The document provides comprehensive guidance on building a data science portfolio to enhance job prospects through networking and showcasing skills. It covers the importance of a public portfolio, elements to include, and strategies for using social media effectively, along with advice on resumes and interviews. Key takeaways emphasize using independent projects, effective communication of results, and tailoring the portfolio to fit career goals.

1. 2.3.2021
Building a Data
Science Portfolio
That Rocks!

2. About Me
● Current: Developer Relations at Anyscale
● Previous: Data Scientist at Scripps Research
Current: Writing about
Distributed Computing
Previous: Understanding
Fitbit Data
Have taught Python/Data
Science/Analytics at Stanford
Continuing Studies, UCSD,
LinkedIn Learning, YouTube, etc.

3. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

4. What is a Portfolio?
● Public evidence of your data
analytics/science skills.
● You have Facebook, Pinterest,
Instagram, Snapchat so why don’t
you have public evidence of your
skills?
● Consider making a professional
online presence.
Job Life
There is some overlap between the
different types of social media sites

5. Goals for Building a Portfolio
● Show people what you can do
instead of telling them you can do
something
● Help you get interviews
● Networking
● Help you with interviews (take home
challenges)
Job Life
There is some overlap between the
different types of social media sites

6. Portfolios can consist of things like
● Resume (how people find your portfolio)
● GitHub
● Kaggle
● Tableau Public
● Blogging
● LinkedIn
● Twitter
● Various Forum Accounts
● Stack Overflow
● Open Source Contributions
● Podcast appearances
● Giving Talks
Job Life
There is some overlap between the
different types of social media sites

7. Importance of a Portfolio
The more public work you do, the
higher the chance of a freak
accident like that: of someone noticing
your work and pointing you towards a
job opportunity, or of someone who’s
interviewing you having heard of work
you’ve done.
Tweet offering an interview
for answering a question well on Stack Overflow
David Robinson Principal Data Scientist at
Heap

8. ● How do you get experience if you
need experience to get your first
job?
● If you don’t have any experience as
a data
analyst/scientist/programmer/anythi
ng, then you should do independent
projects.
Portfolio to get around an Experience Requirement
Get around ridiculous requirements

9. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

10. My Portfolio (notice its not all just my profiles)
GitHub Tableau Public Medium/Blogging
Stack Overflow Online Courses Personal Website

11. My Portfolio
YouTube LinkedIn Twitter
Google Scholar Kaggle (Working on it!) Podcast Appearances
This lecture

12. My Portfolio
Organizing/Attending Events This Talk Resume

13. Employers/Hiring Managers can find you online
● LinkedIn -> Opportunity
● GitHub-> LinkedIn/Resume
● Tableau Public -> LinkedIn/Resume
● Blog -> LinkedIn/Resume
● Stack Overflow -> Google Search
● YouTube -> LinkedIn/Resume
● Kaggle -> LinkedIn/Resume
● Everything else -> LinkedIn/Resume
Job Life
There is some overlap between the
different types of social media sites

14. Employers/Hiring Managers can find you online
● You don’t need to do everything.
● An online presence can lead to more
opportunities.
Job Life
There is some overlap between the
different types of social media sites

15. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in
your Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

16. Identifying what to put in your Portfolio
Microsoft Data Scientist Job Posting
● Design a portfolio to fit with your
career goals
● Look at job postings at companies
and industries you want to work at.
● Will give you some idea of what to
learn and incorporate.

17. Identifying what to put in your Portfolio
Maybe understanding and using these
concepts in a project can help
● Glassdoor interview questions can
also give you an idea of what to
include.
● Note with big companies, interviews
vary drastically as there are many
different teams, but still useful to
look up.

18. Identifying what to put in your Portfolio
Similar things as before
● Glassdoor interview questions can
also give you an idea of what to
include.
● Note with big companies, interviews
can vary drastically as there are
many different teams, but still useful
to look up.

19. Identifying what to put in your Portfolio
Projects are a usual question
● Data Science job postings and
interviews vary drastically, but a lot
of them want ask for projects.

20. Identifying what to put in your Portfolio
Projects are a usual question
● Data Science job postings and
interviews vary drastically, but a lot
of them want ask for projects.

21. Identifying what to put in your Portfolio
● If you don’t know have any
clue which companies you
want to work for, you might
want to showcase your
skills through blogging,
code on GitHub, or by
taking part in a Kaggle
Competition
Traffic Cruising Data Science for Social Good Project
(https://github.com/uwescience/TrafficCruising-DSSG2017)

22. Types of Projects to Include in a Portfolio
I love projects where people
show that they are interested
in data in a way that goes
beyond homework
assignments. Any sort of
class final project where you
explore an interesting dataset
and find interesting results…
William Chen, Former
Data Science
Manager at Quora
Traffic Cruising Data Science for Social Good Project
(https://github.com/uwescience/TrafficCruising-DSSG2017)

23. Sometimes you need to gather,
organize, and clean your own
data.
Types of Projects to Include in a Portfolio
Webscraping can sometimes be an option

24. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

25. Portfolio to help networking
● A lot of people approach networking/job hunting as “it’s not what you know,
it’s who you know”
● Networking is about establishing real connections with a person or
company.
● Real connections are about authentic two-way relationships.
● This means that your network should consist of people that you can help, not
just people who you think can help you.

26. Portfolio to help networking
● If you have helped someone out, they might think better of you and be
happier to see you succeed and potentially help you when you need help.
● Small example: If you post a blog on LinkedIn, people in your network might
share it to a broader audience.
● Medium example: If you brought value to your data analytics/science
community, they might be more receptive to referral requests.
● Large example: It’s possible someone in your network might reach out to you
about jobs to see if you are interested, instead of the other way around.

27. Portfolio to help networking
● Note that even if you have a referral,
you still need to get and pass and
interview (which portfolios can help
with). You can learn how to ask for a referral here.

28. Normal ways to build your network (portfolio helps with this too)
● Attend conferences
● Join data related meetup groups
● Participate in college or bootcamp alumni groups,
● Connect with people through LinkedIn and Twitter.

29. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

30. Take Home Challenges
● Interview processes often have case
studies
● Companies often send you sample
data analytics/science problems to
work on
● Idea: Give challenge that resembles
the work you would be doing on the
job.
Image Source

31. Take Home Challenges
● Instructions might be ambiguous
(intentionally). Day to day work can be
ambiguous as well.
● This means you have to make some
assumptions with the data.
● There might be a use case for a
machine learning model or analysis,
but won’t specify what you should do.
● These are similar to portfolio projects. Not saying this specific job will ask
you to do a take home challenge, but
it can happen in a lot of interview
processes.

32. Take Home Challenges
● Instructions might be ambiguous
(intentionally). Day to day work can be
ambiguous as well.
● This means you have to make some
assumptions with the data.
● There might be a use case for a
machine learning model or analysis,
but won’t specify what you should do.
Project Take Home
Challenge
Choose your own
data
You often have data
given to you
You need to figure
out what to do with
the data
You need to figure
out what to do with
the data
Might have
incomplete or dirty
data
Might have
incomplete or dirty
data
You can
communicate your
results through a
blog or some other
way.
You might be asked
to give a
presentation or
writeup your results.

33. Take Home Challenges
● If they ask for a discussion of your
results, know that communication of
your results is important.
● You might be asked a question like
“how good are your results”
Project Take Home
Challenge
Choose your own
data
You often have data
given to you
You need to figure
out what to do with
the data
You need to figure
out what to do with
the data
Might have
incomplete or dirty
data
Might have
incomplete or dirty
data
You can
communicate your
results through a
blog or some other
way.
You might be asked
to give a
presentation or
writeup your results.

34. Take Home Challenges
● Having a portfolio can make these
challenges easier.
● Often with a portfolio project, you have
to take data which may or may not be
clean and try to solve some problem.
● They both usually require you to
communicate how your analysis
works.
Project Take Home
Challenge
Choose your own
data
You often have data
given to you
You need to figure
out what to do with
the data
You need to figure
out what to do with
the data
Might have
incomplete or dirty
data
Might have
incomplete or dirty
data
You can
communicate your
results through a
blog or some other
way.
You might be asked
to give a
presentation or
writeup your results.

35. Project Take Home
Challenge
Choose your own
data
You often have data
given to you
You need to figure
out what to do with
the data
You need to figure
out what to do with
the data
Might have
incomplete or dirty
data
Might have
incomplete or dirty
data
You can
communicate your
results through a
blog or some other
way.
You might be asked
to give a
presentation or
writeup your results.
Take Home Challenges
● Major difference is for a case study
you might be asked to give a
presentation. If you can figure out the
kind of people you are presenting to,
you can adjust it accordingly.

36. Project Take Home
Challenge
Choose your own
data
You often have data
given to you
You need to figure
out what to do with
the data
You need to figure
out what to do with
the data
Might have
incomplete or dirty
data
Might have
incomplete or dirty
data
You can
communicate your
results through a
blog or some other
way.
You might be asked
to give a
presentation or
writeup your results.
Take Home Challenges
● I recommend you spend time
researching the industry and job you
are interested in and trying to get
familiar with the problems they are
trying to solve before you start a take
home challenge.

37. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

38. Resume Advice
List your technical skills in a skills section
in your resume (get past an applicant
tracking system)
This could not only help your chances for
a job you applied for, but also help in
recruiting searches for future positions.
Kaggle’s CareerCon (video, resumes reviewed)

39. Resume Advice
Particularly technical skills that the job
description mentions (if you have the
skills)
Kaggle’s CareerCon (video, resumes reviewed)

40. Resume Advice
Don’t list common
projects. They don’t
help distinguish you
from others.
Titanic (A), MNIST (B), and IRIS (C)

41. Resume Advice
Don’t list common
projects. They don’t
help distinguish you
from others.
Titanic (A), MNIST (B), and IRIS (C)
Kaggle’s CareerCon (video, resumes reviewed)

42. Resume Advice
List relevant
coursework that is
applicable
Kaggle’s CareerCon (video, resumes reviewed)

43. Resume Advice
Show results and
include links. If you
participated in a
Kaggle competition,
consider putting a
percentile rank.
Kaggle’s CareerCon (video, resumes reviewed)

44. Resume Advice
In projects sections,
there is always room
for links to writeups
and papers.
Kaggle’s CareerCon (video, resumes reviewed)

45. Resume Advice
In projects sections,
there is always room
for links to writeups
and papers.

46. Resume Advice
Use a resume template
if you don’t like your
current resume
https://github.com/sb2nov/resume

47. 1. What is a Portfolio
2. My Portfolio
3. Identifying what to put in your
Portfolio
4. Portfolio to help networking
5. Portfolio for interviews/take
home challenges
6. Resume Advice
7. Social Media Advice
Building a Data Science Portfolio that Rocks

48. LinkedIn
An important part of LinkedIn is their
search tool and to show up more often
it helps to have relevant keywords in
your profile.

49. LinkedIn
You can put keywords in the “About”
section of your profile as well as the
“Skills”, “Experience”, and
“Recommendations” areas.

50. LinkedIn
Udacity has a guide on making a good
LinkedIn profile. There also have a
good YouTube video on this.

51. LinkedIn
LinkedIn is also great for getting your
content/portfolio out
LinkedIn Post

52. GitHub
A lot of data science is about
communicating results, so be sure to
include a README.md file that clearly
describes what your project is, what it
does, and how to run your code.
README.md

53. Blog
A former Head of Data Science at
Medium said that doubling the time
spent drafting an article corresponds to
an 89% increase in Total Time
Reading.
Image from post by Mike Sall

54. Success is an Iceberg
Don’t worry if your portfolio is perfect,
just show that you have experience,
share how you think, and you’ll be well
on your way to impressing [fill in the
blank]
Source: Instagram @orysya.stus.art

55. Questions?