Course Description

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.

Sharad Goel ()
Lauren Gomez (TA) ()
Jongbin Jung (TA) ()
Chiraag Sumanth (TA) ()
Ron Tidhar (Grader) ()
Class: Tuesdays & Thursdays @ 1:30 PM - 2:50 PM in 200-002
Discussion Section: Thursdays @ 3:00 PM - 3:50 PM in 380-380c

No discussion section during the first week of class.

We use Piazza to manage course questions and discussion. Please sign up here.

Office Hours
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.

[ Optional ] Textbooks
All of Statistics by Larry Wasserman (available online)
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
Computing Environment
We primarily use R (R Studio is the recommended interface), including the plotting library ggplot2, and the data manipulation library dplyr.
8 homework assignments (80%)
Final project (20%)