About this Course

Course Description

Due to the growth in electronic sources such as cell phones, Facebook, Twitter, and other online platforms, researches now have enormous amounts of data about every aspect of our lives – from what we buy, to where we go, to who we know, to what we believe. This has led to a revolution in social science, as we are able to measure human behavior with precision largely thought impossible just a decade ago. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides a hands-on, non-technical introduction to the methods and ideas of Computational Social Science. We will discuss how new online data sources and the methods that are being used to analyze them can shed new light on old social science questions, and also ask brand new questions. We will also explore some of the ethical and privacy challenges of living in a world where big data and algorithmic decision-making have become more commonplace. Each week, students will have the opportunity to try their hand at analyzing big data from sources ranging from online dating profiles to New York City taxicabs to #metoo Tweets and other sources. Note that this course is a 4-credit course that includes a weekly, 2-hour lab component in addition to lecture and discussion.

Contributors

This course was created over the course of the 2017-18 and 2018-19 academic years as part of the University of Michigan Computational Social Science Initiative. This course is the product of a four-way collaboration among:

• Elizabeth Bruch
• Jeff Lockhart
• Ed Platt
• Daniel Romero

Materials

Materials for this course, including the syllabus, labs, and technical instructions are all open source on this website and GitHub. We hope that others will use them in their own teaching or learning and ask only that you credit us for materials that you borrow or adapt.