With a vast amount of information now collected on our online and offline actions — from what we buy,
to where we travel, to who we interact with — we have an unprecedented opportunity to
study complex social systems. This opportunity, however, comes with scientific, engineering,
and ethical challenges. In this hands-on course, we develop ideas from computer science and
statistics to address problems in sociology, economics, political science, and beyond. We
cover techniques for collecting and parsing data, methods for large-scale machine learning,
and principles for effectively communicating results. To see how these techniques are applied
in practice, we discuss recent research findings in a variety of areas.
Prerequisites: An introductory course in applied statistics, and experience coding in R or Python. The class is currently oversubscribed. Please complete this short course application by Monday, Sep 23, 11:59pm. Decisions will be announced before the first lecture on Tuesday.
There is a $25 course materials fee for running experiments on Mechanical Turk.
Scott Jespersen (TA) (email)
Zhiyuan “Jerry” Lin (TA) (email)
Lab Section: Thursdays @ 4:30 - 5:50 in Thornton 110
Mondays 3 - 5pm in Huang B007 (Jerry)
Tuesdays 4:30 - 6:30pm in Huang 251 (Sharad)
Wednesdays 10am - 12pm in Huang B016 (Scott)
During the first week of school, Scott and Jerry will hold office hours by appointment only.
The first two lab sections (on Sep. 26 and Oct. 3) will run for two hours, from 4:30 - 6:30, and will be crash courses in Python and R. These are optional but highly recommended. These are interactive session, so please bring your computers and have RStudio (including R), Python 3.7, and JupyterLab installed. Instructions for installing JupyterLab (with the R kernel) are here.
A Unix-like setup is required (e.g., Linux, OS X, or Cygwin). We primarily use R (RStudio is recommended) and Python 3.7 (JupyterLab is recommended). We use the Tidyverse suite of packages in R for data manipulation and visualization. We also use Vowpal Wabbit (a fast online learning algorithm), and Amazon Elastic MapReduce (a web service for distributed computing).
Project proposal (10%)
Final project (25%)