundred percent. Yeah. It's wild, right? 00:34:42.700 --> 00:34:50.220 This portion of Talk Python To Me is brought to you by Posit, the makers of Shiny, formerly RStudio, 00:34:50.220 --> 00:34:56.060 and especially Shiny for Python. Let me ask you a question. Are you building awesome things? Of 00:34:56.060 --> 00:35:00.940 course you are. You're a developer or data scientist. That's what we do. And you should check out Posit 00:35:00.940 --> 00:35:06.940 Connect. Posit Connect is a way for you to publish, share, and deploy all the data products that you're 00:35:06.940 --> 00:35:12.700 building using Python. People ask me the same question all the time. Michael, I have some cool 00:35:12.700 --> 00:35:18.220 data science project or notebook that I built. How do I share it with my users, stakeholders, teammates? 00:35:18.220 --> 00:35:25.420 Do I need to learn FastAPI or Flask or maybe Vue or ReactJS? Hold on now. Those are cool technologies, 00:35:25.420 --> 00:35:30.460 and I'm sure you'd benefit from them, but maybe stay focused on the data project. Let Posit Connect handle 00:35:30.460 --> 00:35:35.980 that side of things. With Posit Connect, you can rapidly and securely deploy the things you build in Python. 00:35:35.980 --> 00:35:42.380 Posit Connect, Streamlit, Dash, Shiny, Bokeh, FastAPI, Flask, Quarto, ports, dashboards, and APIs. 00:35:42.380 --> 00:35:48.380 Posit Connect supports all of them. And Posit Connect comes with all the bells and whistles to satisfy 00:35:48.380 --> 00:35:54.940 IT and other enterprise requirements. Make deployment the easiest step in your workflow with Posit Connect. 00:35:54.940 --> 00:36:01.340 For a limited time, you can try Posit Connect for free for three months by going to talkpython.fm/posit. 00:36:01.340 --> 00:36:08.300 That's talkpython.fm/posit. The link is in your podcast player show notes. Thank you to the team at 00:36:08.300 --> 00:36:09.740 Posit for supporting Talk Python. 00:36:13.740 --> 00:36:18.060 Posit Connect: Let's talk about some career advice. I mean, I know you talked about being 00:36:18.060 --> 00:36:23.180 connected on LinkedIn pretty well and certainly having some kind of social network is important. And they 00:36:23.180 --> 00:36:29.100 maybe, it's not that you would call it not social, but a real world network of actual human beings that 00:36:29.100 --> 00:36:32.700 you're, you know, physically know somehow. Posit Connect: What's that? I don't know what that is. 00:36:32.700 --> 00:36:35.180 Posit Connect: I know. Like, we gave that up back in 2020, I thought. 00:36:35.180 --> 00:36:38.380 Posit Connect: Yeah. Posit Connect: Anyway, like, there was some stat that I saw somewhere that, 00:36:38.380 --> 00:36:44.860 you know, over half of the jobs are filled filled before even becomes a job posting, right? Maybe 00:36:44.860 --> 00:36:49.340 some of the best ones is like, hey, who knows somebody who can do this? We need some, like your 00:36:49.340 --> 00:36:54.300 data science example, data scientist example. They quit like, oh, we need somebody. Does anybody know 00:36:54.300 --> 00:36:58.860 good data science? I don't want to just go put it out on the open job market and have to have a hundred 00:36:58.860 --> 00:37:03.820 interviews and who knows what I'm going to get. Like, if you can recommend somebody, let's start there, 00:37:03.820 --> 00:37:07.580 right? So being in that group to be recommended, it's important. 00:37:07.580 --> 00:37:12.220 Posit Connect: It's the key. There was a really interesting survey done on LinkedIn and they said, 00:37:12.220 --> 00:37:16.860 it was kind of, it was done by the same person and Jordan Nelson, by the way, he said, "How do you 00:37:16.860 --> 00:37:22.860 approach getting a job?" And then the next day he said, "How did you get your last job?" And 80% of 00:37:22.860 --> 00:37:27.740 people, they use what I call the spray and pray method, which basically means you go and you apply to as 00:37:27.740 --> 00:37:32.540 many jobs as you possibly can and hope for the best. Cross your fingers. That was 80% of what 00:37:32.540 --> 00:37:38.140 people were doing. And then on the next poll, the next day, it was a total, I think of what, 70% were 00:37:38.140 --> 00:37:43.740 either headhunted, recruited or referred. And so it's like the Pareto principle here where, you know, 00:37:43.740 --> 00:37:50.060 80% of the effort is only getting you 20% of the results. And really 20% of the effort gets 80% of the 00:37:50.060 --> 00:37:55.100 results. So it's okay. We know networking and getting recruited is really important, but how do we do it? 00:37:55.100 --> 00:37:59.580 It's easier said than done. And like you said- In the industry, how do I make friends who are, 00:37:59.580 --> 00:38:02.700 right? It's like, well, my neighbors don't do it. So I guess I'm out. 00:38:02.700 --> 00:38:06.700 That's the tricky thing is, is yeah, if you're not in the industry yet, how do you get recruited 00:38:06.700 --> 00:38:11.660 into it or how do you know someone? And what I've come to learn is it actually doesn't even matter. 00:38:11.660 --> 00:38:16.060 So like, for instance, let's take, let's take your neighbor, right? Your neighbor is probably not a data 00:38:16.060 --> 00:38:20.540 scientist. Maybe you're lucky and they are, and they can refer you to a company. But what's really cool is 00:38:20.540 --> 00:38:24.540 I've learned that companies really come to trust their employees and their employees' 00:38:24.540 --> 00:38:30.860 recommendations. And so even if your neighbor, let's say is a web developer, or maybe even less 00:38:30.860 --> 00:38:35.260 technical, let's just say your recruiter is in finance, right? And if there's an opening, 00:38:35.260 --> 00:38:39.980 like a data science opening at that company, a lot of the times they will actually take their 00:38:39.980 --> 00:38:44.780 employee referrals much more seriously than any sort of cold application that they get. 00:38:44.780 --> 00:38:49.500 And so a lot of the times I've had students who just know someone that works at the company, 00:38:49.500 --> 00:38:53.420 they saw a job opening pop up. They're quickly, they message their friends. Hey, 00:38:53.420 --> 00:38:57.260 do you know a recruiter or a hiring manager? I could talk more about this role. Could you do 00:38:57.260 --> 00:39:01.740 an internal referral for me? And they were able to land jobs that they probably wouldn't have. 00:39:01.740 --> 00:39:06.860 No, they definitely wouldn't have without that internal referral. So it is tricky. It's the old 00:39:06.860 --> 00:39:08.940 cliche. It's not, it's not what you know, it's who you know. 00:39:08.940 --> 00:39:12.860 I think there's still plenty of ways, COVID notwithstanding. I think that these days, 00:39:12.860 --> 00:39:18.140 there's plenty of ways to get those connections, right? But maybe people don't know, like meetup.com 00:39:18.140 --> 00:39:25.580 is really good. If you live in a non-tiny city, there's many, many things going on that around data 00:39:25.580 --> 00:39:31.020 science, around Python, around other data engineering, whatever, right? You could go to those things. 00:39:31.020 --> 00:39:37.580 They're typically even free. Often they are free with food. They even feed you, right? And make connections, 00:39:37.580 --> 00:39:42.940 or regional conferences or national conferences, right? Like we probably, many people have heard 00:39:42.940 --> 00:39:48.780 of PyCon, right? There's US PyCon, there's EuroPython, and then there's, but that's, those are the ones 00:39:48.780 --> 00:39:54.540 that are often talked about, but there's 10, 20 little smaller regional ones in the US and many more that 00:39:54.540 --> 00:39:59.100 I'm not aware of throughout the world. Probably one of those within driving distance, right? That you could 00:39:59.100 --> 00:40:05.020 go to make connections and just also kind of take the temperature of actually what, what you see on the 00:40:05.020 --> 00:40:10.460 internet versus what you see and actually talking to real people. So I'd also say, just get out there. 00:40:10.460 --> 00:40:17.100 A hundred percent. Those places have the people who probably want to hire you because they're local, 00:40:17.100 --> 00:40:21.580 right? Which is one thing that's, that's trouble on LinkedIn. I'm, I'm big on networking on LinkedIn, 00:40:21.580 --> 00:40:26.380 but a lot of the times you're going to be networking with people who in all likelihood might never have a 00:40:26.380 --> 00:40:30.300 role that's even open to you. But the people that you're like, for instance, we have, 00:40:30.300 --> 00:40:36.620 I'm in Utah and we have Silicon Slopes that has like a tech meetup. We have a local Python 00:40:36.620 --> 00:40:41.740 meetup chapter. We have the big data and developers conference that that's free every year with tons 00:40:41.740 --> 00:40:47.260 of food. And the people who go there are people from companies around there that have the openings 00:40:47.260 --> 00:40:51.740 that you're trying to find. And they want to hire people like you who are in the area. So at least 00:40:51.740 --> 00:40:55.900 you can maybe come to the office once a week or maybe once a month or whatever. Right. And so really, 00:40:55.900 --> 00:41:00.380 like you said, going to those meetups, it's tough because networking is always difficult, 00:41:00.380 --> 00:41:05.260 either online or in person, but at least in those situations, you know, Hey, these are people that 00:41:05.260 --> 00:41:10.380 are tied to real companies that exist around me that do make data higher. So I have a chance. 00:41:10.380 --> 00:41:13.980 Definitely a much higher chance than just shooting out a resume. All right. Well, let's see. 00:41:13.980 --> 00:41:20.700 We talked about job hunting already. What about like applications and resumes? What are your thoughts on 00:41:20.700 --> 00:41:26.220 that? I think once again, with the applications, the more targeted that you can make it, the better, 00:41:26.220 --> 00:41:32.620 right? So if you can really hone in on, I really want this job, I'm going to cold message five people 00:41:32.620 --> 00:41:37.340 at this company and see if I can get that internal referral one way or another, make a real connection 00:41:37.340 --> 00:41:42.780 with them. I think that's really key. And then with resumes, resumes are more of an art than they are a 00:41:42.780 --> 00:41:49.260 science. I feel like they are so difficult to figure out. And these ATSs that are trying to match you 00:41:49.260 --> 00:41:53.260 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 00:41:53.260 --> 00:41:57.580 scientist behind those, we need to have a conversation with them because it's, it's a little tricky 00:41:57.580 --> 00:42:02.060 sometimes. But one of the coolest concepts I've been introduced to recently, and I have a whole 00:42:02.060 --> 00:42:10.380 episode on my podcast about it is A, B testing your resume. And basically the idea is a resume's job 00:42:10.380 --> 00:42:15.820 is just to get you a screener interview or like a beginner interview, basically. Right. That's all an 00:42:15.820 --> 00:42:19.500 interview. Like no one's seeing a resume and then hiring you. They're always going to interview. 00:42:19.500 --> 00:42:25.180 So if you think about it, a resume's job, the only job it has is to convince someone to get on the phone 00:42:25.180 --> 00:42:29.820 and talk to you. And it's just a piece of paper. And guess what? You can put whatever you want on 00:42:29.820 --> 00:42:35.340 that piece of paper. Now I'm not saying to lie, but I'm just saying you could theoretically make a 00:42:35.340 --> 00:42:39.580 perfect resume for whatever job you're trying to go for and send it out there and see what happens. 00:42:39.580 --> 00:42:43.820 Right. But I'm not saying to do that. I'm not saying to lie. My point in saying this is that the resume 00:42:43.820 --> 00:42:48.940 is just to get you the interview. And if you're not getting interviews, something's probably wrong 00:42:48.940 --> 00:42:54.460 with your resume. And so, you know, tweak something, apply to 10 more jobs, see what happens. Tweak 00:42:54.460 --> 00:42:59.980 something, apply 10 more jobs, see what happens. Until you finally have the right combination, skills 00:42:59.980 --> 00:43:04.940 of experiences of different keywords. Because a lot of the time you're just trying to beat the ATS. And 00:43:04.940 --> 00:43:10.140 that's the sad part about it is it's like, how do I prove to this random computer algorithm that they should 00:43:10.140 --> 00:43:14.620 talk to me on the phone? That's a hard game to beat. And there's a whole bunch of advice 00:43:14.620 --> 00:43:18.620 from all these different people. What I've come to learn is it's different for every company. It's 00:43:18.620 --> 00:43:22.700 different for every person. You kind of kind of a numbers game till you get lucky and you figure it 00:43:22.700 --> 00:43:28.540 out. That's good advice. I guess two thoughts. One is I know that speaking specifically to anyone, 00:43:28.540 --> 00:43:34.860 one. But in general, women wait until they match all the requirements of a position where a guy's like, 00:43:34.860 --> 00:43:41.500 I know three of those things. I'm taking a flyer. I'm sending it. I would just like to encourage the 00:43:41.500 --> 00:43:47.180 women out there to just send it as well. I 100% agree with that. And I think if you reach 60% of 00:43:47.180 --> 00:43:52.460 the requirements, I think you have a chance. Like it's a lot of the times those are wish lists and not 00:43:52.460 --> 00:43:58.380 actual requirements. And depending on, are you local to the area? Do you have a domain experience 00:43:58.380 --> 00:44:02.860 in this company? Like there's lots of other factors. What about contributing to open source 00:44:02.860 --> 00:44:08.300 or having GitHub repos that can be like projects that you can show off or what's your advice there? 00:44:08.300 --> 00:44:13.980 I'm a huge proponent of projects in the portfolio. I think if you don't have experience with something, 00:44:13.980 --> 00:44:18.940 you create your own by building a project. And if you can do that with open source, 00:44:18.940 --> 00:44:23.900 I think you should totally do that because I've benefited so much from open source. I have not 00:44:23.900 --> 00:44:29.900 given back as much as I should to open source development and projects. I definitely should do that. 00:44:29.900 --> 00:44:33.980 But if you can find a project that you're passionate about that you can help with, I think you should 00:44:33.980 --> 00:44:38.940 totally do that. Even if it's not open source and you're just building a project to showcase your skills, 00:44:38.940 --> 00:44:43.660 I'm all about that. I think you can do projects that are super fun, maybe that are good for your 00:44:43.660 --> 00:44:48.700 community or good for your life. I'm a huge fan of personal projects. I've put a Fitbit on 00:44:48.700 --> 00:44:54.460 my dog before and looked at her steps. I've found the healthiest meal at McDonald's. I've looked at, 00:44:54.460 --> 00:44:59.740 like visualized my weight over time and tried to create like different, like forecasting models and 00:44:59.740 --> 00:45:03.660 stuff like that. There's so much data in our lives that you can use to make really cool projects. 00:45:03.660 --> 00:45:08.460 Oh, absolutely. You talked about, okay, you get your first job and that's where you kind of really 00:45:08.460 --> 00:45:14.300 learn. But if you don't have your first job, you can effectively simulate that. Say, I would have gone 00:45:14.300 --> 00:45:18.780 on to a job and been given a project to analyze something. I'm just interested in this thing. I've got 00:45:18.780 --> 00:45:24.060 two hours a day until I get a job that I can be inspired about this and just get going on it. Maybe 00:45:24.060 --> 00:45:29.980 create a website and publish your results and it can draw more people in to actually see that, right? 00:45:29.980 --> 00:45:34.060 And start to appreciate it. They could even ask like, all right, who's behind this cool project? 00:45:34.060 --> 00:45:39.340 Maybe I want them to come work for me. Little did they know you're doing all this work because you got 00:45:39.340 --> 00:45:43.580 some spare time and you're trying to build up your experience and a self-guided study, right? 00:45:43.580 --> 00:45:49.020 Yeah. If you can build a cool project and flip the job hunt where you're not applying for jobs, but jobs 00:45:49.020 --> 00:45:54.460 start to apply for you, you're in such a good position and doing really cool projects can help you get there. 00:45:54.460 --> 00:45:59.260 Now it's hard to do cool projects. It's hard to publish projects, which is one of the things 00:45:59.260 --> 00:46:04.300 that people really struggle with. For all you Python listeners out there, let me just tell you, 00:46:04.300 --> 00:46:12.620 Streamlit is absolutely amazing because it makes the deployment process so easy. It's free. It's a 00:46:12.620 --> 00:46:17.900 little tricky to deploy at first, but compared to what you used to have to do it back in the day, 00:46:17.900 --> 00:46:23.020 I'm saying back in the day, like four years ago, basically. But it was really hard to deploy 00:46:23.020 --> 00:46:27.020 something where you could send someone a URL. Hey, check out my web application, machine learning 00:46:27.020 --> 00:46:33.500 application. Streamlit is such a cool app that makes it so easy and so intuitive to make these 00:46:33.500 --> 00:46:38.460 cool little apps that you could just put on your resume, put on your portfolio, send to recruiters. I'm 00:46:38.460 --> 00:46:40.300 such a fan of the Streamlit app. I love it. 00:46:40.300 --> 00:46:44.540 Yeah, it's super cool. There's a couple of those and Streamlit is definitely one of the really nice 00:46:44.540 --> 00:46:51.740 ones there. There's also some hosting behind Streamlit as well these days, right? You don't even 00:46:51.740 --> 00:46:54.540 have to set up a server or anything and just create it and put it up there. 00:46:54.540 --> 00:46:59.980 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 00:46:59.980 --> 00:47:06.380 is more customizable than Streamlit and can do quite a bit more, but it's a lot more work to deploy it. 00:47:06.380 --> 00:47:07.660 It's more like programming. 00:47:07.660 --> 00:47:09.740 Yeah, it is more programming. 00:47:09.740 --> 00:47:12.460 Programming the UI rather than just the behind the scenes. 00:47:12.460 --> 00:47:12.700 Yeah. 00:47:12.700 --> 00:47:18.300 You have to do both and you have to know a little bit about systems and data engineering and stuff 00:47:18.300 --> 00:47:22.380 like that versus Streamlit kind of takes that, abstracts that away. But yeah, back in the day, 00:47:22.380 --> 00:47:27.420 I used to make Dash web applications and deploy them on Heroku back when they had a free tier of 00:47:27.420 --> 00:47:32.540 hosting and they've taken that away. So I don't even know what the go-to free hosting platform is 00:47:32.540 --> 00:47:35.980 nowadays. I just, I moved most of my things to Streamlit and it's so nice. 00:47:35.980 --> 00:47:38.620 Yeah. We got Shiny for Python now, which is also nice. 00:47:38.620 --> 00:47:40.220 I haven't checked that out. How is it? 00:47:40.220 --> 00:47:45.100 I haven't done too much with it either, but Joe and the team over there are doing pretty cool stuff, 00:47:45.100 --> 00:47:50.860 like adding more dynamic interactive stuff to Jupyter, like running inside Jupyter and things. Yeah, 00:47:50.860 --> 00:47:51.260 pretty cool. 00:47:51.260 --> 00:47:52.220 I'll have to check it out. 00:47:52.220 --> 00:47:56.060 I think they also do a bunch of hosting stuff over there as well, is why it came to mind. 00:47:56.060 --> 00:47:58.140 What other advice you got for folks out there? 00:47:58.140 --> 00:48:05.100 So AI is AI, not studying AI or learning to use AI, machine learning, but is there a benefit of trying 00:48:05.100 --> 00:48:12.540 to use ChatGPT to help you get this job or is there a danger? I'm thinking, for example, have ChatGPT 00:48:12.540 --> 00:48:18.460 write me an awesome resume and then the tools are like, well, we've detected this is AI generated and 00:48:18.460 --> 00:48:21.340 it's out. You know what I mean? What do you see happening there? 00:48:21.340 --> 00:48:29.260 A lot of people see AI as like an all or nothing tool as in it's either you, the human doing the 00:48:29.260 --> 00:48:33.820 work or it's the AI doing the work. But whenever, I don't know about you, but whenever I'm using 00:48:33.820 --> 00:48:40.300 ChatGPT for anything, it's very rare it's copy and paste for me or at least not iterative where I'm 00:48:40.300 --> 00:48:45.420 doing multiple prompts, prompt after prompt after prompt, trying to tweak it exactly what I want. 00:48:45.420 --> 00:48:50.300 And so the way I look at ChatGPT and other gen AI that will be coming out, that's only inevitable, 00:48:50.300 --> 00:48:55.180 is instead of looking at does this replace me? Does this, like for instance, am I going to build 00:48:55.180 --> 00:49:02.060 my whole resume using ChatGPT? Can ChatGPT build, you know, take a data scientist's job and build the 00:49:02.060 --> 00:49:07.180 whole model for them? I like to see it more as like a hammer. It's like a tool for the data scientist or 00:49:07.180 --> 00:49:13.340 a tool for the job searcher to use in conjunction with your screwdriver or anything else. It's like 00:49:13.340 --> 00:49:18.380 something to be wielded by a human, not replaced for the human, if that makes sense. 00:49:18.380 --> 00:49:22.780 You know, it's really good for stuff like, hey, I know a regular expression will do this. 00:49:22.780 --> 00:49:23.260 Yeah. 00:49:23.260 --> 00:49:27.740 The last time I studied, I completely forgot what this is about. And I know it's gnarly, 00:49:27.740 --> 00:49:32.700 but if I just ask, here's an example, here's what I want. Boom. And traditionally what you would end up 00:49:32.700 --> 00:49:35.020 doing is you'd be on Stack Overflow. Yeah. 00:49:35.020 --> 00:49:38.860 You'd be all over the internet. You'd be trying to piece it together from external information anyway. 00:49:38.860 --> 00:49:44.220 And so code is something that's a little bit more in the wheelhouse of the generative AI, 00:49:44.220 --> 00:49:50.220 because it can't really make it up as much. I know it could like do something insecure and you didn't 00:49:50.220 --> 00:49:55.340 know it was or whatever, but it's not like asking for legal advice where it makes up cases that didn't 00:49:55.340 --> 00:50:00.460 exist. Like it gives you code. You put it in the runtime of the compiler and it runs or it doesn't. 00:50:00.460 --> 00:50:01.740 And the output comes like you did. 00:50:01.740 --> 00:50:02.700 Yeah. It works or not. 00:50:02.700 --> 00:50:08.620 Yeah. So it's pretty, pretty effective for that. But yeah, for resumes, I would be more like, 00:50:08.620 --> 00:50:14.620 let me ask it. What are the in demand things? And if I know these three skills, what other skills should 00:50:14.620 --> 00:50:20.140 I know to get a, you could sort of use it in an explorative way to then come up with what you 00:50:20.140 --> 00:50:22.140 might write for yourself, right? Something like this. 00:50:22.140 --> 00:50:27.900 I find it really useful for brainstorming like action verbs on your resume bullets. Like I think 00:50:27.900 --> 00:50:33.180 it's really good at that. What's 10 different ways to say lead. So I don't say lead five times on my 00:50:33.180 --> 00:50:38.700 resume and I use some different action bullets. I think it's great at that. I personally, it's pretty 00:50:38.700 --> 00:50:44.380 rare that I start any Python code from scratch nowadays. I'm either starting hopefully from a 00:50:44.380 --> 00:50:49.740 template that I've already written, or I'm starting from a ChatGPT. Like this is what I kind of want 00:50:49.740 --> 00:50:55.260 to accomplish, right? Like the outline for it. Like one of the things I hate doing is I make a lot of 00:50:55.260 --> 00:50:59.260 streamlet apps. I probably make a streamlet app a month right now. And I hate starting from scratch 00:50:59.260 --> 00:51:03.260 with streamlet. It's super easy to start from scratch, but I'll say, Hey, ChatGPT, I want to 00:51:03.260 --> 00:51:06.860 build a streamlet app. This is like the component I want here. This is the component I want here. 00:51:06.860 --> 00:51:12.300 This is the component I want here. And it's almost like a warmup for me as a programmer. And it will 00:51:12.300 --> 00:51:18.060 create something that works. It's not what I want. And I spend the next five hours trying to make it 00:51:18.060 --> 00:51:23.740 what I want, you know, without ChatGPT, but it kind of gives me a warm start to my programming process. 00:51:23.740 --> 00:51:29.100 So I really like it. I think it's something that everyone should use. And I think if you're thinking 00:51:29.100 --> 00:51:34.540 about getting into any sort of programming, you know, whether it's data science or web development, 00:51:34.540 --> 00:51:40.940 I think you should be a little bit less worried about it taking your job and job security. I think 00:51:40.940 --> 00:51:45.820 you should almost be more excited that, wow, the bar has never been lowered to break into tech. 00:51:45.820 --> 00:51:51.900 Like this is a step up gift from the programming gods that I get to use to break into tech. 00:51:51.900 --> 00:51:56.620 Another thing to keep in mind is I imagine a lot of people listening to this podcast are not just 00:51:56.620 --> 00:52:03.820 starting a college program, right? They're coming from possibly other experiences, other specialties. 00:52:03.820 --> 00:52:07.980 You know, what's really good for job security, knowing the intersection of two things, the 00:52:07.980 --> 00:52:13.900 intersection of chemistry and programming, the intersection of geology and programming for Exxon, 00:52:13.900 --> 00:52:19.900 potentially, right? Like those things take you from a pool of a thousand to a pool of tens, 00:52:19.900 --> 00:52:24.540 tens, right? And so what's awesome about that is it means two things. You don't throw away, 00:52:24.540 --> 00:52:28.140 if you got a degree in something else like biology or whatever, you don't throw away like, 00:52:28.140 --> 00:52:33.660 well, that was wasted four years. That's out. And it slices the pool of people who could apply for 00:52:33.660 --> 00:52:36.140 certain jobs way, way smaller, right? Sounds like you agree. 00:52:36.140 --> 00:52:41.180 Oh, a thousand percent. I'll just tell a quick little anecdote. When I was at ExxonMobil, 00:52:41.180 --> 00:52:45.820 there's a lot of things I did not like at ExxonMobil, but this is something I really liked. It's about 00:52:46.460 --> 00:52:51.980 once a quarter, they would do a crowdsourced data science competition for the whole organization, 00:52:51.980 --> 00:52:56.060 like around the entire world. And they would say, this is a business problem we're trying to solve, 00:52:56.060 --> 00:53:00.460 you know, and at Exxon, we have data scientists all over the world and like all sorts of different 00:53:00.460 --> 00:53:05.100 teams and things like that. So I like did not know all the data scientists at Exxon. And they'd say, 00:53:05.100 --> 00:53:10.860 this is the problem we're facing. Here's the data go, right? And I loved participating in these. It was 00:53:10.860 --> 00:53:15.180 like right up my, my wheelhouse of like, I really enjoy exploration and all this stuff. 00:53:15.180 --> 00:53:18.700 At the time I was getting my master's degree, but I didn't have my master's degree. 00:53:18.700 --> 00:53:24.220 And I was competing against, so I'm just a chemical engineering grad, right? And I'm competing against 00:53:24.220 --> 00:53:29.660 people with PhDs in computer science and in data science and all these like people who have way 00:53:29.660 --> 00:53:35.260 more experience than me. And I actually won a few of these competitions. Thank you. I appreciate it. 00:53:35.260 --> 00:53:40.220 And it's not because I was a better programmer or a better data scientist. It's because I majored in 00:53:40.220 --> 00:53:46.460 chemical engineering and I knew the business problem, the domain extremely well. And I kind 00:53:46.460 --> 00:53:51.180 of knew the programming and the data science stuff, but the combination of them made me very valuable. 00:53:51.180 --> 00:53:56.860 Like one of the best examples I have is we're looking at crude oil properties. And I remember 00:53:56.860 --> 00:54:01.100 like there was a forum where you'd like ask your questions. And one of the, one of the data scientists 00:54:01.100 --> 00:54:06.620 asked, Hey, is sulfur bad? There's lots of sulfur in this. Is it bad? And like to a chemical engineer, 00:54:06.620 --> 00:54:11.420 that's like the most obvious thing. No, you, yes. Sulfur is very bad in crude oil. That's very, 00:54:11.420 --> 00:54:17.260 no, no, that's like such a fundamental thing to me and to him or her. That was like groundbreaking. 00:54:17.260 --> 00:54:20.700 And so, yeah, your domain can become your superpower in your career. 00:54:20.700 --> 00:54:26.940 Yeah. And it makes it way harder for ChatGPT and other types of tools to just automate you out of a 00:54:26.940 --> 00:54:31.500 job because you bring in all these skills together, which is awesome. But it also makes it easier for 00:54:31.500 --> 00:54:35.500 you to get the job. It makes it easier for you to continue your momentum of whatever you've been up 00:54:35.500 --> 00:54:39.660 to. It's just, it's good all around. Yeah. I think it's more fun too, because once again, 00:54:39.660 --> 00:54:45.180 like when I was trying to decide if I should study computer science, I was like, man, I don't really want to 00:54:45.180 --> 00:54:52.460 to build an Excel workbook for building an Excel workbook sake. That's still true for me today. 00:54:52.460 --> 00:54:57.260 I don't want to do data science for data science sake. I only like machine learning or data science 00:54:57.260 --> 00:55:01.580 when I'm doing it to solve a really fun problem I'm passionate about. That's where it's more fun. 00:55:01.580 --> 00:55:05.900 So if you can be excited about the domain and excited about the algorithms, I think that's a 00:55:05.900 --> 00:55:09.660 great place to be. Absolutely agree. All right. We're getting short on time, 00:55:09.660 --> 00:55:14.380 but maybe tell us a bit about your data career jumpstart. You've referred to it a couple of times. 00:55:14.380 --> 00:55:18.780 Yeah. I have a company called data career jumpstart. I just try to do a lot of education. So the 00:55:18.780 --> 00:55:23.180 education happens on LinkedIn happens on YouTube. And I actually forgot to mention this at the 00:55:23.180 --> 00:55:28.540 beginning, but I have my own podcast called the data career podcast, where I help people land their 00:55:28.540 --> 00:55:33.980 first data job. We're about at a hundred episodes. So not quite the groundwork that you've put in. 00:55:33.980 --> 00:55:35.260 That's still a ton. That's awesome. 00:55:35.260 --> 00:55:40.940 Yeah, we're getting there. And then, yeah, I also have a bootcamp where I try to affordably help 00:55:40.940 --> 00:55:46.700 people land their first data analyst position by teaching them the skills, the networking and the 00:55:46.700 --> 00:55:50.140 project and portfolio building that they need to do something. 00:55:50.140 --> 00:55:51.740 Like the long version of this show. 00:55:51.740 --> 00:55:56.220 Yeah. Basically. Well, yeah. Just take what we talked about today, expand on it, 00:55:56.220 --> 00:56:00.620 make it like 350 unique lessons. And that's exactly what it is. 00:56:00.620 --> 00:56:06.860 Yeah. Very cool. All right. Well, we're about out of time. So maybe just every final call to action, 00:56:06.860 --> 00:56:11.660 people maybe are inspired. I see Dave go out in the audience. That's an awesome talk. Very much so. 00:56:11.660 --> 00:56:15.420 What's next. It's easy to be inspired, but you got to take action. 00:56:15.420 --> 00:56:21.260 Yeah. I love that. I think it's always fun to listen to podcasts, but you probably benefit way 00:56:21.260 --> 00:56:26.460 more from the action you take after a podcast. So for you guys who are maybe interested in a data 00:56:26.460 --> 00:56:32.140 analytics or a data science career, explore that. If you're like, yes, I'm in, make a plan, make a 00:56:32.140 --> 00:56:36.860 roadmap. If you need help, I have a webinar that will help you make a roadmap. What skills should you 00:56:36.860 --> 00:56:40.620 learn? How should you be networking and stuff like that? But really probably if you're just getting 00:56:40.620 --> 00:56:44.220 started trying to figure out what skills you should learn, like what are the top skills that you should 00:56:44.220 --> 00:56:48.620 be learning and then learning those skills and then not only learning those skills, but take action and 00:56:48.620 --> 00:56:52.540 learning and build some sort of a project that we talked about that you could put on a portfolio, 00:56:52.540 --> 00:56:57.260 make a streamlet app or something like that. That's probably the best action you could possibly take. 00:56:57.260 --> 00:57:02.380 If you need any ideas, advice, feel free to check out my website, datacareerjumpstar.com or the 00:57:02.380 --> 00:57:07.020 podcast data career podcast. Hopefully there's a lots of free resources for you guys to check that out. 00:57:07.020 --> 00:57:09.580 If you've never seen streamlet before, I have some YouTube videos about 00:57:09.580 --> 00:57:13.660 streamlet that you guys can check out, but I love it. Just take action somehow, do something. 00:57:13.660 --> 00:57:18.540 That's one of the huge, huge differentiators is like, you might be inspired, but you just got 00:57:18.540 --> 00:57:23.260 to start taking those steps and it becomes a snowball. So thanks for sharing all your experience and your 00:57:23.260 --> 00:57:28.380 advice. Hopefully some people out there are taking action and yeah, I'll put everything we talked about 00:57:28.380 --> 00:57:30.620 in the show notes, of course. So thanks for being here, Avery. 00:57:30.620 --> 00:57:32.380 Yeah. Thank you. Thanks for having me. I appreciate it. 00:57:32.380 --> 00:57:33.180 You bet. 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