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 take note of the following two course policies.
On-time attendance at lectures is required, and attendance at discussion sections is encouraged. Our aim is to create a collaborative and supportive learning environment. One of the best ways to learn the course material is to engage with the lectures by asking questions. If you need to miss a class (e.g,. for an illness or sporting event) or will be late, please email Sharad prior to the lecture. In-class attendance checks will be periodically carried out throughout the quarter.
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 we institute this policy. (We're happy to make exceptions in special circumstances.)
Jongbin Jung (TA) (email)
Jerry Lin (TA) (email)
Camelia Simoiu (TA) (email)
Discussion Section: Thursdays @ 3:00 PM - 4:20 PM in STLC 114
Mondays @ 5 PM - 7 PM in Shriram 366 (Camelia)
Tuesdays @ 3 PM - 5 PM in Huang 251 (Sharad)
Wednesdays @ 10 AM - 12 PM in GESB 131 (Jongbin)
Thursdays @ 5 PM - 7 PM in Spilker 317 (Jerry)
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%)
Final exam (20%)