The document discusses the essential skills and experiences for developing effective data scientists, emphasizing strong communication, coding practices, and exposure to diverse datasets. It includes advice from various data scientists on the importance of curiosity and interdisciplinary learning. Additionally, it provides actionable to-do's for both students and teachers to enhance data science education.

1. Developing
data scientists
Erin Shellman PhD
Data Science Kick-off
University of Michigan
October 6, 2015
effective

2. MS in Biostatistics ’08
PhD in Bioinformatics ‘12
…and an alum!
I’m Erin and I’m a
data scientist.

3. data scientists
communicate well.
effective

4. 1. Present your work
2. Blog
3. Teach others
Student’s t0-do:

5. Center coursework
on presentation and
communication
Teacher’s t0-do:

6. data scientists write
high-quality code.
effective

7. –Sarah Guido, Data Scientist @ Bitly and UM alum
The single most useful thing I learned
in grad school was how to work with
data in Python.

8. –Trey Causey, Data Scientist @ ChefSteps, @4thdownbot
I wish there had been more emphasis
on real data/messy data/getting my
own data/cleaning data.

9. 1. Develop deep expertise in a
programming language
2. Adopt good coding
practices now
Student’s t0-do:

10. 1. Expose students to a variety
of datasets
2. Focus on applications
Teacher’s t0-do:

11. data scientists are
curious, life-long
learners.
effective

12. –Wendy Grus, Technical Data Analyst @ Inrix and UM alum
LEARN MORE CODING AND
STATISTICS. Meet other people working
on data sets not in your field. Learn
something not in your field. Don't sit in
a box staring at your thesis project.

13. –Amanda Casari, Senior Data Scientist @ Concur
I took Naval Criminal Law & international
relations. It was hard to stay awake. By
comparison, I looked forward to vector
calculus and loved Matlab. Lesson: Don't
be afraid to explore your options while
you are still on the track of figuring out
what comes next.

14. –James Pestrak, Senior Data Scientist @ Nordstrom
Boundaries between disciplines are
becoming less-defined. There are general,
powerful patterns of thinking that apply
to many disciplines. A good way to
become aware of these patterns is to
broaden exposure to disciplines.

15. 1. Pursue academic interests
outside your discipline
2. Go to meet-ups!
Student’s t0-do:

16. 1. Teach with diverse datasets
2. Illustrate parallels between
disciplines
Teacher’s t0-do:

17. Big ups to these
data scientists
effective
Amanda Casari
Trey Causey
Wendy Grus
Sarah Guido
James Pestrak