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Computational Social Science Minor


students programming a robot

The interdisciplinary minor in Computational Social Science (CSS) at UC San Diego combines formal causal models from the Social Sciences with statistics, programming, and large-scale data analysis. Students enrolled in the minor will complete 28 total units, of which at least 20 units (5 courses) must be upper-division (including the core course CSS 100). Students may petition to receive credit for courses not listed below.

Learning Objectives

  • Provide a robust curriculum that gives students the skills to understand and apply computational and analytic techniques to theoretical and empirical questions in the social sciences.
  • Understand the ethical constraints associated with computational social science and large-scale human data.
  • Provide extensive statistical training and application of modeling, programming, and data analysis techniques.
  • Combine large data with formal, causal social science models.
  • Expand applied learning opportunities in Computational Social Science to widen the possible career paths for undergraduates.

Examples of Research Topics

Some research topics that could be answered using computational social science methods include:

  • During COVID-19, how can we find the areas that lack testing and need it the most?
  • How do microaggressions relate to consumer behavior?
  • How does gender bias play a role in sports journalism?
  • How can we reduce misinformation in social media during crisis events?

Of course there are many other research questions out there!  The goal of CSS is to give you the tools to help come up with answers to the questions that you are passionate about.

Minimum Requirements

  • A grade-point average (GPA) of at least a 2.0 in your computational social science minor is required for graduation.
  • At least 4 upper-division courses towards the minor must be taken at UC San Diego
  • Students enrolled in the minor will take the following set of courses:
    • 3 core courses in Computational Social Science 
      • These core courses must be taken for a letter grade.
    • At least 4 upper-division electives
      • At least 2 of these electives must be selected from departments outside of the student's major field of study (e.g., a student with a major in Cognitive Science must take at least 2 electives that are not Cognitive Science courses).
        • Note: Joint majors like CBN are considered students from two departments and must take these two courses from outside of both departments.
      • At least 3 of these electives must be taken for a letter grade.

Check out our CSS Information Session Video: 

Course List

Core Courses

Students are required to take the following 3 core courses for a letter grade: 

  • CSS 1. Introductory Programming for Computational Social Science (4)
  • CSS 2. Data and Model Programming for Computational Social Science (4)
  • CSS 100. Advanced Analytic Programming for Computational Social Science (4)

Please see the Courses page for course descriptions.

Elective Courses

4 courses will be chosen from the list below.  Please note the following:

  • No more than 1 Upper-Division CSS elective course may be taken for Pass/No Pass.
  • At least 2 of these electives must be taken from departments outside of the student's major field of study
  • Students are advised to strongly consider MATH 10A-B-C or MATH 20A-B-C and MATH 18 as preparation for upper-division electives.
  • Please visit UC San Diego's general course catalog to view the course descriptions as well as any prerequisites needed for the courses listed below.  
    • Please note that each individual department controls their prerequisites.  Please work with their respective academic advisors for questions on their prerequisites.


  • ANAR 104. Introduction to Geographic Information Systems (GIS) for Anthropologists and Archaeologists (4)
  • ANAR 115.** Coastal geomorphology: an example from Israel, south-Eastern Mediterranean - today and yesterday (4)
  • ANAR 120.** Documenting Climate Change: Past and Present (4)
  • ANAR 121.** Cyber-Archaeology and World Digital Cultural Heritage (4)

Cognitive Science

  • COGS 108. Data Science in Practice (4)
  • COGS 109. Modeling and Data Analysis (4)
  • COGS 118B. Intro to Machine Learning II (4)
  • COGS 118D. Mathematical Statistics for Behavioral Data Analysis (4)
  • COGS 119. Programming for Experimental Research (4)
  • COGS 123. Social Computing (4)
  • COGS 137. Practical Data Science in R (4)
  • COGS 144. Social Cognition: A Developmental and Evolutionary Perspective (4)
  • COGS 150. Large Language Models/Cognitive Science (4)


  • ECON 120A. Econometrics A (4)
  • ECON 120B. Econometrics B (4)
  • ECON 120C. Econometrics C (4)
  • ECON 125. Demographic Analysis and Forecasting (4)
  • ECON 176. Marketing (4)
  • ECON 178. Economic and Business Forecasting (4)


  • LIGN 165. Computational Linguistics (4)
  • LIGN 167. Deep Learning of Natural Language Understanding (4)

Political Science

  • POLI 100F. Social Networks (4)
  • POLI 112A.** Economic Theories of Political Behavior
  • POLI 118.** Game Theory in Political Science
  • POLI 171. Making Policy with Data (4)
  • POLI 172. Advanced Social Data Analytics (4)
  • POLI 175.** Machine Learning for Social Scientists (4)
  • POLI 176. Text as Data. (4)


  • PSYC 193.* Topics in Psychology: Advanced Quantitative Methods (4)
  • PSYC 193.* Topics in Psychology: Data Analysis and Visualization in R (4)
  • PSYC 193:* Topics in Psychology: Perception & Computation (4)


  • SOCI 102. Network Data and Methods (4)
  • SOCI 103M. Computer Applications to Data Management in Sociology (4)
  • SOCI 109. Analysis of Sociological Data (4)

Urban Studies and Planning

  • USP 189.* Special Topics in Urban Planning: Urban Data Science (4)
  • USP 191A.** Intermediate GIS for Urban and Community Planning (4)
  • USP 191B.** Advanced GIS for Urban and Community Planning (4)

*Course topics vary; course will only count towards the minor when the topic matches that listed above. Other topics may be petitioned.

**Please send a VAC message to the Department of Psychology to have this course count for your CSS minor. 

Guidelines for Applying for a CSS Minor

  • To declare or change your minor, use the online minor tool on Tritonlink.
  • You are not required to finish your minor as declared; you are only demonstrating your basic knowledge of minor requirements and will not be held to the classes listed.
  • When completing the computational social science minor application, be sure to list exactly 7 courses (and only 7) or your application will be denied.
  • You can only overlap up to 2 upper-division (UD) courses between your major and CSS minor. Both departments must pre-approve the UD courses you plan to overlap.
  • Processing time for computational social science minor applications is 7-14 business days from the date you submit your application.
    • Applicants will be notified of any errors through the Virtual Advising Center. All students can check their status on TritonLink under the major/minor tool.


Advising Hours & Location: 

  • Students may use the "Ask a Question" feature within the Virtual Advising Center (VAC) to submit questions to CSS Advising. Advisor's responses can be found in the student's "Contact Record" area of their VAC. 

Drop-In Advising is currently being handled in person and via Zoom. 

  • In-person: the CSS Advising Office is located at 1503 Mandler Hall
  • Remote: sign in to your Virtual Advising Center (VAC), select "Meet with Advisor," scroll to "Computational Social Science" and you will be placed in the advisors' queue for a Zoom meeting.

Advising Hours

  • Tuesday (remote)
    • 1:30 p.m.-3:30 p.m.
  • Wednesday (in person): 
    • 1:30 p.m.-3:30 p.m. 

Note: advising hours availability is subject to change.