Owen became interested in data science after working as a software developer in the insurance industry where he was able to learn domain expertise. While he majored in engineering, he took useful courses in linear algebra, programming, and teamwork. As a developer, he learned the importance of understanding the domain and business language. He transitioned to data science by participating in Kaggle competitions and finding the right mentor. Owen emphasizes the importance of asking the right questions over getting perfect answers. He provides an example to illustrate the difference between correlation and causation when analyzing relationships between variables like being good at math and becoming a data scientist.

1. How I became a Data Scientist
Owen Zhang

2. Let’s tell a story
● Just to prove that I can talk other things than kaggle
● Today’s goal, as always, is to entertain, not enlighten.
● Apologize for presuming myself to be “experter data
scientist”.

3. What It Takes to be a Good Data Scientist
● Domain knowledge
● Coding skills
● Math/Stats
But maybe equally (or even more) important:
● Ask the right question
● Tell a good story

4. How Much Math(/Stats) is Required?
● Math is an extremely broad field
● Personally I am good at numerical problems but bad
at algebra
● Guestimate has always been my strength vs “precise
answer”
● Having good intuition is more helpful than being
able to prove theorems

5. Majored in Engineering but...
Always wanted to be a “Data Scientist”
● Unfortunately that didn’t exist at that
time
Three useful things learned in college
● Linear algebra
● Programming
● Teamwork (a.k.a. party with your
friends)

6. Even after Y2K, there were plenty of IT jobs
● By chance I got a job as software developer
● By chance it was in insurance
○ Arguably insurance has the best data to practice
data science on
○ Very noisy
○ High variety
○ Not too small and not too big

7. The Most Useful Things Learned Doing IT
● It is NOT how to program!
○ My coding skill probably degenerated
● Be interested in learning the domain
○ I learned my “domain expertise” here
● Speak the “business language”
○ Terminology is very important
● How to talk to IT folks

8. What to do when bored with your job?
● Career switch!
● The following approach isn’t recommended:
Wanna
be a
chef?
I’ve never
cooked before
but you can
trust me

9. Lesson learned in switching careers
● It is counter productive to talk about how
you would be good at something that you
haven’t done before
● Use cases / stories
● Find the right mentor/sponsor

10. Don’t Laugh, but Almost Became an Actuary
● Why?
○ Actuaries were doing “data science” way before
“data scientist” became a job title
○ My wife is an actuary
○ I am good at taking exams
● Why not?
○ Data Science came along before I finished all the
exams

11. Finally made it to Data Science
IT Developer

12. Finally made it to Data Science
IT Developer Data Scientist

13. Became “Expert Data Scientist”
● It is both easy and hard to transform from “some IT
guy who wants to be a (predictive) modeler” to
“expert data scientist”
○ The trick is to get new colleagues
● At that time it was called
“predictive modeler”
● “Legitimized” by Kaggle
Kaggle

14. What I Learned being a “Practitioner”
● The most important insight:
○ Asking the right question is more important than
getting the perfect answer
● The right “form” of question:
○ What will/can you do differently if you have a
prediction of [????]

15. If We Finish here...
● Then we would have made a very common mistake
in data analysis
○ All we have is an anecdote
● Enemies and friends of Data Science
○ “Anecdotal” vs “general”
○ “Co-occurrence” vs “correlation”
○ “Correlation” vs “causality”

16. An Example
Owen was good at math and became a data scientist

17. An Example
Owen was good at math and became a data scientist
(1000 people) were good at math and became data
scientists

18. An Example
Good@Math Became Data Scientist
Yes No %Became DS
Yes 1,000 99,000 1%
No 10,000 90,000 10%
%Good@Math 9% 52%
Owen was good at math and became a data scientist
(1000 people) were good at math and became data
scientists

19. An Example
Good@Math Became Data Scientist
Yes No %Became DS
Yes 1,000 9,000 10%
No 1,000 99,000 1%
%Good@Math 50% 8.3%
Owen was good at math and became a data scientist
(1000 people) were good at math and became data
scientists

20. An Example
● We found something!
○ People who are good at math has 10 times
better chance to become Data Scientist!
● Is this good enough? Depending on your use case:
○ Probably good enough to make up some math
interview questions for DS
○ But not necessarily good enough to say “let’s
teach kids more math so that more of them
become data scientists”

21. That’s All
● Questions?
● Office hour at 1:30pm