Published May 21, 2023, 1:20 a.m. by Naomi Charles
Data science is a process of extracting knowledge from data. It is a process of understanding and exploring data to find hidden patterns, correlations, and insights. The goal of data science is to turn data into actionable insights.
In this episode of quant finance, Ken Jee reviews your data science projects. He discusses the process of data science and how to approach data analysis. He also reviews some of the most popular data science libraries and tools.
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[Music]
hello everyone
ken here back with another video where i
review your projects resumes portfolios
and now
sometimes linkedin profiles special
thanks today
to sheila i hope i'm pronouncing that
correctly
i'm absolutely butchering these names
lately but he submitted his resume
and his github for review his work is
fairly unique because it's very focused
on the finance sector so he's looking to
work as an analyst a data scientist
in that realm so i think he does very
well on some things and there are a
couple tweaks he could make
i think that again this is a really good
use case to talk about how you can
niche down and target some very specific
roles and that how that can potentially
help you
i think sheila actually landed an
internship within this domain
according to his github profiles so this
could be in a sense a success story and
you can follow along with some of the
things that he does well
if you'd like your projects portfolios
resumes or linkedin profile
reviewed please comment below to let me
know and also shoot me an email at
kenji.ds
gmail.com all that information will be
pinned
in the first comment of the uh on the
video
so without further ado let's hop in to
the
resume review here so
this is a very good just traditional
resume
he has um at the top i blurt out his
email and his phone number
it's perfectly fine i think this is the
type of resume most
companies are used to seeing and i think
that that's perfectly fine
one thing that i like that he does is
that he makes it clear that he's a us
citizen
i think sometimes there can be some
ambiguity about that
and companies up front
generally want to know that if there's
any chance that they
that there might be some uncertainty
about that you might as well include it
as well
his skills again i love that they're up
top
he has a lot of relevant python a lot of
relevant skills for this field something
that i think
might help him out a little bit more
would be to have
a couple of packages or a couple
of different tools that are more related
to finance so
in finance you're still using excel
quite a bit i would expect to see that
in
a resume related to finance if there's
any python packages that are directly
relatable
perhaps even the yahoo finance api just
having familiarity with those things and
making it clear
that you have skills within that domain
could be really valuable
that also goes for his education i love
seeing the relevant coursework here
but if he has taken any finance courses
i think it would be really important to
put those in here because it shows that
you have a bit more depth
if he's taken any certificates or
watched any or done any
course work outside of school related to
finance i would also put that in here
i think that could be really relevant he
goes on to show that he does have a lot
of experience in finance through his
extracurricular activities
and this is done really well for someone
who doesn't have
as much true work experience or
internship experience
i love how in depth he goes into his
extracurricular activities
and a lot of people don't realize how
valuable these extracurriculars can be
so he's on the first robotics team he
does a great job talking about these
things
he's part of the algorithmic trading
club it looks like he really wants to go
into algorithmic trading in general
so this is the perfect club for him to
join and get involved in
if you're in college if you're in school
i cannot stress
enough how important it is for you to
join clubs or even start clubs
if there aren't ones that you
that meet your specific interests in
grad school i started a data science and
sports analytics club
and a lot of people when i was
interviewing actually asked me about
that
so they're interested in that for two
reasons the first is okay he's clearly
interested in domain
what did he learn who did he bring in
what were the speakers like
what was that experience like the second
thing
is that they really like to see
leadership experience that you can work
well with others and that's a
clear point of departure for a lot of
people
uh kind of i guess third thing also is
that it shows that you can get along and
potentially work on projects with other
people
so that's another kind of icing on the
cake for that type of experience
again he's in a lot of these different
programs and i think that's really good
it shows a lot of the things that
you would look for in someone's work
experience and if you're early in your
college career or if you just haven't
had those opportunities yet
this is a great way to supplement some
of that
all of his projects are also very
clearly related
to the positions he's applying for
so there's a current currency swap
arbitrage
opportunity um i didn't see this in his
repo
i i think it's interesting that he says
code available upon request
you know that's something that he could
probably write a blog post about or he
wouldn't have to share that code if it's
still being in use
uh the the time series forecasting with
machine learning
and then a trade stat logger
in his github so i think his resume is
really good
i would like to see um
you know i i wouldn't make too many
changes other than integrating his
schoolwork or infusing his schoolwork up
top with some financially
related things uh if he wanted to
you know when he does have this this
internship that he has now on his resume
i would like i'd be interested to see
how he talks about that where he fits it
in
you know does he make a new section what
would that look like
um i i think he could even
switch out extracurricular activities
and call it leadership experience
or some other sort of experience
the kind of branding might work fairly
well there
obviously he was able to land an
internship so i think that
again this resume worked reasonably well
and with a few tweaks
i could really push him forward in the
future
of getting a full-time job not just
internships so let's switch over
to his github profile so it's a nice
professional high quality photo
this is looks like a professional photo
one thing
again if you're in school that i really
recommend you might be able to get
free professional photos taken i know
that a lot of entrepreneurship
departments a lot of business
school departments sometimes even a lot
of cs departments offer this service
100 capitalize on it if they offer it
um also ask if they offer or if they'd
be willing to put together an offering
for that at your school your your um
you know like the guidance counselor for
for careers whatever that might be
that's a really great opportunity
professional photos can go
a a a long long way he says where he's
working
he says you know where this company is
located etc
um and then he has all of his repos so
i think that he could probably go with a
bit more
with a bit clearer naming conventions
for these so tutorials i mean
he says what they are i think that's
totally fine uh one thing i've been
seeing a lot is people pin
repos that are most relevant so he might
want to consider doing that
or as i've talked about in like the last
three or four videos
having a landing page that
my friend import data has talked about
and has a tutorial on
so i'll link that above and below i
think that's really good
and i keep saying i'm going to do it for
mine but i haven't gotten to it yet so
guys hold me accountable you can yell at
me in the comment section if you need to
so the first thing that i liked was this
get all tickers code so this is really
well documented
this is something that he expects other
people to be using
and i really like the logic of why he
built this so he couldn't find any
libraries to retrieve all the tickers
for all the different stocks
so he went out and solved this problem
himself with some light code
those are the exact types of projects
that i would like to see as a
manager as someone who's hiring or just
a person in general like this is useful
to me
i'm actually thinking of doing some some
machine learning type projects with some
stock data
and i might actually use his exact
package
of course i will cite it i'll fork it
i'll do whatever uh
but making your work valuable to others
when you see a problem
solving it with your with your own
skills is i mean this project might very
well be what landed him his first
internship
so again a really really good
documentation here
shows how to go about doing that you can
choose which exchange it's coming from
and again just all around this isn't
necessarily a data science project
but part of data science is the data
collection and this does that really
well
i expect that he probably used this in
one of his more data science related
projects in general
he also has a one of the
competitions that he participated in
again he's
worked really hard on the structure of
the readme this is a high quality readme
and i really like
why he chose why he noted the reasons
that he chose java over python
something i recommend in all of your
code and all of your readmes is when you
whenever you make a key decision you
should explain why you do it
or why you did it and again that's the
perfect use case for java versus python
here
in my project from scratch series uh
tutorial i guess video or project
walkthrough whatever you want to call it
i use jupiter notebooks for some of the
analysis
and spider for for some of the other
different parts of the analysis
and i make a very clear use case why so
for the exploratory data analysis
you want to be able to interact with it
in real time it makes more sense to use
a jupyter notebook for the other stuff
you're implementing it you'd want to run
it
as a script potentially so it makes more
sense to do it as a raw python file
so whenever you're making those
decisions it's very important to explain
why
and to kind of tell a story about what's
happening here
another a good one is the trade sat
logger
that he does here i think that again
these are really strong
this is very very well documented and
one thing i like to see with these
are the examples this is something
that's very important
he has contact information this is
pretty uh as textbook as you can be
about um you know about what a project
should
probably look like again not purely a
data science project this is more of a
programming project
but this is very well done i again might
actually
use this for my project that i'm
planning hopefully by the end of the
year
so let's get into some of his actual
machine learning projects
which i think probably are more relevant
for this
so obviously he he really slimmed down
on the readme here
this is something where i think he could
really improve his chances
if he's applying for a pure data science
role so if he's doing machine learning
engineering
the other projects are probably fine but
we still want to see better
documentation
in this very similar to how we set up
the other readmes
so let's see what this looks like
actually i think i had it loaded here
well
so he goes through he does use comments
fairly well and he talks through the
steps
so this is something i would probably
like to see in the readme as well it's
fine if he just copies it
in both places
so he gets a list of tickers
and he uses the um
sklearn i guess that's
uh i guess that's their version of the
neural net in sklearn
that's something i've never used before
i'm actually learning something new with
you guys
i might experi experiment with this a
little bit maybe in my next
upcoming uh kaggle video that'll come
out
a little bit later this looks like the
implementation slightly easier than
keras or some of the other
architectures out there so again might
explore with this this looks pretty cool
generally this project is is pretty good
it's a little bit short but again it's
right related to the use case that he's
trying to understand
so again if if he can go into
an interview with someone related to
finance or
or a quantitative trading whatever that
might be
and show them that he's built tools to
be able to do this tools that they might
already be able to use and he can run
analysis using them
i think that's a really really strong
thing it's a really
like powerful hey i can show you the
work i've already done
to further the work that you guys are
doing at your company so
i highly recommend i would almost say
for
every new position you apply for if
you're really crazy about it
or you can find four or five companies
in the same sector
do a project related to that sector you
can use it and talk about it in each of
these different interviews and it can
really help you along
the next one that i saw was this value
investing
again even more sparse on the
on the readme here so maybe talk about
what value investing is
i believe that's from warren buffett
where we're talking about
um you know investing for the long term
and he's probably trying to create that
here
so let's look at what this looks like
again he does a
good job here about explaining what it
is what the strategies are
so when it looks cheap and selling it
once it seems expensive
seems pretty intuitive i'm sure warren
buffett would be pleased
um maybe a little bit more sparse with
the commenting here but overall
both of these projects have looked very
good
they're pretty straight and to the point
i potentially like to see a bit more
exploratory analysis
i think using some graphs that are
familiar to people in finance
you know you see a lot of those uh kind
of like
graduated bar charts that probably looks
pretty cool
and that you know that's used a lot in
technical trading
so mixing some of the things that people
are expecting to see in
could take this even further so i really
liked
sheila's profile and his resume
they obviously worked in lending him an
internship again i think he can take
this a step further
especially when he's focusing on his
machine learning projects
documenting them just a little bit
better and also
you know making sure that his
educational background
is is showcasing as much of the finance
related stuff as possible
so i hope that this is useful to anyone
who is either looking to get
a job in quantitative finance is looking
to get a job as an analyst in data
science in college
i think that there's a lot of things
that that
can be used as best practices in his
work here
especially related to creating projects
building the tools
that you believe can help you do the
analysis you want to do
so thank you so much for watching and
good luck on your data science journey
you
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