Data Science & Social Systems
Put data to work to tackle society's greatest challenges
Data Science & Social Systems B.A.
These degree requirements apply to students who declared the minor after September 1, 2026.
If you declared a Data Science major prior to this date, please refer to the past requirements page:
Visit Past Program Requirements
If you originally declared prior to September 1, 2026, but are interested in switching to the new requirements, please reach out to the Student Services Officer.
The B.A. degree in Data Science & Social Systems enables students to develop a triple fluency: expertise in statistical and computational methods, domain knowledge across the core social sciences, and a deep and interdisciplinary understanding of an important social problem.
Interdisciplinary thinking is essential for the data-driven analysis of complex social problems, and this program is designed to prepare students with both superb technical training and in-depth knowledge of the social sciences. This is ideal preparation for leadership roles in industry, government, or the nonprofit sector.
Degree Requirements
Data Science Core (8 courses)
Computation
Complete both courses:
CS 106A - Programming Methodology
and
- CS 106B - Programming Abstractions
Note: Most students should take CS 106A. However, students with prior programming experience in a language other than Python may instead take CS 193Q. Note that DATASCI 112 (required for major) uses Python.
For students with prior experience in Python who successfully complete DATASCI 112 without taking CS 106A can have the CS 106A requirement waived.
Math
Complete one of the following:
- MATH 51 - Linear Algebra, Multivariable Calculus, and Modern Applications
- advanced alternatives: MATH 61CM or MATH 61DM
Data
Complete:
- DATASCI 112 - Principles of Data Science
Mathematical Modeling
Complete one of the following:
- MATH 104 - Applied Matrix Theory
- approved substitution (limit 2 throughout core): ENGR108 - Introduction to Matrix Methods
- advanced alternative: MATH 113 - Linear Algebra and Matrix Theory
Probability
Complete:
- STATS 117 - Introduction to Probability Theory
- approved substitutions (limit 2 throughout core): CS 109, MS&E 120, or EE 178
- advanced alternative: STATS 118 - Probability Theory for Statistical Inference
Statistics
Complete one of the following:
- STATS 191 - Introduction to Applied Statistics
- approved substitutions (limit 2 throughout core): MS&E125 or MS&E226
- advanced alternative: STATS 200, STATS 200Q, or STATS 203
Optimization
Complete one of the following:
- MS&E 111 - Introduction to Optimization
- MS&E 111DS - Introduction to Optimization: Data Science
- MS&E 111X - Introduction to Optimization (Accelerated)
- advanced alternative: EE 364A - Convex Optimization I
Pre-Approved Substitutions
We recommend all students take the standard courses shown above. However, to provide flexibility, students in the Data Science & Social Systems or Data Science for Artistic and Cultural Analysis B.A. may use up to two of the following pre-approved substitutions within the Data Science Core without additional approval. Students requesting more than two substitutions must submit the inquiry form and obtain approval from the program director.
- Required course: MATH 104 | Approved substitution: ENGR 108
- Required course: STATS 117 | Approved substitutions: CS 109, MS&E 120, or EE 178
- Required course: STATS 191 | Approved substitutions: MS&E 125 or MS&E 226
Extended Core (4 courses)
Data Science for Social Impact
Complete:
- DATASCI 154 - Data Science for Social Impact
Causal Inference
Complete one of the following:
- DATASCI 161 - Causality, Decision Making and Data Science
- ECON 102C - Advanced Topics in Econometrics
- MS&E 228 - Applied Causal Inference with Machine Learning and AI
- POLISCI 150C - Causal Inference for Social Science
- SOC 258B - Quasi-Experimental Research Design & Analysis
Machine Learning
Complete one of the following:
- CS 124 - From Languages to Information
- CS 129 - Applied Machine Learning
- CS 161 - Design and Analysis of Algorithms
- CS 221 - Artificial Intelligence: Principles and Techniques
- STATS 202 - Statistical Learning and Data Science
Ethics
Complete one of the following:
- COMM 154 - The Politics of Algorithms
- CS 120 - Introduction to AI Safety
- CS 139 - Human-Centered AI
- CS 181 - Computers, Ethics, and Public Policy
- CS 182 - Ethics, Public Policy, and Technological Change
- ETHICSOC 20 - Introduction to Moral Philosophy
- POLISCI 103 - Justice
- POLISCI 145B - Governing Artificial Intelligence: Law, Policy, and Institutions
Students who identify another course that explores the intersection between data, technology, and ethics may submit the Data Science Requirement Inquiry Form to obtain approval from the program.
Pathway (7 courses)
The pathway is an opportunity for students to develop specific expertise on a topic they are passionate about and where data science methods and approaches have something compelling to offer. Students may develop their own pathway or select one of ten predefined pathways that focus on major societal challenges and the social systems that we need to understand to make progress.
Data Science in Practice: WIM and Capstone
A.) Most students pursuing the Data Science & Social Systems B.A. will complete the Data Science Practicum (DATASCI 192A and B), which satisfies both the Capstone and WIM requirements:
Work in teams to provide actionable recommendations and practical tools to partners, which may include government agencies, community organizations, companies, or research labs. Integrate material from coursework, gain experience applying data science techniques to complex, real-world problems, and develop your ability to work in teams.
Data Science Practicum Steps & Timeline:
- Optional: Take DATASCI 192D during the fall quarter of your senior year if you want to source your own capstone partner organization. Application is required.
- Take DATASCI 192A during winter quarter of senior year.
- Take DATASCI 192B during spring quarter of senior year. These courses must be taken consecutively, within the same academic year.
- Assemble and submit final project & present at the Data Science Capstone Showcase in May/June.
- This course series counts as both the Capstone and WIM.
B.) In special instances, students can apply to fulfill the capstone requirement through conducting an independent data science research project under the supervision of a professor, in which case they must take DATASCI 120 to satisfy the WIM requirement.
Program Policies
- All courses that fulfill major requirements must be taken for a letter grade.
- Students may be granted a one-time exception to use one course taken for credit (CR) toward the major. This exception does not apply to the WIM and capstone, which must always be taken for a letter grade.
- The program does not have a minimum GPA requirement to graduate other than the university minimum of 2.0.
- The major follows the university policy on double-counting between the data science major and another major or minor, including the list of approved courses here.
- Double-counting within the major is not allowed, unless explicitly stated otherwise.
- The program must approve transfer credit before it can be applied toward Data Science major requirements. First, students should submit the course-to-course equivalency request form. After receiving a decision on that request (whether approved or denied), students should submit the request to apply transfer credit toward the Data Science major. Requests are reviewed on a case-by-case basis by the Program Director. Please see our Transfer Credit page for more information.
Where can Data Science & Social Systems take you?
The program will prepare students for professional careers at the intersection of data and decision-making in the private, public, and non-profit sectors. This major is ideal training for careers as product managers, chief data and innovation officers in city and state government, and in philanthropies and nonprofits where technical skills and social scientific training are in high demand.
Ready to join our community?
"The Data Science & Social Systems BA track has allowed me the space to find a nuanced middle ground between technology, social sciences, and humanities. Through this major, human rights and ethical behavior are embodied in my career as a data scientist as a main priority rather than an afterthought. In a world ripe with technological growth, it is more important than ever that we confront the consequences of it with both criticism and empathy."
