hey guys so the other day I had an
interview with Peter Scopus who is a
data scientist with experience at
companies like TuneIn which is an audio
streaming company in San Francisco and
in this interview he shared some tips
that he used to get his six-figure data
science job and I thought it was really
helpful so I want to share it with you
guys today and hopefully you'll find it
helpful too
so you're a data science job at tune in
was your really first data science job
right right yeah could you tell us a
little bit about like what you actually
did there yeah so I feel like the the
work I did there can kinda be separated
into sort of a couple of different
categories one sort of part of my job
was kind of helping product managers
design and implement and analyze a beat
s so I'd sit down with a product manager
and discuss you know a product or
feature change and sort of the best way
to test that change and then help design
the experiment and then I sort of put
together an analysis of that experiment
to sort of help the product team think
about how to move forward another aspect
of my work there was sort of designing
various metrics and putting together
different types of dashboards to help
you know the marketing team or the
product team as we would be able to
understand how things are working but a
lot of my work in it I feel like sort of
the bulk of my work was centered a
little bit more around modeling and
prediction so answering things like you
know if our customers did X Y or Z can
we predict what sort of they want to do
on the platform or can we put you know a
monetary value on a certain feature you
know meaning if we we got rid of that
feature how many of our you know paying
customers would would churn know so
prior to this
call you were telling me that you
struggled a little bit when you were
trying to get your first data science
job could you tell us a little bit about
that um yeah yeah so like I said like a
couple of the issues was you know
learning like the different type of
skills that I needed and and sort of
stuff like that but I kind of felt like
there's five sort of like really like
major things that I learned that are
like super important for getting your
first data science job and the first one
is sort of just making sure that you
want sequel it's kind of the sort of
like foundational skill at data science
and it's definitely not the most like
exciting skill but it's just like
incredibly necessary
another thing that's sort of really
important is you know just being able to
sort of like explain your experience in
a way that makes sense to product
managers and to executives so when
you're in school and when you're doing
research you're you know mostly
surrounded by other technical people but
when you're working for a company you
know you're gonna be interacting you
know with non-technical people all the
time and then all right um let's go
through those sort of one by one okay so
for sequel what do you think would be a
good way to learn sequel so what I think
is probably the the easiest way is to
kind of just like learn by doing and
there's definitely a lot of a lot of
tutorials out there that I feel like
kind of differ and how helpful they are
I found this tutorial from mode
analytics that was kind of like an
introduction to sequel that I thought
was super helpful and you know it was
able to sort of help me prepare
for like even the what I thought were
the hardest sort of sequel questions I
was given during the interview process
right okay so for your second point of
being able to explain your experience in
a way that makes sense to PM's and
executives because you may be you know X
because you may be give us an example or
maybe tell us a little bit more about
that yeah so I felt like when I was
first starting to apply like I don't
know why I didn't think about this but
just that there's going to be you know
product managers and executives that are
going to be part of the interview
process and so you know a lot of times
you'll be asked you know by them to talk
about you know like a time that you use
data to you know like analyze a problem
or like analyze like a business problem
and sort of when I first started
interviewing I feel like I prepared for
that question
but I prepared to answer that question
to like other technical people so like I
felt like I had a good answer to that
but I only had a good answer to that
when I was explaining it to someone who
like had a background in programming or
had a background in statistics and so I
basically had to sort of like go back
and figure out a good way to explain it
you know but to someone who doesn't have
that background so how did you change
that in your particular case I mean yeah
so at UC Berkeley I researched carbon
taxes and sort of the impact of them
unlike manufacturing prices and so sort
of in my whole like technical discussion
of that I talked about the different
models I used and the different
assumptions I made and when I tried to
do when I shifted that to sort of talk
to
you know a product manager about that I
tried to walk them through more like a
more I don't want say like a simple
version but just like talking about like
how old prices are impacted by taxes and
sort of like how like we went about the
the process of like trying to determine
by how much so I guess you focused on
the end result more then at the price
itself like yeah yeah yeah less on I
feel like that sort of like technical
part of like how we got there and yeah
more about like sort of like the impact
of that project like what it means stuff
like that okay let's go through the rest
of the tip see you had it earlier okay
yeah yeah so another another tip I have
is just sort of putting together and and
showcasing personal projects I think
that this is sort of especially
important for your first data job and
when you're sort of like inexperienced
in and maybe don't have much experience
outside of school and this can be
something as simple as just kind of
going online and finding like a free
public data set and putting together
visualizations and sort of doing your
own analysis or in my case what I what I
also did was I just kind of used some
connections and I was able to convince a
company to to basically let me do sort
of like basic data science work for them
for for free I was just sort of like
this is like a little bit of experience
I could put on my resume and I'm sort of
like talk to talk about two companies
during interviews nice walk I know if
they don't know is this did you do for
them so it was mostly just like
visualization stuff it was a I was like
a commercial real estate company
that had like various like sale and
purchased data on like different like
real estate transactions I could cross
United States and so I just put like a
couple of visualizations together about
like where different
like types of real estate transactions
were happening
and sort of like what different trends
they could see stuff like that all right
do you have any tips on how to like
showcase your personal projects yeah I
think like a really good way to do it is
to just sort of like put together like a
basic website where you can you know if
you're putting together like different
types of visualizations you can you know
like post a couple of them they're sort
of like talk about them and then I'll
also have a link to to code like where
your code is so that when you you know
are interviewing and you've sent out
your resume and it has that website you
know on your resume recruiters and
whoever else is looking would have a
chance to sort of like browse your
website and just like a general idea of
sort of your skills related to that
right okay so earlier you mentioned that
you have five tips
I think we've discussed three so far
sequel being able to explain your
experience in a certain way and
showcasing projects well or the other
two yeah so the other one are one of the
other ones is just being able to sort of
like showcase your like business or your
product sense so you can kind of thing
this is like you know being able to
discuss with an interviewer how you
would use data to define like the
success of a product or whether like
certain aspects of the business are
doing well and you know being able to
like understand and ask questions about
like the product roadmap sort of like
stuff like that I think that like your
technical skills are super important but
you know at the end of the day your job
is you know to help the company you know
grow or scale or you know improve their
revenue stream or something like that so
you need to take sort of like your
background and always think of it in
terms of like more of like a business
mindset so how did you cultivate your
business and product sense I feel like a
couple of ways one was I I had found
this tutorial that was on there's also
on mode analytics actually and it was
sort of like a like a business analytics
type thing where it was more like
focused on like case study questions
like in terms of like oh this metric for
some like hypothetical companies down
how would you go about like thinking
through sort of like the process of like
trying to figure it figure out like why
that's the case
and I felt like that was helpful to
prepare for like some of those case
questions during the interview process
but I also feel like what was also kind
of helpful was like really just as I was
going through the interview process like
learning more about like various
startups and like thinking through like
what their goals were and just like
thinking about you know like I can use
data to you know help answer this
question like oh I know what I could do
for for this
and it's kind of like stop thinking
about you know like data and statistics
in a vacuum and instead think of it more
as like I can do this or use this
methodology to like answer this question
sort of like stuff like that so was he
like using some apps and thinking about
how you might use data for them yeah
like that yeah that's like a like a
really good strategy is you know like
say you do have an interview at I don't
know let's just let's just say like like
Twitter or something you know you could
you know use the app and think about
like oh like I think like this would be
like a good like product improvement or
a good feature improvement or you know I
wonder if like this is like a pain point
for users and so you think about those
questions and then think about to answer
that I wish I had like this data or I
think like you know this type of data
would be super helpful and just kind of
yeah like having an example of like a
product and and and sort of thinking
about ways to improvement and and sort
of how you would like define improvement
or how you would define like success of
a feature okay and what was the last
step you you had the last one was like
being really good at the basics I think
that a lot of aspiring like data science
and analytics applicants kind of just
immediately jump into you know fancy
machine learning and deep learning
models and they kind of forget about the
basics of of like how to run a good a B
tests how to clean data sort of the
basics of statistics stuff like that and
you know there's definitely a place for
you know machine learning and deep
learning type stuff but for your first
data science job you're most likely not
going to be working on you know that
type of stuff you know unless you are
coming out with a PhD and like even then
you just don't have you know any
experience and so like there's just a
good chance you're not going to be on
you know a team that sort of has the
capability to to let you do a lot of
that work and so just like knowing the
basics and starting there is what I
think is sort of like a really good
strategy for fruit landing your first
data job and how do you actually become
good at those basics I mean in terms of
like programming I mean we talked a
little bit about like just being really
good at sequel to get better at stuff
like Python and R I think you know
what's really important is you know like
finding your own data or something like
that and just kind of focusing on you
know getting better at cleaning data
getting better at you know putting
together you know clean nice-looking
visualizations and then in terms of
statistics I don't really have like a
great recommendation for like a
statistics course but I would definitely
you know spend time I'm like reading
about how to run a good a B test just
sort of the more like stats 101 stuff
that I feel like people just like tend
to forget about sometimes okay so those
are Peters five tips but one thing he
didn't mention is that in a lot of data
science interviews they ask you probably
the related questions and to practice
those I actually recommend this videos
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alright anyway see you in the next video