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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.

The course is built around three modules: prediction and elections, recommendation and business analytics, and sampling and social network analysis. We will be using Python for all programming assignments and projects.

The course is also listed as AC209, STAT121, and E-109.
Instructors:
Hanspeter Pfister, Computer Science
Joe Blitzstein, Statistics

Staff:
Chris Beaumont, Head TF
Johanna Beyer
Colin Bischoff
Nicolas Bonneel
Tommy Chen
Alex D'Amour
Rahul Dave
Lehman Garrison
Giri Gopalan
Brandon Haynes
Ray Jones
Steffen Kirchhoff
Seymour Knowles-Barley
Alexander Lex
Aaron Meisner
Iva Milo
Josh Schroeder
Deqing Sun

Lectures: Tu / Th 2:30-4 pm
Maxwell Dworkin G115

Labs: F 10-11:30 am
Maxwell Dworkin G115

Adobe Connect Live Classroom
Live Video (alternate)
Video Archive
Lecture Slides
iSite Dropbox for Submissions