Data Science
Apply computational and quantitative thinking to innovative lines of inquiry
Data Science B.S.
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.S. in Data Science is the successor to the major in Mathematical and Computational Science (MCS). The goals of our program remain ambitious: we aim to provide a broad and deep understanding of the foundations of the discipline, training nimble and versatile data scientists. Increasing data size and availability, enhanced computational power, and progress in algorithms and software make this an ever exciting area.
Students pursuing the B.S. in Data Science will acquire a core mathematical knowledge, upon which they will build competences in computation, optimal decision making, probabilistic modeling, and statistical inference. By learning the theory behind data science, the students develop the capacity to stay up-to-date in a field that is evolving rapidly. They learn to design new methodologies and quickly get up to speed with new developments.
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
- advanced alternative: MATH 113 - Linear Algebra and Matrix Theory
Probability
Complete:
- STATS 117 - Introduction to Probability Theory
Note: We strongly recommend that all students take STATS 117. However, students may substitute CS 109, EE 178, or MS&E 120 in place of STATS 117. Alternatively, students who prefer a more accelerated pace may substitute MATH 151 or STATS 116X in place of both STATS 117 and STATS 118.
Statistics
Complete one of the following:
- STATS 191 - Introduction to Applied Statistics
- 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
Extended Core (6 courses)
Multivariable Integration
Complete one of the following:
- MATH 52 - Integral Calculus of Several Variables
- advanced alternative: MATH 62CM - Modern Mathematics: Continuous Methods
Proof-Writing
Complete one of the following:
- CS 103 - Mathematical Foundations of Computing
- CS 154 - Introduction to the Theory of Computation
- MATH 56 - Proofs and Modern Mathematics
- MATH 61CM - Modern Mathematics: Continuous Methods
- MATH 62CM - Modern Mathematics: Continuous Methods
- MATH 63CM - Modern Mathematics: Continuous Methods
- MATH 61DM - Modern Mathematics: Discrete Methods
- MATH 62DM - Modern Mathematics: Discrete Methods
- MATH 63DM - Modern Mathematics: Discrete Methods
- MATH 113 - Linear Algebra and Matrix Theory
- MATH 115 - Functions of a Real Variable
- MATH 171 - Fundamental Concepts of Analysis
- STATS 219 - Stochastic Processes
The proof-writing course may double-count with other requirements in the major.
Theoretical Statistics
Complete both courses:
STATS 118 - Probability Theory for Statistical Inference
and
STATS 200 - Introduction to Theoretical Statistics
or STATS 200Q - Philosophical Foundations of Statistics
We strongly recommend that all students take STATS 118. However, students may substitute MATH 151 or STATS 116X in place of both STATS 117 and STATS 118.
Stochastic Modeling
Complete one of the following:
- MS&E 221 - Stochastic Modeling
- STATS 217 - Introduction to Stochastic Processes I
Ethics
Complete one of the following:
- BIOE 131 - Ethics in Bioengineering
- 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
- NBIO 101 - Social and Ethical Issues in the Neurosciences
- POLISCI 145B - Governing Artificial Intelligence: Law, Policy, and Institutions (NEW)
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.
Subplan (choose one)
Subplans and electives in Data Science provide students the opportunity to develop their interests. Students can explore how inferential and computational thinking can be effective in areas as diverse as finance, biology, and neuroscience, or they can choose to acquire greater depth in our core disciplines.
Each student must select one of the subplans below, which has its own specific requirements.
Biology and Medicine
Computational Neuroscience
Mathematics and Computation
Quantitative Finance
Data Science in Practice: WIM and Capstone
One of the following options fulfills the WIM and Capstone requirements:
- DATASCI 120 - Data Narratives (WIM) and one of the following Capstones:
- DATASCI 190 - The Data Science Experience or
- DATASCI 194_ (any Data Science in Context class, numbered 194_) or
- Independent Research Project (DATASCI 199, requires department pre-approval) or
- Notation in Science Communication
- Data Science Practicum I and II (DATASCI 192A and 192B)
- Honors Capstone Thesis (DATASCI 199W, honors students only)
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 probability requirement, theoretical statistics requirements, 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.
- Double-counting within the major is not allowed, unless explicitly stated otherwise:
- The Proof-Writing course (in Extended Core) may be double-counted with another requirement within the major.
- 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.
- 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 think [Data Science] is one of the best majors offered at Stanford; it is a "liberal arts" major for the computationally-minded. It has served me very well and it was fun to work towards the major because the classes were easy to balance since they were so different and exciting. I really think it is a gem that showcases Stanford's best departments all under one degree. It prepared me well for my doctoral work and my postdoc."
