March 28, 2024

Reviewing Your Data Science Projects - Episode 15 (Quant Finance)



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.

You may also like to read about:



[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

Resources:

Similar videos

2CUTURL

Created in 2013, 2CUTURL has been on the forefront of entertainment and breaking news. Our editorial staff delivers high quality articles, video, documentary and live along with multi-platform content.

© 2CUTURL. All Rights Reserved.