An increasing amount of data is now generated in a variety of disciplines,
ranging from finance and economics, to the natural and social sciences.
Making use of this information requires both statistical tools and an
understanding of how the substantive scientific questions should drive
the analysis. In this hands-on course, we learn to explore and analyze
real-world datasets. We cover techniques for summarizing and describing data,
methods for statistical inference, and principles for effectively communicating results.
Prerequisites: MS&E 120 or equivalent, and CS 106A or equivalent
We encourage you to attend our crash course on R on Saturday, January 14 and Sunday, January 15. Please sign up here. You can view the R course materials here.
Lauren Gomez (TA) (email)
Jongbin Jung (TA) (email)
Chiraag Sumanth (TA) (email)
Ron Tidhar (Grader) (email)
Discussion Section: Thursdays @ 3:00 PM - 3:50 PM in 380-380c
No discussion section during the first week of class.
Mondays @ 3 PM - 5 PM in Shriram 054 (Chiraag)
Mondays @ 7 PM - 9 PM in Huang 305 (Lauren)
Tuesdays @ 3 PM - 5 PM in Huang 356 (Sharad)
Tuesdays @ 5 PM - 7 PM in Shriram 366 (Lauren)
Wednesdays @ 10 AM - 12 PM in Huang 203 (Jongbing)
Wednesdays @ 5 PM - 7 PM in Y2E2 335 (Chiraag)
Thursdays @ 4:30 PM - 6:30 PM in Y2E2 105 (Lauren)
There are no office hours during the first week of class. Feel free to schedule an appointment if you would like to meet.
R for Data Science by Garrett Grolemund and Hadley Wickham
Statistics by David Freedman, Robert Pisani, and Roger Purves
Natural Experiments in the Social Sciences by Thad Dunning
Final project (20%)