Master's Program Curriculum

To solve society’s most pressing problems, we need the ability to collect, organize, and make sense of data, to communicate what it tells us and to use it to drive new, informed predictions.  The Master’s program in Computational Social Science combines training in math, programming, and statistics with the core theories of social science disciplines to create new insights. 

Graduates from the program will be well-positioned to bring their knowledge, innovation, and resourcefulness to industry, government, public policy, academia, research, and education sectors. 

All M.S. students will complete 14 courses, beginning in early July and concluding in mid-June.

The full curriculum includes:

• A ten week summer bootcamp that provides an introduction to data-driven causal inference and domains of Computational Social Science.
• A two-course sequence in Statistical Computing and Inference from Data
• A course in Machine Learning for Social Sciences 
• Three terms of the Computational Social Science Research Seminar
• Three elective courses in areas including cognitive models, images, time series analysis, causal inference, geographic information systems (GIS), networks, and textual analysis, giving the student the ability to tailor their training to their areas of interest, or to achieve additional breadth of training
• A three-quarter capstone that involves an internship or mentored project supervised by an affiliated faculty member, either of which culminates in a final work (paper and presentation).