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. This course was previously
listed as MS&E 331.
Prerequisites: An introductory course in applied statistics, and experience coding in R or Python.
There is a $25 course materials fee for running experiments on Mechanical Turk.
Imanol Arrieta Ibarra (TA) (email)
Lab Section: Wednesdays @ 12:30 - 1:20 in Thornton 110
Sharad's Office Hours
Mondays 3 - 5pm in Huang 356
Imanol's Office Hours
Wednesdays 3 - 5pm in Huang 314
During the first week of school, there is no lab section and office hours are by appointment only.
On Sunday, Oct. 2, we will hold optional (but highly recommended) crash courses on R (10am - 12pm) and Python (1 - 3pm) in Thornton 110. This is an interactive session, so please bring your computers, and have R and Python 2.7 already installed.
A Unix-like setup is required (e.g., Linux, OS X, or Cygwin). We primarily use R (R Studio is recommended) and Python 2.7 (Anaconda Python is recommended), including ggplot2 for visualization and dplyr for data manipulation. 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 (30%)
Scribe notes for one lecture (5%)