Our new minor in Computational Social Science (CSS) is open to all majors at the University of Arizona and will give you an important foundation for pursuing careers and post-graduate studies in technology, policy, business, marketing, and research.
What is Computational Social Science?
Computational Social Science bridges the gap between social science and data science, using computational tools to explore human behavior, social networks, and institutional patterns. Students who minor in CSS will learn programming, gain hands-on experience with techniques such as machine learning and network analysis, and work with real-world social data. The curriculum not only focuses on technical proficiency but also emphasizes critical engagement with the social and ethical impacts of big data and algorithmic decision-making.
This minor is ideal for students from any discipline who are curious about how data is reshaping our world and want to apply quantitative reasoning to pressing social questions. By combining computational training with social analysis, the CSS minor gives students a competitive edge in the job market, preparing them for diverse careers in technology, policy, business, marketing, and research.
Declaring the Minor
To declare your minor as Computational Social Science, you can fill out our declare minor form and schedule an appointment with School of Sociology advisor John McNeil.
Minor Requirements
The minor in Computational Social Science requires a minimum of 18 credits. At least 9 credits must be upper division coursework.
Core Courses (9 credits)
Take both of the following required courses (6 credits):
- SOC 301A: Introduction to Computational Social Science
- SOC 374: Research in the Social Sciences
Take at least one Introductory Social Sciences course (3 credits):
- SOC 101: Introduction to Sociology
- SOC 150B1: Social Issues in America
- SOC 150B2: Gender, Power, and Inequality
- SOC 150C2: The Good Society
- POL 201: American National Government
- POL 202: International Relations
Inferential Statistics Core (3 credits)
Take at least one course from the following list (3 credits):
- SOC 375 Quantitative Reasoning in Sociology (recommended for Sociology majors)
- ISTA 116: Statistical Foundations of the Information Age (recommended for Information Science majors)
- DATA 363: Introduction to Statistical Methods (recommended for Math or Data Science majors)
Methods Core (6 credits)
Take at least two courses from any the following lists (3 credits):
From College of Social & Behavioral Sciences
- SOC 430 Social Networks
- SOC 301B: Advanced Computational Social Science
- POL 397B: The Origins of Data in Politics and Policy
- POL 424: Politics in the Digital Age
- POL 403: Political Networks
- PA 472: Digital Research in Politics and Policy
- GEOG 330: Introduction to Remote Sensing
- GEOG 222: Working with Numeric, Spatial, and Visual Data
- LING 388: Language and Computers
- LING 408: Computational Techniques for Linguists
- LING 438: Computational Linguistics
- LING 439: Statistical Natural Language Processing
From College of Science
- DATA 201: Foundations of Data Science
- DATA 375: Introduction to Statistical Computing
- DATA 467: Introduction to Applied Linear Models
- DATA 474: Introduction to Statistical Machine Learning
From College of Information Science
- ISTA 130: Computational Thinking and Doing
- ISTA 320: Applied Data Visualization
- ISTA 321: Data Mining and Discovery
- ISTA 322: Data Engineering
- ISTA 350: Programming Informatics Applications
- ISTA 421: Introduction to Machine Learning
- ISTA 455: Applied Natural Language Processing