SDS PODCAST EPISODE 457: LANDING YOUR DATA ......to land your data science dream job, whether you're...

Show Notes: http://www.superdatascience.com/457 1

SDS PODCAST

EPISODE 457:

LANDING YOUR

DATA SCIENCE

DREAM JOB

http://www.superdatascience.com/457


Jon Krohn: 00:00:00 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.


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.


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.


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?


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.


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.


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.


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.


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.


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!"




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.


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!


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.


SDS PODCAST EPISODE 457: LANDING YOUR DATA ......to land your data science dream job, whether you're...

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