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

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.

Sharad Goel ()
Camelia Simoiu (TA) ()
Chiraag Sumanth (TA) ()
Karly Jerman (TA) ()
Class: Tuesdays & Thursdays @ 1:30 PM - 2:50 PM in 200-002
Discussion Section: Thursdays @ 3:00 PM - 4:20 PM in 200-205

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

Office Hours
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.

[ 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 (RStudio is the recommended interface), including the suite of tidyverse packages.
8 homework assignments (75%)
5 in-class quizzes (5%)
Final project (20%)