Computational Social Science
Department: Applied Mathematics, Columbia University
Instructors: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii
Course number: E4990
Time: Fridays, 1:00-3:40pm
Location: 337 Seeley W. Mudd
With an increasing amount of data on every aspect of our daily activity—from what we buy, to where we travel, to whom we know—we are able to measure human behavior with precision largely thought impossible just a decade ago, creating an unprecedented opportunity to address longstanding questions in the social sciences. Leveraging this information, however, requires both scalable computational tools, and understanding how the substantive scientific questions should drive the data analysis. Lying at the intersection of applied mathematics, statistics, computer science, and the social sciences, the emerging field of computational social science fills this role, using large-scale demographic, behavioral and network data to investigate human activity and relationships. This class will introduce key tools and techniques of computational social science, consistently framing these methods in terms of motivating research questions. In addition to core mathematical concepts, the course will also address practical issues in working with real datasets, including familiarity with APIs, Unix tools, and statistical libraries to collect, manipulate, and analyze data.
Linear algebra (APMA E3101 or equivalent), Probability & Statistics (SIEO W4150 or equivalent). Previous exposure to a high-level programming language such as Python, MATLAB (e.g. COMS W1005), Ruby, Perl, R or similar is recommended.