This is episode number 457 with Harpreet Sahota, the lead data scientist at Price Industries. 

Jon Krohn: 00:00:12

Welcome to the SuperDataScience Podcast. My name is Jon Krohn, a chief data scientist and bestselling author on deep learning. Each week, we bring you inspiring people and ideas, to help you build a successful career in data science. Thanks for being here today, and now let's make the complex simple. 

Jon Krohn: 00:00:42

Welcome back to the SuperDataScience Podcast, I'm your host Jon Krohn. We are very fortunate indeed, to be joined today by Harpreet Sahota. Harpreet is an eminent contributor to the data science community, he is the host of the Artists of Data Science Podcast, the principle data science mentor at Data Science Dream Job, and the founder of the Data Community Content Creator Awards. And, I haven't even mentioned his day job, yet. He's lead data scientist at Price Industries, a global industrial leader. During today's episode, Harpreet fills us in on how to land your data science dream job, whether you're keen to move into the field of data science or looking to make the jump into a more senior role. He's got a ton of tips and resources for you, so let's jump right in. 

Jon Krohn: 00:01:36

Harpreet, welcome to the show. I'm so excited to have you on. 

Harpreet Sahota: 00:01:40

Jon, thank you so much, man. It is an absolute pleasure to be here, the first data science podcast I've ever been invited to. 

Jon Krohn: 00:01:47

No! 

Harpreet Sahota: 00:01:47

And, it ends up being the SuperDataScience Podcast. I made it straight out the huddle man, to the end zone. 

Jon Krohn: 00:01:57

Wow. You are so lucky! 

Harpreet Sahota: 00:01:59

Yeah. Usually, I feel like it takes steps, do a little small podcast and then you get on a big one. I just got invited to this one, man. Thank you, thank you for having me. 

Jon Krohn: 00:02:07

Well, it's an honor to have you on the show, Harpreet, despite your humility, and despite the unbelievable fact that this is your first podcast appearance as a guest, you have tons of experience hosting podcasts. We're going to talk about all of that later on, on the show. 

Jon Krohn: 00:02:24

But first, tell us a little bit about you. So I'm Canadian, I grew up in Toronto and I saw from your LinkedIn profile that you're in Winnipeg. I assumed that you were Canadian, too, but I discovered that, in fact, you are not. 

Harpreet Sahota: 00:02:37

Yeah, I'm born and raised Sacramento, California, South Sacramento for anybody listening, Valley High. Definitely an amazing place to grow up, but I've been in Winnipeg for the last seven-ish years, so Canadian permanent resident. I feel more Canadian than I do American. 

Jon Krohn: 00:02:57

Nice. Do you have a hockey team that you cheer for? I guess, the Winnipeg Jets? 

Harpreet Sahota: 00:03:00

Yeah, I rep for the Jets, man. I've got to put it down for the Jets. Football's always the 49ers, always 49ers. 

Jon Krohn: 00:03:04

49ers, not CFL football? Have you got into that, Canadian Football League? 

Harpreet Sahota: 00:03:12

Yeah, I've been to a couple of Blue Bombers games, over the years. They're not as fun. 

Jon Krohn: 00:03:18

Yeah. We're going off on a little bit of a tangent here, and I'll reign it back in pretty quickly for the listeners who aren't interested in the differences between Canadian and American football. But, Canadian football, it only has three downs instead of four downs, so it means you still have to get as far ... Well actually, you have to get 10 meters not 10 yards, and 10 meters is a little bit longer than 10 yards. But, that's not the big hurdle. The big hurdle is that you've only got three shots to get 10 yards as opposed to four chances, so it means that it's a much faster moving game. I think it's very exciting. Oh, and also the clock between plays is only 20 seconds instead of 40 seconds, so everything's moving faster. But, there's not very many teams.

Harpreet Sahota: 00:04:03

Because it's cold, man. It's too cold to play football during that season. 

Jon Krohn: 00:04:09

Yeah, I guess that's true. I hadn't thought of it that way. 

Harpreet Sahota: 00:04:12

Yeah. 

Jon Krohn: 00:04:12

There's also just not as big of a population to support it. 

Harpreet Sahota: 00:04:16

Yeah. 

Jon Krohn: 00:04:17

But, fun fact for listeners out there. The oldest professional sports club in the world ... Sorry, not in the world. The oldest professional sports club in North America, so in the US or Canada, is the Toronto Argonauts, which are a Canadian football team based out of Toronto. Yeah, they started as a rowing club in the 1890s or something. 

Harpreet Sahota: 00:04:37

Yeah, that's interesting, man. 

Jon Krohn: 00:04:39

There you go. Anyway, you're in Winnipeg, you cheer for the 49ers, the Jets and the Blue Bombers. How is the lockdown, the pandemic lockdown, out in Winnipeg? It's cold. 

Harpreet Sahota: 00:04:57

Yeah, it's cold. It's five Celsius, which I think that's almost 40 degrees Fahrenheit. I'm out there with a t-shirt now, because that feels amazing. 

Jon Krohn: 00:05:06

Yeah, that's warm in a Winnipeg winter, for sure. 

Harpreet Sahota: 00:05:08

After literally three straight weeks of negative 30 Celsius, it was brutal. 

Jon Krohn: 00:05:14

Wow. 

Harpreet Sahota: 00:05:14

But, we were on lockdown pretty severe from October to just about the middle of February, everything was on lock. They gradually opened it up. We started the last two weeks in February, things opened up to 25% capacity. Now, some places are up at 50% capacity, we can go out to restaurants and stuff like that. But, we can only go with people who are members of your household, and you have to provide identification. 

Jon Krohn: 00:05:41

Oh, that's interesting. Oh, wow. So you have to show that you live at the same address, or something. 

Harpreet Sahota: 00:05:46

Yeah. Yeah. 

Jon Krohn: 00:05:47

Wow, so interesting.

 

Harpreet Sahota: 00:05:51

Yeah. 

Jon Krohn: 00:05:51

For listeners who need to convert that negative 30 Celsius into Fahrenheit, that's pretty much negative 30 Fahrenheit because at negative 40 Fahrenheit is equal to negative 40 Celsius. Either way, negative 30 is bloody cold. 

Harpreet Sahota: 00:06:06

Yeah. After a certain point, it's all cold. 

Jon Krohn: 00:06:13

Well yeah, it's nice that you can finally dine in. In New York, where I live, you could dine at restaurants all through the last few months, all through the winter, but until very recently it was outdoor only, which obviously you can't do when it's minus 30. And even here, if it's even approaching freezing ... We tried one night, my girlfriend and me tried going out, it was just above freezing. And, just before the mains came I was like, "Can you just pack the mains up for us? We're going to take everything to go because my girlfriend's freezing." Anyway, we're getting there. 

Harpreet Sahota: 00:06:55

Yeah. Everything here, restaurant wise, during that period you could do takeout and all that stuff, which is great. I'm huge on supporting local, I am all about pumping money back into local economies, so always trying to find opportunities to order from smaller restaurants and keep them going. 

Jon Krohn: 00:07:14

Nice. You guys have cars and stuff, unlike here, so you can go and just pick up the takeaway, which is nice. 

Harpreet Sahota: 00:07:18

Yeah. 

Jon Krohn: 00:07:19

Cool. 

Jon Krohn: 00:07:22

Hey everybody, hope you're enjoying this amazing episode. We've got a quick announcement, and then we'll get straight back to it. The announcement is that DataScienceGO Virtual number three is approaching quickly, it's happening on April 10th to 11th, and you can get your free tickets today at datasciencego.com/virtual. We've got incredible speakers, hands-on workshops, and an expo area that you can virtually attend. And of course, we've also brought back one of the most popular parts of DSGO Virtual, the networking sessions. These sessions are the best way to become part of our global data science community. Over the course of the conference, there will be several three minute speed networking sessions, in which you connect with a randomly selected data scientist from anywhere in the world. After the three minutes, if you like each other and you'd like to remain connected you hit the connect button, and you can stay in touch. 

Jon Krohn: 00:08:13

Once again, every aspect of the DataScienceGO conference is absolutely free. Register for your ticket today at datasciencego.com/virtual, and we'll see you there. And now, let's get back to the episode. 

Jon Krohn: 00:08:29

All right, you and I met pretty recently. This is the first time that we've spoken to each other, so we've corresponded by email recently. The way that we know each other is through Kate Strachnyi. Kate is an awesome person, she's a huge LinkedIn data science leader, if you haven't heard of her, which is probably a minority of listeners. And, she was on episode 441 of the SuperDataScience Show, and she highly recommended that I speak to you, Harpreet. I looked at your profile and right away I was like, "Absolutely." I messaged you immediately, to get you on the show. 

Jon Krohn: 00:09:07

That connection between us of Kate, that is pertinent to the first thing that I want to talk about, which is such a cool thing that you two are doing together. You and Kate are creating the first, I guess it's probably going to be annual ... 

Harpreet Sahota: 00:09:23

Yeah, I'm hoping. Hoping it is. 

Jon Krohn: 00:09:25

Yeah, the first annual Data Community Content Creator Awards. These are so cool, I can't believe that I haven't seen something like this before, now that I know it exists. This was just announced yesterday, at the time of recording. The award show itself is on April 27th. This episode is airing early April, so if you're listening to this episode shortly after it came out, you're going to be able to watch the Data Community Content Creators Awards, live on LinkedIn. And, I think it's going to be April 1 7th or something like that, that nominations will close so probably, if you're listening to this early on after release, we'll provide you with a URL in the show notes so you can go and nominate people to the categories of your choice. 

Jon Krohn: 00:10:18

So, we should talk about that. Harpreet, run us through the various categories that you and Kate have on the show. 

Harpreet Sahota: 00:10:25

Yeah, definitely. I'll run through the categories, but I think it's funny to get a little bit of context on how this thing came about. 

Jon Krohn: 00:10:30

Oh, for sure. 

Harpreet Sahota: 00:10:31

I was scrolling LinkedIn one day, and I just saw somebody was giving out awards to people. I thought to myself, "Do you need some governing body to give you authority to give awards to people, or can you just make it happen?" I thought about it and I was like, "Actually, you could just make it happen, you could just start giving awards to people." 

Harpreet Sahota: 00:10:51

I'm all about doing big things, just weird, different things. I thought it would be really interesting to take the People's Choice Awards, add the flair and swag of the MTV Awards together with this LinkedIn Live thing that's happening, and create an awards ceremony around that. I reached out to Kate. I was like, "Dude, I've got this crazy idea." I know Kate is big on doing innovative things, just like she was doing with the DATAcated conference, the first conference hosted entirely on LinkedIn, I think that's amazing. She was down for it. 

Harpreet Sahota: 00:11:29

We came up with this thing, man, and we've got a bunch of categories. We've got YouTubes, blogs, GitHub, Kaggle, podcasts, talk show host, authors of instructional technical textbooks, and author for data science books that are for the popular culture, social media presence. With YouTube, we've split it up into a few different categories. We've got math and stats tutorials, YouTube for machine learning and AI, and then a YouTube for data science. Then we've got bloggers, and we've got your favorite Kaggler, whether it's the Grandmaster or whatever Kaggle Master that you really enjoy. We've got GitHub, if there's a particular GitHub user who is just constantly doing awesome stuff, nominate them. You got your favorite podcasts, so go and vote for SuperDataScience. 

Jon Krohn: 00:12:15

Thank you very much, Harpreet. 

Harpreet Sahota: 00:12:19

You can nominate your favorite author for technical books or for popular audience. And again, LinkedIn, Instagram and Twitter. 

Jon Krohn: 00:12:29

Amazing. Some of these are ones that I'm like, "Yeah, that makes a lot of sense," and they're traditional, like authors. But, it's great how you split that up into the technical side and the popular side. Some of these categories are really fun ones that I don't think I would have thought of, like Kaggle user, GitHub user. I'm really looking forward to these social media personalities. I know that I'm going to learn from things like the Instagram category, because I haven't personally seen Instagram used a lot as a way of conveying data science information, but I've heard that people are doing it. So by seeing these nominees and seeing what they're doing, I could get some inspiration, maybe, to be doing stuff on Instagram as well. 

Jon Krohn: 00:13:10

So tons to learn, I think that this is a huge opportunity to see what other people are doing outside of the bubble that we live in. When I go on LinkedIn, I see Kate right at the top of the page, I see Ben Taylor, I see you, but there's other big data science personalities out there that I've never heard of. So an award show like this, where everyone's nominated by the viewers themselves, by data community members, I'm going to get exposure to a much broader range than I otherwise could, possibly. 

Harpreet Sahota: 00:13:44

Yeah, that's the biggest reason we're doing this is just to bring awareness to all these awesome people out there doing work that is helping all of us. And, we all learn from different people, different platforms, so when you go and register and place your vote, you don't have to vote for every category. If there's some categories where you don't know people, that's all good. But, by the end of the ceremony that one category you didn't know anybody for, you're going to learn about some new people and that's the biggest thing that I'm hoping to get from this. 

Jon Krohn: 00:14:15

Yeah, I can't wait. I'm going to be there with my tuxedo on. 

Harpreet Sahota: 00:14:18

Yes. Do it, dude. 

Jon Krohn: 00:14:20

Sitting right here, probably, in the same chair as always. 

Harpreet Sahota: 00:14:23

Yeah, I've got a pretty good tuxedo getup I'm going to be rocking as well. 

Jon Krohn: 00:14:28

Nice, I'm looking forward to it. 

Jon Krohn: 00:14:31

All right, that isn't the only data science thing that consumes your time. In fact, I think listeners are going to be blown away by the variety of ways that you provide content to the data science community. The next one that I'd like to talk about is the Artists of Data Science Podcast. This airs three times a week right now, which is amazing. I can only imagine, we do two episodes a week with the SuperDataScience Show and I'm like, "That's a lot." 

Jon Krohn: 00:15:03

The three a week right now, you've got a big guest episode, you've got a Friday Happy Hour, and then right now on Sundays, you have Office Hours with Ayodele from Comet ML. Ayodele, she was a guest on the show the SuperDataScience Show recently, episode 449. We had an amazing conversation in which I learned so much about ethical AI. Which I highly recommend, if you're not aware of the potential issues associated with deploying data science models into the real world, I'd definitely recommend checking out that episode. Even if you know a bit about it, which I do, I learned a ton from Ayodele who is an expert in the space, she's writing a book on the topic. 

Harpreet Sahota: 00:15:49

Oh, nice. I did not know that. But yeah, one of our Office Hours we had a couple weeks ago, there was heavy conversation around that topic of AI ethics. She provided a wealth of information, so if you guys get a chance, go check that out. I think it's the February 21st episode that we go deep on that topic, so it'll be up on the YouTube. 

Jon Krohn: 00:16:11

Of the episode of the Artists of Data Science? 

Harpreet Sahota: 00:16:15

Yeah. 

Jon Krohn: 00:16:15

Oh, of the Happy Hour? 

Harpreet Sahota: 00:16:17

Of the Happy Hour, yeah. 

Jon Krohn: 00:16:18

Yeah. Nice. Those are the Comet Machine Learning Happy Hours on Sunday with Ayodele. You also the Happy Hour on Friday. Tell us about this Happy Hour format, in general, and how its caught on. 

Harpreet Sahota: 00:16:31

Yeah. Both sessions, Comet ML and the Friday Happy Hours, have the same format that this is driven entirely by you, the audience, and your questions. Without you guys, it wouldn't be possible for me to do these types of events. 

Harpreet Sahota: 00:16:48

Essentially, you just come in if you've got a question related to the job search, question related to a project you're working on, maybe it's something that you need help understanding. Just whatever question you have related to your journey in data science, this is a platform for you to come and ask that question, and get some insight. We're not going to have all the answers, but we can help point you in the right direction. I think it's just a way to give back to everyone. 

Jon Krohn: 00:17:16

I think it's amazing that you do it, and it's free for anyone to attend. We'll have the URLs in the show notes, for both the Friday Happy Hour as well as the Sunday Office Hours. But, maybe tell us when they are and how people can sign up. 

Harpreet Sahota: 00:17:29

Yeah, Friday Happy Hour is 4:30 PM, that's Central time. These are Bit.ly links, so bit.ly and then A-D-S-O-H, so Artists Data Science Office Hours. And then, Comet ML's Sunday session is on 11 AM Central time, and that's a Bit.ly link as well, /Comet-ML-OH. That's 11 AM, mostly just because there's a bunch of people from Europe who don't get to come to the Friday session because it's the middle of the night for them, so I figured that would be a perfect time to host that. 

Jon Krohn: 00:18:10

Nice. It's cool, because by having them at different times, different kinds of people can show up. People sign up for free, and then it's just a Zoom call and people can just ask questions of anything, and learn from the wisdom of the crowd. 

Harpreet Sahota: 00:18:25

Yeah it's so cool, man. They're a lot of fun. It started out, the Office Hours that I did the first five to seven episodes was just me and maybe five, six people. And, it was just so question answer sessions, so those were really intimate because they started some really personal questions and stuff, so I was happy to talk about. But then slowly, it just started catching on and now the Friday session is over 40 people, and people like David Langer, Tom Ives, Ben Taylor's always there. 

Jon Krohn: 00:18:54

Wow! 

Harpreet Sahota: 00:18:55

Kate will stop by every now and then. 

Jon Krohn: 00:18:57

Nice. Yeah, all those names are familiar to me. 

Harpreet Sahota: 00:19:00

Yeah. 

Jon Krohn: 00:19:00

I haven't met all of them, but I know who they all are. 

Harpreet Sahota: 00:19:02

Yeah, it's cool to see all of your LinkedIn influencers in data science in one space, coming to hang out. For me, that was, "What is going on? This is wild. I follow all of you people, look up to all of you guys and respect you guys, and you're just showing up to my house on Fridays now, hanging out for an hour, or two hours." It's huge, man. It's cool, I really, really enjoy it. 

Jon Krohn: 00:19:24

It is really cool. But, we haven't even talked about what I think is the coolest aspect of all, which is your big episode, your big guest episode every week, where you have authors on the program. And, I thought that the reason why you called your podcast Artists of Data Science was because you had these creative types, like authors, as your guests on the show primarily. But, I learned I was wrong. Tell us about the name, Artists of Data Science. 

Harpreet Sahota: 00:19:50

Yeah. The Artists of Data Science, it's the listener, it's my audience, these are the artists of data science. I use artists in the same sense that Seth Godin and Steven Pressfield use the word artist. An artist is someone who uses bravery, insight, creativity and boldness to challenge the status quo. 

Jon Krohn: 00:20:11

Nice. 

Harpreet Sahota: 00:20:12

I feel like there's a special breed of data scientists that listen to my show, just like there's a special breed of data scientist that listens to SuperDataScience. For me, it's those data scientists who realize that data science isn't everything, that there's more out there. That they can and should be interested in more than just data science. So for that reason, I definitely talk to data scientists as well, but mostly just authors who have written books that I really, really enjoy. 

Harpreet Sahota: 00:20:36

I'm big into personal development, self development, refining my character, just into all that wellness and that type of soft stuff, and I think data scientists don't get enough of that in their lives. I don't know why they don't get enough of it, maybe they think that that's not something they should be involved in, but I'm just trying to normalize it and make it okay for you to be interested in other things, and not tie your identity up as just a data scientist. So for me, the Artists of Data Science is the Impact Theory for data scientists. 

Jon Krohn: 00:21:09

Love it. So Impact Theory is another podcast that you're inspired by. 

Harpreet Sahota: 00:21:12

Yeah. Tom Bilyeu is one of my heroes in the space, so I'm just a cheap Tom Bilyeu knockoff at this point. But yeah, definitely [crosstalk 00:21:22] about that. 

Jon Krohn: 00:21:21

With a data science spin that gives it a special twist. 

Harpreet Sahota: 00:21:25

Yeah. 

Jon Krohn: 00:21:26

Yeah. And in time, it'll grow to be really huge. And especially, because you've had big name guests like Robert Greene. You've had some of the biggest authors on the planet as guests on your program, and you've only just started. 

Harpreet Sahota: 00:21:36

Yeah. I have all these books on my bookshelf. It was like, "Why can't I ask them? Nobody's going to reach out of my screen, slap my hand and say no, you are not allowed to reach out to Robert Greene and ask him to be on your podcast." So I just started doing it, and my response rate was just crazy. People started saying yes and my mind was blown. I've had awesome people, like you mentioned Robert Greene, James Altucher, Barbara Oakley. I've had Donald Robertson, whose written a few books on Stoicism. A bunch of other people man, too many to list. But hopefully, getting bigger and bigger names of people who I look up to, who have written books that I really enjoy. And, just ask them questions to help us think about stuff other than data science. 

Jon Krohn: 00:22:26

Nice. You mentioned Stoicism, there. So, you're a Stoic philosopher? 

Harpreet Sahota: 00:22:30

I would say ... 

Jon Krohn: 00:22:33

You're a Stoic practitioner? 

Harpreet Sahota: 00:22:35

Correct. 

Jon Krohn: 00:22:35

Or, you aim to be. 

Harpreet Sahota: 00:22:37

I try to be, right. It's difficult. It's very, very hard. I definitely have an affinity towards the philosophy, it just resonated with me primarily the last year and a bit. 

Jon Krohn: 00:22:48

For listeners that haven't heard of it, it's Stoic with a capital S. 

Harpreet Sahota: 00:22:52

Yes. 

Jon Krohn: 00:22:52

Tell us a little bit about it. 

Harpreet Sahota: 00:22:56

It's such a big, beautiful philosophy. But essentially, all it's about is just ... It's not stoic as in, "Oh I'm emotionless, I'm cold. I have no emotions," or anything like that. 

Jon Krohn: 00:23:06

Yeah, that's the lower case S. 

Harpreet Sahota: 00:23:08

That's the lower case S. I'd say capital S Stoicism is all about just using rational judgment, being able to pause before reactions, and really practicing these cardinal virtues that they espouse. Courage, wisdom, justice, temperance, and training and discipline of your character, which is hard. Not easy. 

Jon Krohn: 00:23:31

For sure. It's a lifelong journey. By studying these people, by reading their works, and then by getting them on as podcast guests, it seems like a pretty solid way to be making inroads. Of course, with all of the self-reflection. 

Harpreet Sahota: 00:23:51

Yeah. Yeah, for me it's just an excuse to explore my own curiosity and then talk to people about it. And then, share that with other people. 

Jon Krohn: 00:23:59

Nice. I love it. With that, with three episodes of the Artists of Data Science a week, it might sound like that what you do primarily, but it isn't even close. We've only scratched the surface of you, Harpreet. We've already talked about the Data Community Content Creator Awards, we've talked about Artists of Data Science. But, tell me about Data Science Dream Job, which is something else that you invest a fair bit of your time in. 

Harpreet Sahota: 00:24:24

Yeah. Data Science Dream Job is a platform that is a coaching and mentorship platform to help people get into data science. Whether you are switching careers into data science, or whether you're fresh out of school, or maybe you've had a couple of jobs in data science and now you're trying to take it to the next level, we're there to help you along the way. 

Harpreet Sahota: 00:24:45

The first couple modules, we talk all about mindset, and habits, and how to develop those in yourself so that you can be successful going forward. And then, we get into all about how to, essentially, how to carry yourself through the interview process. People always wonder, "What skills do I need to get my first job in data science?" They don't realize that interviewing itself is a skill, so we help you guys develop that skill. But, we've also got a bunch of technical workshops that we have. We're not a bootcamp by any means, but we host a fair amount of technical content. 

Harpreet Sahota: 00:25:20

For example, I'm doing a SQL From the Ground Up course, starting from the very, very basics of SQL and incrementally moving up every week. We do all sorts of other take home assignments ... Not take home assignments, I'll help you on your take home assignments. But, we've got projects, and portfolio project examples, and things like that. 

Jon Krohn: 00:25:40

Nice. Cool that you've got that entry level SQL course. I'm waiting for the follow-up course, which you're definitely going to call SQL The Sequel, right? 

Harpreet Sahota: 00:25:47

Can you add that sound effect? 

Jon Krohn: 00:25:51

Yeah, we can do that. Yeah, there we go. So Data Science Dream Job, with great courses like SQL The Sequel. It's a learning platform, and you do have small happy hours in there, too. The Data Science Dream Job, this is a platform that is a subscription platform, and it's targeted at people who might be early in their data science career, or maybe looking to transition into data science. And probably, even some people who are mid-career, they've had a couple of data science jobs and they're looking to get to the next level with a more senior job. You've got material for any of those kinds of people. I think it's amazing that you do this. How did you get into it? 

Harpreet Sahota: 00:26:43

I joined as a student myself, back in 2018. 

Jon Krohn: 00:26:46

Oh, so you signed up for the Data Science Dream Job platform? 

Harpreet Sahota: 00:26:53

Yeah. Yeah. When I started making my transition from biostatistics into data science, it was early 2018. I think I started becoming active exactly three years ago on LinkedIn, and one of the first couple people that popped up, obviously there's Kate, and there also Kyle McKiou. I started following Kyle, and joined his program in about June or July 2018. And, started off as a student of the platform myself, took a lot of the teachings and lessons to hear, made sure I showed up to all the office hours, made sure I showed up to all the mentoring calls, asked questions and was helpful. And, by the end of 2018, when I was in a position where I had multiple job offers, Kyle was like, "Hey, I'd love to have you on as a mentor," which was crazy to me. 

Jon Krohn: 00:27:41

Ah, cool. 

Harpreet Sahota: 00:27:42

My mind was blown. I was like, "What? That's awesome." And then, by the middle of 2019 he said, "You know what, let's make you the head mentor." And then just recently, "Let's make you principle mentor." I was like, "Dude, this is awesome. I'm really excited." 

Jon Krohn: 00:27:58

Incredible. You're showing it's all about investing yourself. You didn't just sign up for the platform and do it halfheartedly. I think that that is a part of the Stoic philosophy, too, is to really, with anything you do, put all of yourself into it. 

Harpreet Sahota: 00:28:12

Yes. 

Jon Krohn: 00:28:12

So your behavior there, of going to all the happy hours, all the workshops, doing everything you can, goes to show not only did it land you a bunch of data science job offers, but it now means that you're principle mentor at Data Science Dream Job itself. 

Harpreet Sahota: 00:28:29

Yeah. I never think of anything as a waste of time. If you're putting time, and effort, and energy into something, you will be rewarded with new skills, new insights, new lessons learned. And if you're going to do something, then do it with seriousness, and focus on it, concentrate on that thing like it's the only thing in front of you. I think that's really been how I've been able to manage all it is that I do. I cut out all the other noise completely, and just focus on the things that are going to inch me closer to wherever it is I'm trying to go. 

Jon Krohn: 00:29:12

It sounds like a single, huge piece of career advice, not only for data scientists but for anybody, to focus on one thing at a time, and to invest yourself fully in whatever that thing is. 

Jon Krohn: 00:29:28

With everything that we've talked about, about you. Artists of Data Science, Data Community Content Creator Awards, Data Science Dream Job ... Oh, right before we transition to what you actually do for a living, which isn't any of those other things, do you have, for listeners ... I'm sure we have tons of listeners on the SuperDataScience Podcast who would love to benefit from a platform like Data Science Dream Job, I think it sounds phenomenal, especially for people early in their career or looking to make that jump into data science, or to the next level in data science. Can you help us out, is there some kind of discount code or something that listeners could have? 

Harpreet Sahota: 00:30:05

Yeah, absolutely. It's dsdj.co, then /Artists with an S, A-R-T-I-S-T-S 70. That'll get you 70% off the course, you'll be invited to take the entire coursework that we have, look at all our history of catalog of technical skill workshops. But, you also get office hours with the other mentors who are far more awesome and intelligent than I can ever imagine. They're amazing people that are going to be able to help you. 

Jon Krohn: 00:30:39

Nice. All right, thank you so much. I'm sure many of our listeners will really appreciate that opportunity, so thank you Harpreet. 

Harpreet Sahota: 00:30:46

Yeah. 

Jon Krohn: 00:30:48

As I was about to transition, all of these things, Data Science Dream Job, Artists of Data Science, Data Community Content Creator Awards, that isn't actually how you make a living. You are the lead data scientist at Price Industries. 

Jon Krohn: 00:31:00

Price Industries, I hadn't heard of it before I was researching you, but they are an incredibly cool company. 

Harpreet Sahota: 00:31:09

Yeah, it's a massive company based right here in Winnipeg, owned by Dr. Jerry Price who like literally a rocket scientist, super smart guy. But, it's an HVAC company. 

Jon Krohn: 00:31:26

Heating and air conditioning. 

Harpreet Sahota: 00:31:27

Heating and air conditioning. The Apple Campus, the Spaceship Campus, all the HVAC in that building is done by Price. Most of the Apple Stores out there in malls, HVAC's done by Price. So it's a huge, huge company, doing some awesome stuff. 

Harpreet Sahota: 00:31:43

They hired me as their very first data scientist, to help them with a problem that they've been working on for a couple years, that they thought would be a good application of machine learning. I was able to come in, and within a few months, five to seven months of me starting, we were able to go from data to a deployed model, just me and one other guy. 

Jon Krohn: 00:32:09

Wow, that's great. Often, data science projects don't work out, so it's great that you were able to start at Price Industries, and make a big impact as their first data scientist. I'm sure they greatly appreciate that. Tell us a bit about the project. 

Harpreet Sahota: 00:32:23

Yeah, the project was for a suggested multiplier project. The way Price works is we don't really sell directly wholesale to the public. Price works with sales representatives, sales offices, field offices, and the sales representatives in these field offices, they have essentially a contract with us, an agreement to sell our product at some specified discount amount. We call that a standard multiplier. But, every now and then, they will want to get more competitive with their pricing so they can place a better bid, and seal the deal on whatever job they're working on. They'll have a special discount request come in, and these special discount requests are reviewed and approved by high level executives. They go through hundreds a week of these special discount requests. 

Harpreet Sahota: 00:33:23

So pretty much, was able to build a model, going through the last two years of historical data, and come up with a suggested multiplier that will, essentially, be the optimal multiplier based on historical information that we think will get this bid closed. Yeah man, it was a lot of fun. I just got an email yesterday from the primary stakeholder, that he was impressed with how this model is spitting out numbers, and that it's well aligned with what he would be giving out. So it's one more step to completely automating it. 

Jon Krohn: 00:33:57

Awesome. That's great, Harpreet. When you're doing work like this, your data science work, when you're building a model like that, what kinds of tools do you use? 

Harpreet Sahota: 00:34:07

For me, primarily it's Python. That's my bread and butter language of choice, and scikit-learn. 

Jon Krohn: 00:34:14

Nice. Yeah, that makes perfect sense. I think that would probably be the most common choice. 

Harpreet Sahota: 00:34:18

Yeah. 

Jon Krohn: 00:34:21

It's interesting. Coming from a statistics background like you have, so you did math education and a statistics education. Bachelor's degree at California State University in Fullerton, University of California Davis. And then, a Master's degree in math and statistics at Illinois State University. In those programs, as probably with my formal academic training, you had a big focus on R. 

Harpreet Sahota: 00:34:50

Yeah, everything was R. R is great, I learned it "growing up," when I was at Davis, when I was in grad school. R was the language of choice, I didn't even hear of Python until 2017. You know, picked it up just because the name Python sound fricking awesome. 

Jon Krohn: 00:35:12

Right. Did you know that the name Python comes from Monty Python, the British comedy troupe? They have all the Monty Python movies, they have a Broadway musical, and there was a TV series called Monty Python's Flying Circus. The Python programming language is named after Monty Python. 

Harpreet Sahota: 00:35:28

Yeah, I did not know that. That is a good piece of trivia. Yeah, that's pretty cool. Only thing I know about Monty Python is this one skit where he's like, "Fetch me a shrubbery." That's the only thing that stands out in my mind. 

Jon Krohn: 00:35:41

That's a part of one of the movies. It's the King Arthur movie, Quest For the Holy Grail or something like that, is the name of that movie. "A shrubbery!" 

Harpreet Sahota: 00:35:51

Yes. 

Jon Krohn: 00:35:53

It's the Knights Who Say "Ni". 

Harpreet Sahota: 00:35:55

That's the one, yes. 

Jon Krohn: 00:35:57

Yeah, exactly. I used to watch that movie a lot. Yeah, Monty Python had tons of skits, these bits that, I think particularly people who knew those kinds of British shows, you know a lot of these classic skits and classic lines, like the shrubbery line. As Python was being originally developed, a lot of the original demo functions, and demo datasets, they involve Monty Python skits in some way, so kind of interesting. 

Harpreet Sahota: 00:36:30

That's pretty cool, man. 

Jon Krohn: 00:36:33

There you go. So delighted to be able to teach you something today, even if it's not in any way helpful to you being a data scientist. 

Jon Krohn: 00:36:44

So R and Python, it's interesting. We tend to learn R if you come up through a formal math and statistic training, we tend to learn that in university. But then, on the job we tend to use Python more. I don't want to say that R isn't a real programming language, but Python has a lot of options for gluing to other programming languages, it's very useful in production systems, so I think that's why we end up using it more now, as practitioners. But, do you think that somebody should only ever learn one or the other? 

Harpreet Sahota: 00:37:25

I don't think so. I don't take any part in that Python versus R debate. I think data scientists should probably learn Python, for sure, mostly because if you're looking to be in an organization that is deploying models into production, then Python's probably going to be the way to go. It's a common language, between software engineers, software developers and data scientists. 

Harpreet Sahota: 00:37:49

For example, if we're sitting here having this conversation and I'm talking about in Punjabi and you're sitting here looking at me, talking in English, that's not going to work. 

Jon Krohn: 00:37:57

I would be pretty lost, for sure. 

Harpreet Sahota: 00:38:00

But software engineers, they don't really use R but they do know Python, they can understand Python, and you guys can have that common ground, that language, to work together with.

 

Jon Krohn: 00:38:13

Nice. Yeah, and Python has tons of associated tricks that you can be using in production systems. We had, in the guest episode that aired just before this one so in episode 455 with Horace Wu, he talked about how they, for a specific realtime model inference problem that they were having, they weren't getting the speed that they need out of Python so they're now using Cython, which is related to Python. But, it allows you to get more into the low level sea, and really optimize things and speed things up. Definitely, for production systems it's pretty cool. 

Harpreet Sahota: 00:38:50

Yeah. And if you're like me, coming from a background where you use primarily SAS and R, I think making the jump to Python isn't that difficult. The book I'd recommend is Wes McKinney's book Python For Data Analysis. That's an excellent book to introduce to anybody whose brand new to programming to Python. I think it walks you through the standard data structures and Python syntax, and by the end of that book you'll develop an understanding command of Pandas in four to six weeks, which isn't that long. 

Jon Krohn: 00:39:25

Yeah. Wes McKinney invented the Panda's library for working with data frames, for manipulating. Data frames are a data structure that allows you to have different data types in each column. So you can have the first column can be someone's name as a character string, the second column can be how old they are in years and that's an integer. You can have all different kinds of data types in this data frame, and Wes McKinney's made that a highly performant data structure in the Python library ecosystem. 

Harpreet Sahota: 00:39:57

Yeah. Yeah, and that book teaches you the ins and outs of it, which is really helpful. 

Jon Krohn: 00:40:02

Nice. We actually had, one of the first SuperDataScience episodes that I hosted, episode 437 with Claudia Perlich, she is a senior data scientist at Two Sigma. And up until recently, she was working alongside Wes McKinney at Two Sigma. He now has his own startup in Nashville, that is funded by Two Sigma. At least in part, maybe wholly, I can't remember. But, very cool. 

Harpreet Sahota: 00:40:29

That is. 

Jon Krohn: 00:40:30

He's done a lot. 

Harpreet Sahota: 00:40:32

Yeah. That's cool, man. These people that learn their craft at such a deep, intuitive level that they can then go and create things from nothing. Creating startups, you've worked at startups, it's not easy. And, I don't know, just to ask you this. People who don't have that level of super depth, in detail understanding of whatever it is that they're in, do you think those people can be successful in startups? Or, building a startup? 

Jon Krohn: 00:41:07

Totally. Great question. Yeah, in the episode that I just mentioned, episode 455 with Horace Wu. He is a lawyer, he formally trained as a lawyer, he worked for 20 years as a lawyer. He was inspired by providing advice to tech companies for so many years. He was like, "I want to be a tech entrepreneur." He's now onto his second tech startup, and it's a machine learning startup specifically that automates aspects of revising legal documents. 

Jon Krohn: 00:41:43

Basically, it allows you to almost magically, based on ... I'm now getting into a bit of detail on this, but I think it's such a cool company. It's called Syntheia. If you work at a big law firm, these law firms have hundreds of millions of historical legal documents. These are long documents. If you want to write a clause, a paragraph in a contract and you're like, "Oh I need to have a paragraph on intellectual property," or whatever, you can use this tool, Syntheia, which is built right into Microsoft Word, and it allows you to look up, in all of that giant historical database, those hundreds of millions of documents, historical clauses that are most like the one that you need. You can use a little bit of natural language, and then in realtime you'd get results back. You can say, "Okay, these clauses are ones I'm looking for. No, not like these," and then it goes and refreshes instantly and you get new suggestions back. 

Jon Krohn: 00:42:49

All of a sudden, we're talking about using machine learning to augment human intelligence. This is a huge example where, up until now, in all of history, if you're a lawyer you've got to remember or look this stuff up manually. Whereas now, thanks to machine learning, you can have these tools that can automatically assist you, and give you suggestions, and use the power of these huge historical develops. I think it's so cool. 

Jon Krohn: 00:43:25

This guy Horace, he still works part time as a lawyer, he's bootstrapping the startup on the side. But, he's got a big tech team that are developing it, and he can get into the weeds. He doesn't have any formal scientific or technical training, but just from Googling thousands of things over the last few years, he has a deep understanding of the models and the technical stack that they need to make this application happen. 

Harpreet Sahota: 00:43:51

That's such a cool idea. That's an important thing. Obviously, you don't need to have studied whatever math, stats, computer science to become a data scientist. Just because you did not study those things does not mean that you can not become a data scientist. 

Harpreet Sahota: 00:44:05

But, here's the interesting thing that I think is really worth noting here, is that this guy came from a completely different field and collided his field with data science, and then created this new thing. That act of creation I think is super interesting. For people out there who are thinking, "Oh my God, I'm coming from this field and I'm making the switch into data science, there's so much that I don't know, I'm not going to be able to make an impact." That outsider perspective will help you make that bigger impact. You're coming with a whole new fresh set of ideas, and whole new fresh perspective. You collide that with data science, machine learning, you can have a huge impact. 

Jon Krohn: 00:44:48

Exactly. By following the Stoic philosophy and investing yourself in whatever you've been doing, no matter what you've been doing in your life, if you've been very present and meaningful with things you've done in the past, any of those experiences are going to end up being helpful and influential. Exactly. Who knows, maybe machine learning people might never have devised a legal tool like this so it takes a lawyer to do it. 

Harpreet Sahota: 00:45:16

Yeah, right. Here's a more commonplace example that I think some of us might be more familiar with. But, this idea of churn modeling. It wasn't just invented because of eCommerce, the methodology to solve that problem was not unique to eCommerce. You and I come from a biostats type of background, that's just a survival model. 

Jon Krohn: 00:45:36

Yeah. Actually, I have a funny story about this. For people who don't know what churn is, churn is when a customer stops. If you have a subscription platform, like your own Data Science Dream Job platform that you're a mentor in, people subscribe. And, if they stop subscribing, that's churn. You can model people leaving. 

Jon Krohn: 00:46:00

One of my first interviews, I'd only been out of my PhD for a year or two, I was in an interview where they had me white boarding, they were describing a churn problem, and how I would model churn. I didn't know anything, I didn't have very much commercial experience at that time, and so I thought they were saying turn. I did this hour long whiteboard exercise where I kept saying and writing the word turn on the whiteboard. I was thinking of it as turnover, and they didn't correct me. 

Harpreet Sahota: 00:46:38

That's funny, man. Did you get the job?

Jon Krohn: 00:46:42

That's a long story, and I don't want to say anything negative about that experience. 

Harpreet Sahota: 00:46:48

All right, we'll talk about that on my podcast. That's fine. 

Jon Krohn: 00:46:52

Great. Yeah, listeners can get ... Yeah right, sounds good. 

Harpreet Sahota: 00:46:56

Yeah. 

Jon Krohn: 00:46:58

Anyway, you were talking about churn. 

Harpreet Sahota: 00:47:00

Yeah. That's just an example of taking something that worked in one industry, and colliding it with your industry. Before there was "data scientists" in an eCommerce company, they probably hired statisticians. And the statistician's like, "Oh wait, how do I model when people are going to leave? Well, I know this one thing from here that happened, maybe I can apply that here." And then they do it, and all of a sudden we have churn modeling. It's a thing, but really it's just an idea from statistics called survival analysis.

Harpreet Sahota: 00:47:31

The larger point I'm trying to make is that, even if you're coming from an "unrelated field," or you're making this transition, it's not like all of your work experience in history just evaporates and is not going to help propel you forward. All of that work experience that you've brought up to this point is going to help make you successful going forward. 

Jon Krohn: 00:47:55

100%. Great example. Are there any specific tools, or technologies, or skills that you think that listeners should be getting into over the coming years? You have a lot of experience, particularly through the Data Science Dream Job platform and your Artists of Data Science work. We talked about Stoicism already. But, is there anything else, maybe anything specifically technical, that aspiring data scientists or data scientists who are looking to make the jump to the next level in their careers, what should they be focusing on over the coming years? 

Harpreet Sahota: 00:48:40

Yeah. I'm going to say, it's not going to be any tool or technology that's not going to make sense to whoever's listening to this 150 years in the future. We're sitting here talking about Python. They're going to look at us like, "What the hell's a Python?" It's not going to be anything like that. 

Harpreet Sahota: 00:48:53

But, I think just how about the skills of learning how to learn, how about the skill of how to think clearly, how to solve problems from a ground up perspective. I think these are the skills that are really going to help propel you forward. Let's not call it a soft skill, because it's a hard skill, emotional intelligence. Being able to communicate with people, and connect with people, so that you can convey your ideas in such a way that they think that they came up with it. You want everybody who is done talking to you thinking that they know enough that they can go be a data scientist now. That's how you want to explain things to people, is to make them feel smart. 

Harpreet Sahota: 00:49:35

If you're really trying to make it to the next level, it's not about PyTorch, it's not about picking up another programming language, or learning some other algorithm, it's about learning how to learn effectively, efficiently. And then, learning how to interact with people in a way that is going to benefit both of you guys. So, start learning how to play positive sum games. 

Jon Krohn: 00:49:56

Great answer. Interestingly, that same Horace Wu, the lawyer now machine learning entrepreneur, he also said learning how to learning as his answer to this question. And, I didn't mention it in that episode but I remembered subsequently, since we filmed, that there's a company called 80,000 Hours which is really cool, they're a charity. They're backed by Y-Combinator, so the really famous startup accelerator, but it was a Y-Combinator program for charities. 80,000 hours is the average number of hours, roughly, that you have in your career. 

Jon Krohn: 00:50:39

What this startup does, startup charity ... It was founded by someone named Ben Taylor. No, not Ben Taylor, Ben Taylor's who we know. Benjamin Todd, his name is Benjamin Todd. I just had Ben Taylor on my mind because you and I are always dealing with him. Benjamin Todd founded 80,000 Hours, and it's a company that tries to ... They started off by providing one-on-one guidance on how you could have your most impactful career. I actually did an interview with Ben Todd years ago, when I was transitioning. Well, this was part of why I transitioned out, was through this work that I did with them. I transitioned out of being a trader at a hedge fund, so deploying quantitative models, high frequency trading, using data science in financial markets, and leaving and going into a space where I could be communicating more openly with the public about what I'm doing, and doing this education and podcasting stuff that I'm doing now. 

Jon Krohn: 00:51:43

So 80,000 Hours tries to use as much possible research, quantitative data, to provide you with guidance on how you can have the most impact in your life, particularly your career. And, their research, I remember a research paper that they did from years ago, the number one skill to be successful in a career and have a big impact is learning how to learn. And the second thing, if I remember correctly, was exactly what you said about being able to communicate your ideas effectively. 

Harpreet Sahota: 00:52:18

Yeah well, there you go, man. I don't want to leave your listeners without any tangible places where they can go to learn about learning how to learn. So Coursera has this massive course, I think it's the most popular online course in the world, Learning How To Learn is the name of the course on Coursera, absolutely free. 

Jon Krohn: 00:52:37

Wow. Cool, we'll put that in the show notes. 

Harpreet Sahota: 00:52:39

Yeah, it's taught by Barbara Oakley who I had the pleasure of interviewing for the podcast, who also wrote a book called A Mind For Numbers, which I highly recommend checking out. And, there's Jim Quick's book Limitless, which is also phenomenal. 

Harpreet Sahota: 00:52:53

And then, here's one book that I'm reading. It's actually an older book, but I just came across it. It's called Pragmatic Thinking and Learning by Andy Hunt. So, Pragmatic Thinking and Learning. This is one of the coauthors of The Pragmatic Programmer, I'm not sure if you've heard of that book. 

Jon Krohn: 00:53:12

Oh yeah. Yeah, that's one of the bestselling Addison Wesley books of all time. 

Harpreet Sahota: 00:53:17

Wow. 

Jon Krohn: 00:53:19

My book, Deep Learning Illustrated, is published by Addison Wesley. I'm aware of this Pragmatic Programmer, it's one of the bestselling software books of all time. Yes, yes, yes. 

Harpreet Sahota: 00:53:31

This book is amazing. I'm interviewing Andy Hunt next week, the interview is set up. 

Jon Krohn: 00:53:36

No way! 

Harpreet Sahota: 00:53:37

Yeah. 

Jon Krohn: 00:53:38

Wow! 

Harpreet Sahota: 00:53:39

The interview itself won't be up until probably the end of 2021, or middle of it, who knows. But yeah, going to get a chance to interview him and we're going to go on just how to develop mastery and things like that. 

Harpreet Sahota: 00:53:52

Another book for you guys is Mastery by Robert Greene, which is a phenomenal book on how to just cultivate and develop, essentially just the right mindset and the right frame of mind to be come a master in your field. 

Jon Krohn: 00:54:06

Yeah, I've read that book. I read the entire thing. I felt like some of the examples went on a bit long. 

Harpreet Sahota: 00:54:13

Yeah, he has a tendency to do that. Yeah. 

Jon Krohn: 00:54:17

But, it was hugely valuable. As I was growing through Mastery, he talks about, in so many different disciplines, how people have become masters of their field and the process, the formal process, as you become a master in your field. Absolutely fascinating. Because his examples are so in-depth, as I was reading sometimes I was like, "Where are we getting to with the point here?" But now, in retrospect, because those examples were so detailed, things that happened in my life triggered memories of those very specific examples that he was giving and the lessons from those examples. And so, it has ended up being really helpful in my life. 

Harpreet Sahota: 00:55:02

Yeah man, all of his books are amazing. You have to listen the podcast I did with him, which is releasing on the one year anniversary of my podcast which is going to be on April 9th, where I'm releasing that episode. 

Jon Krohn: 00:55:14

Amazing. Yeah, that'll be right after this episode airs. This episode should air on April 1st or thereabouts. Cool. 

Harpreet Sahota: 00:55:22

Yeah. But in general, man, let's not even call it a skill, let's call it the personality trait that you can cultivate and develop for yourself is just the trait of wanting to get better, wanting to become more. Just always having this constant bit of agitation in yourself, I think is a huge personality trait. That agitation is a good agitation, not wanting to be complacent and just always wanting to grow, always want to learn, always being curious. There are going to be the skills that, I think, are going to take you to that next level in whatever career you're in, because the technical skills are going to fade, they're going to come and go, but these personality traits, these character traits, I think these are lasting and eternal. 

Jon Krohn: 00:56:05

For sure. I couldn't agree more, and you said it beautifully. Harpreet, I usually end the episode by asking for book recommendations, but you've just given us a slew of them and they are perfect. We'll wrap things up by asking, how can listeners contact you, or follow you? You have so many amazing venues for connecting with data scientists and making a big impact on their lives. I think that you're an exceptional person that they could learn a lot from. Obviously, we know about some things like dropping into your Friday Happy Hours or your Sunday Office Hours with Ayodele. Of course, there's the Data Science Dream Job platform, which is probably the way to get maybe the most small group size impact from you. 

Jon Krohn: 00:56:53

But more generally speaking, are you active on social media? Can people follow you there? 

Harpreet Sahota: 00:56:57

Yeah. LinkedIn is my social media of choice, so look me up, Harpreet Sahota. The backslash to that is LinkedIn.com/harpreetsahota204 because there might be a few of me out there. But, that's the one. 

Jon Krohn: 00:57:11

Nice, nice. We'll have it in the show notes. 

Harpreet Sahota: 00:57:11

Yeah. That's my primary social media of choice. I've got an Instagram, the Artist of Data Science, and I've got Twitter at @ArtistsofData. Just picked up on Clubhouse as @ArtistsofData, so find me on there. I'm hoping to get more active on Clubhouse. And, the podcast you can find it anywhere, the Artists of Data Science. The website for that is theartistsofdatascience.fireside.fm, because my website is completely trash right now because I don't have a team. 

Jon Krohn: 00:57:46

Nice. Well thank you so much, I'll catch up with you again for sure. The latest would be the Data Community Content Creator Awards, I can't wait to see it happen. Thank you so much for being on the show, Harpreet. Yeah, I think listeners can expect to be hearing me on an episode of Artists of Data Science at some point as well. 

Harpreet Sahota: 00:58:09

Yeah, absolutely man. Looking forward to having you on. Thank you again for inviting me to be on this show, it means a lot to me, it's a huge platform. Thank you guys, if you're listening to this point in the podcast, thank you for sticking with us that long. Appreciate you guys giving us your time and trusting us with your time. I just want to leave you guys with my standard farewell message and that's you've got one life on this planet, why not try and do something big? Cheers everyone. 

Jon Krohn: 00:58:35

Beautiful. Thank you Harpreet, and see you again soon. 

Jon Krohn: 00:58:43

Harpreet is so cool, isn't he? He oozes capital S Stoicism, and practices what he preaches by giving his data science career his all and building a massive community of data scientists committed to helping each other succeed. In today's episode, we covered the Data Community Content Creator Awards, Harpreet's inspiring Artists of Data Science Podcast with its fun and free Office Hours, the deeply supportive and interactive Data Science Dream Job platform, and the critical skills it takes to succeed at any level in a data science career, particularly learning how to learn and communicating data effectively. 

Jon Krohn: 00:59:21

As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, and the URLs for Harpreet's LinkedIn, Twitter, and Instagram at superdatascience.com/457. That's superdatascience.com/457. If you enjoyed this episode, I'd of course greatly appreciate it if you left a review on your favorite podcasting app or on YouTube, where we have a high fidelity, smiley face filled video version of this episode. 

Jon Krohn: 00:59:52

I also encourage you to follow me or tag me in a post on LinkedIn or Twitter, where my Twitter handle is @jonkrohnlearns, to let me know your thoughts on this episode. I'd love to respond to your comments or questions in public and get a conversation going. You're also welcome to add me on LinkedIn, but it might be a good idea to mention you were listening to the SuperDataScience Podcast so that I know you're not a random salesperson. 

Jon Krohn: 01:00:15

Since this podcast is free, if you'd like a hugely helpful way to show your support for my work, then I'd be very grateful indeed if you made your way to the Data Community Content Creator Awards nomination form, the link is in the show notes. Of course, we'd love you to nominate this SuperDataScience Podcast for category seven, the podcast category. I'd also love my name, Jon Krohn, nominated for category eight, the textbook category, for my book Deep Learning Illustrated. And finally, I'd also love my name, again Jon Krohn, nominated for category two, the machine learning and AI YouTube category, for my YouTube channel which contains tons of free videos on deep learning, linear algebra applications, and machine learning libraries. 

Jon Krohn: 01:01:00

All right, thanks to Ivana, Jaime, Mario and JP on the SuperDataScience team, for managing and producing another great episode today. Keep on rocking it out there, folks, and I'm looking forward to enjoying another round of the SuperDataScience Podcast with you very soon.