Computational Social Science

(858) 534-3001
css@ucsd.edu
http://css.ucsd.edu

All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice.

The master of science (MS) in computational social science brings together formal models from the social sciences with the technical training to collect, structure, and utilize large-scale naturalistic data; to integrate such data with formal social science models; and to use the combination to make predictions, develop interventions, and drive policy. This interdisciplinary master’s is offered by the School of Social Sciences and housed administratively in the Department of Psychology. The MS in computational social science is a full-time, self-supporting degree program that most students complete in one year, comprising summer, fall, winter, and spring quarters.

Admission

New students are admitted in the summer of each academic year. To qualify for admission, students must have completed a bachelor’s degree typically in the social sciences or a closely related field, or a bachelor’s in math, computer science, or a related field with a minor or substantial advanced course work in one or more social science domains, and typically must have a minimum 3.0 GPA; GRE scores, a CV or resume, letters of recommendation, and a personal statement will also be considered. Pre-existing training in statistics and/or formal logic is expected and prior experience with one or more technical domains (e.g., programming, statistics, formal logic, calculus, linear algebra) is recommended. International applicants must submit official scores from the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS), and UC San Diego minima will be respected.

Program of Study

The full-time degree program is designed to be completed in one year. In the special summer session, classes are scheduled five days a week, eight hours a day for eight weeks. In the fall, winter, and spring, students take courses that are held during regularly scheduled university class hours. Students are required to complete fifty-five units of course work (fourteen courses), comprising forty-three core units, including a ten-unit capstone project, and twelve elective units. All courses, with the exception of CSS 209 and CSS 296, must be taken for a letter grade.

Core Courses (forty-three units)

CSS 201S. Principles of Computational Social Science (eight units)
CSS 202S. Computational Social Science Technical Bootcamp (eight units)
CSS 204. Statistical Computing and Inference from Data I (six units)
CSS 205. Statistical Computing and Inference from Data II (four units)
CSS 206. Machine Learning for Social Sciences (four units)
CSS 209. Computational Social Science Research Seminar (one unit, must be completed three times)
CSS 296. Research in Computational Social Science (must be taken three times, at two units, four units, four units), constituting the capstone

Elective Courses

Students enroll in three classes (twelve units) of elective course work related to computational social science. Please contact the program for a list of approved elective courses.

Plan

The degree follows Master Plan II: Comprehensive Examination. Under this plan, the student must complete a practical comprehensive examination designed to evaluate the student’s ability to integrate knowledge and understanding as well as utilize associated skills. The exam will be integrated into host courses, and will normally be completed over multiple quarters. More information regarding the comprehensive examination can be found in a separate document provided by the computational social science advising staff in the Department of Psychology.