SDS PODCAST EPISODE 457: LANDING YOUR DATA ......to land your data science dream job, whether you're...
Transcript of 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
Show Notes: http://www.superdatascience.com/457 2
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!
Show Notes: http://www.superdatascience.com/457 3
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
Show Notes: http://www.superdatascience.com/457 4
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.
Show Notes: http://www.superdatascience.com/457 5
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
Show Notes: http://www.superdatascience.com/457 6
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.
Show Notes: http://www.superdatascience.com/457 7
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.
Show Notes: http://www.superdatascience.com/457 8
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
Show Notes: http://www.superdatascience.com/457 9
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.
Show Notes: http://www.superdatascience.com/457 10
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
Show Notes: http://www.superdatascience.com/457 11
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
Show Notes: http://www.superdatascience.com/457 12
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.
Show Notes: http://www.superdatascience.com/457 13
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
Show Notes: http://www.superdatascience.com/457 14
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
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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.
Show Notes: http://www.superdatascience.com/457 16
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
Show Notes: http://www.superdatascience.com/457 17
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.
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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,
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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
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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.
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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.
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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.
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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.
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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
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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.
Show Notes: http://www.superdatascience.com/457 26
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,
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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,
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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
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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.
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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.
Show Notes: http://www.superdatascience.com/457 31
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.
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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
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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.
Show Notes: http://www.superdatascience.com/457 34
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.
Show Notes: http://www.superdatascience.com/457 35
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
Show Notes: http://www.superdatascience.com/457 36
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.