Phil Supinski discusses how to become a data scientist in his detailed presentation.

1. H O W T O B E C O M E
A D A T A S C I E N T I S T
PHILSUPINSKI.COM

2. I N T R O D U C T I O N
Data science isn’t for
everyone. Success
requires an aptitude
for math combined
with creativity and
curiosity. More
succinctly, success is
being able to
manipulate the data
into telling stories
and giving insights.

3. Q U E S T I O N S T O A S K
Y O U R S E L F
A R E Y O U J U S T I N
I T F O R T H E
M O N E Y ?
C A N Y O U
P R O G R A M ?
D O Y O U L I K E T O
S P E N D Y O U R F R E E
T I M E L E A R N I N G N E W
T H I N G S ?
Are you just in it for the
money? Salaries rise and
fall and eventually
stabilize around a market
value. Data science is hot
right now… But it won’t
always be.
Many of the tools
for data science
require the ability
to script and/or do
full programming.
In this rapidly
growing field, you
will have to work
and learn to stay
on top.

4. TIME TO GET WORKING. HERE’S
WHAT YOU NEED TO DO TO LAND A
DATA SCIENCE JOB.
S T I L L
I N T E R E S T E D ? . .
G R E A T !

5. E D U C A T E
Y O U R S E L F
There are lots of resources. In my
opinion, if you already have some
computer and math skills, you
should take a condensed course,
like the one at Galvanize. You
could just educate yourself, but a
certification carries a lot of weight
with recruiters.

6. N E T W O R K
Jobs are much easier to land when someone will
pass your resume directly to the hiring
manager.

7. D O A P R O J E C T
Find some data and make it tell a story.
Preferably something that is interesting to
the field you’d like to get into. Start here
for all the data you could ever want.

8. F A M I L I A R I Z E
Y O U R S E L F W I T H T H E
T O O L S
You never know what tools you will need to
use. Could be something like tableau or
just plain old python. The more tools you
have the faster you will find success.

9. F I N D 5 T A R G E T P O S I T I O N S
Go to job search boards such as
Glassdoor, LinkedIn Jobs, or Indeed
and search for data science
positions in your chosen industry.
Try to search for a variety of terms
instead of just “data scientist”. Try
terms like data analysis, machine
learning engineer, or quantitative
analyst. Often times many people
discover a problem with too many
options rather than a few, so
eliminate many of them.

10. O V E R A L L
At the end of the day, data
scientist positions are
competitive, but it’s all a numbers
game. The best way to maintain
momentum and positivity is to
keep a pipeline full of
opportunities you’re excited
about.

11. T H A N K Y O U !PHILSUPINSKI.COM