This document discusses data science education. It provides details about several data science courses including one taught by Jeff Leek that had 7,000 students registered. It also discusses the components of a MOOC and compares the standard content of a graduate program in statistics to the less standard content of a data science specialization. Finally, it outlines two discussion points: how to add data analysis to a curriculum without subtracting something else, and what opportunities exist at a "medium scale" for data science education.

1. Intro
slide
Data Science Education at

2. Teaching
Blogging
Research

3. Teaching
Blogging
Research
jhudatascience.org

4. Teaching
Blogging
Research
simplystatistics.org
@simplystats

5. Teaching
Blogging
Research
jtleek.com

6.
Teaching hands on
data analysis

7. “Here is a
monograph on
GLMMs.”

9. +
-
Github
Data cleaning
Machine learning
Reproducibility
Data Analysis
Special cases
Some asymptotics
Some mathematical details

10. Your project is to quantify the level of
collaborative and methodological work
performed by the tenure-track faculty at
the following departments:
• Johns Hopkins Biostatistics
• University of Washington Biostatistics
• University of Minnesota Biostatistics
• Stanford Statistics
• Iowa State Statistics
• University of Chicago Statistics

12. 1st
Discussion Point:
To add data analysis you
have to subtract something
else.

13. How we get into MOOCs

14. every letter is negotiable, from Wikipedia citing Mathieu Plord

16. from: jtleek@gmail.com
Roger let me know you gave him a
ballpark figure for the number of
students registered for his course
"Computing for Data Analysis”. Could
you give me an idea of how many have
registered for my course "Data
Analysis?”

17. from: pangwei@coursera.org
Hi Jeff,
7,000 students! It's pretty awesome.
(You'll be able to check this out
yourself next week, once the class sites
are up.)

18. from: rdpeng@gmail.com
You are f**ed.
-roger

19. A MOOC is
Videos

20. A MOOC is
Quizzes

21. A MOOC is
Forums

22. A MOOC is
Peer grading

23. 6503
Data analysis
completers
6761*
M.S. in
Statistics
*
h.p://community.amstat.org/blogs/steve-­‐pierson/2014/02/09/largest-­‐graduate-­‐programs-­‐in-­‐staAsAcs

24.
Data Science
Specialization

25. 9 classes
1 month long
Every month

26. Less standard content
Standard content
Github
Data cleaning
Interactive graphics
Presentations
Capstone
Probability
Inference
Regression and GLMs
EDA

27. The Team

28. Cumulative Enrollment

29. 2nd
Discussion Point:
What opportunities are
there at “medium scale”?

33. JHU DSS Collaborations
Instant hands on data science
Lectures
Course materials
Automated Grading
Instructor Dashboards
Virtual TAs

34. jtleek.com/talks
1. Data analysis è ? in
curriculum
2. Opportunities at
medium scale