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
Please do not use electronics (laptops, tablets, phones) during lectures. But please bring laptops to the Thursday discussion sections, as there will be in-class coding and analysis. See here and here on why I institute this policy. (I'm happy to make exceptions in special circumstances.)
We encourage you to attend our crash course on R, offered 6-9pm on Monday, January 15 and repeated on Tuesday, January 16 in Thornton 110. Please sign up here. You can view the R course materials here.
Camelia Simoiu (TA) (email)
Chiraag Sumanth (TA) (email)
Karly Jerman (TA) (email)
Discussion Section: Thursdays @ 3:00 PM - 4:20 PM in 200-205
Mondays @ 5:45 PM - 7:45 PM in Y2E2 101 (Camelia)
Tuesdays @ 3 PM - 5 PM in Huang 251 (Sharad)
Wednesdays @ 5 PM - 7 PM in Hewlett 101 (Chiraag)
Thursdays @ 5 PM - 7 PM in Herrin T195 (Karly)
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
5 in-class quizzes (5%)
Final project (20%)