Data Science for Artistic and Cultural Analysis
Explore data's role in understanding culture, history, society, and art
Data Science for Artistic and Cultural Analysis 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.
Students pursuing a B.A. in Data Science for Artistic and Cultural Analysis will learn to integrate a comprehensive knowledge of Data Science methods and theories alongside core critical thinking and creative skills from humanistic study and artistic practice.
They will learn how to apply quantitative and statistical methods to the study of cultural and aesthetic objects; discover how data can change our understanding of history; practice the integration of computation into the creative process; and think critically and philosophically about the role of data in society, culture, and the arts.
Students will develop a broad skillset in computational and statistical methods that they can apply across a variety of domains, including philosophy, history, literature, art, and cultural studies. The B.A. in Data Science for Artistic and Cultural Analysis will prepare students to work across of a range of industries in which a deep knowledge of data science, creative approaches to fundamental problems, and critical thinking skills are prized.
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
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 203 - Regression Models and Analysis of Variance
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)
Thinking and Making with Data
Complete:
- DATASCI 156 - Thinking and Making with Data
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
Moral Philosophy
Complete:
- ETHICSOC 20 - Introduction to Moral Philosophy
Additional Ethics Course
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
- 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 Director.
Pathways (8 courses)
Upon declaring the major, students will select one of the following four pathways as their primary concentration.
Analysis
Applying methods of data science to the study of cultural or artistic objects.
Archive
Understanding new data driven methods for storing and accessing the cultural record.
Creation
Making and designing artistic objects or visualizations using data.
Critique
Applying humanistic methods to critique computational or quantitative objects or processes.
Data Science in Practice: WIM and Capstone
To satisfy the WIM and Capstone requirements, complete the following:
DATASCI 120 - Data Narratives (WIM)
and
- DATASCI 193 - Applied Artistic and Cultural Analysis (Capstone)
The capstone course (DATASCI 193) gives students the opportunity to integrate and apply the skills and knowledge that they have acquired in their studies. Through a collaboration with the Center for Spatial and Textual Analysis (CESTA), they will work closely with a faculty or industry PI to address a real problem in artistic and cultural analysis, or to develop data-driven artistic projects. Students will identify a set of concrete tasks associated with their chosen project to complete over the quarter, and produce regular reports that combine progress updates and critical reflection on their process. Outcomes may include, for example, a written analysis, an interactive visualization, a conference presentation, a digital collection, or a creative object made using data.
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.
Ready to join our community?
"I am so lucky that I ended up being at Stanford when the new Data Science [for Artistic and Cultural Analysis] program was created, as it was exactly what I had been trying to do all along! It's a wonderful program and I'm so happy to be their first major and [one of their first] graduate[s].
For me, the Data Science major has been the perfect way to combine my interests in logical reasoning, data analysis and mathematics with my passion for history and literature. I've found such wonderful communities for interdisciplinary research here at Stanford through my classes."
