Subplans: Data Science B.S.
These subplan requirements apply to students who declare between September 1, 2026 and August 31, 2027. Looking for a different requirement year?
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, and follow its specific set of requirements.
Biology and Medicine
Computational Neuroscience
Mathematics and Computation
Quantitative Finance
Biology and Medicine (4-5 courses)
Course at the intersection of computation and biology:
Complete one of the following courses:
- CME 108 - Introduction to Scientific Computing with Machine Learning Applications
- CME 209 - Mathematical Modeling of Biological Systems
- CS 161 - Design and Analysis of Algorithms
- CS 173A - Foundations of Computational Human Genomics
- CS 270 - Modeling Biomedical Systems
- CS 274 - Representations and Algorithms for Computational Molecular Biology
- CS 279 - Computational Biology: Structure and Organization of Biomolecules and Cells
Biology Course Set:
Complete all courses in one of the following sequences:
Biology Sequence:
- BIO 82 - Genetics
- BIO 83 - Biochemistry & Molecular Biology
- BIO 84 - Physiology
- BIO 86 - Cell Biology
OR
Human Biology Sequence:
- HUMBIO 2A - Genetics, Molecular Biology and Evolution
- HUMBIO 3A - Cell and Developmental Biology
- HUMBIO 4A - The Human Organism
Computational Neuroscience (6 courses)
Mathematical Preparation
Complete one of the following:
- MATH 53 - Differential Equations with Linear Algebra, Fourier Methods, and Modern Applications
- MATH 63CM - Modern Mathematics: Continuous Methods
- MATH 131P - Partial Differential Equations
- EE 263 - Matrix Methods: Singular Value Decomposition
Introduction to Neuroscience
Complete one of the following:
- BIO 102 - Introduction to Neuroscience
- PSYCH 50 - Introduction to Cognitive Neuroscience
- PSYCH 202 - Cognitive Neuroscience
Introduction to Cognitive Psychology
Complete one of the following:
- PSYCH 30 - Introduction to Perception
- PSYCH 35 - Minds and Machines
- PSYCH 45 - Introduction to Learning and Memory
Machine Learning for Neuroscience
Complete one of the following:
- STATS 220 - Machine Learning Methods for Neural Data Analysis
- BMDS 274 - Machine Learning for Neuroimaging
- PSYCH 249 - Large-Scale Neural Network Modeling for Neuroscience
Advanced Neuroscience Electives
Complete two of the following:
- CS 428A - Probabilistic models of cognition: Reasoning and Learning
- CS 428B - Probabilistic Models of Cognition: Language
- DATASCI 194N - Data Science for Neuroscience
- EDUC 464 - Measuring Learning in the Brain
- EDUC 486 - Educational Neuroscience
- MUSIC 251 - Psychophysics and Music Cognition
- MUSIC 451A - Basics in Auditory and Music Neuroscience
- PHIL 167D - Philosophy of Neuroscience
- PSYCH 154 - Judgment and Decision-Making
- PSYCH 164 - Brain decoding
- PSYCH 169 - Advanced Seminar on Memory
- PSYCH 209 - Neural Network Models of Cognition
- PSYCH 236 - Mind Reading with Movies and Neuroimaging
- PSYCH 242 - A modern explainable AI approach to Theoretical Neuroscience
- PSYCH 263 - Neuroscience of Visual Intelligence
An additional course from the “Machine Learning for Neuroscience” category above can also be taken as an advanced neuroscience elective.
Mathematics and Computation (6 courses)
Additional Math Course
Complete one of the following:
- MATH 53 - Differential Equations with Linear Algebra, Fourier Methods, and Modern Applications
- MATH 63CM - Modern Mathematics: Continuous Methods
- MATH 62DM - Modern Mathematics: Discrete Methods
- Any MATH course numbered 100 and above
Additional Computation Courses
Complete two of the following:
- CME 108 - Introduction to Scientific Computing with Machine Learning Applications
- CS 107 - Computer Organization and Systems
- CS 145 - Introduction to Big Data Systems
- CS 154 - Introduction to the Theory of Computation
- CS 161 - Design and Analysis of Algorithms
Additional Statistical Learning or Causality Course
Complete one of the following:
- STATS 202 - Statistical Learning and Data Science
- STATS 202F - Statistical Learning and Data Science [Flipped]
- STATS 202V - Statistical Learning and Data Science [Virtual]
- STATS 315A - Modern Applied Statistics: Learning
- STATS 209 - Introduction to Causal Inference
- STATS 361 - Causal Inference
- STATS 263 - Design of Experiments
Technical Electives
Complete two additional technical electives for a total of at least 6 units.
- See the Technical Electives section for approved and recommended courses.
Each course must be at least 3 units. Students may use a maximum of one independent study/research course (3 units) as a technical elective if the research is related to data science and approved by the program director.
Quantitative Finance (5 courses)
Economics Preparation
Complete one of the following:
- ECON 50 - Economic Analysis I
- ECON 50Q - Economic Analysis I (Quantitative)
Time Series
Complete one of the following:
- STATS 207 - Time Series Analysis
- STATS 218 - Introduction to Stochastic Processes II
- STATS 232 - Machine Learning for Sequence Modeling
- MS&E 349 - Financial Statistics
Finance Electives
Complete two of the following:
- CS 251 - Cryptocurrencies and blockchain technologies
- ECON 135 - Foundations of Finance
- ECON 141 - Investments in Financial Markets
- ECON 165 - International Finance
- FINANCE 320 - Debt Markets
- FINANCE 620 - Financial Markets I
- MATH 238 - Mathematical Finance
- MS&E 145 - Introduction to Finance and Investment
- MS&E 245A - Investment Science
- MS&E 245B - Advanced Investment Science
- MS&E 146 - Corporate Financial Management
- MS&E 246 - Financial Risk Analytics
- MS&E 339 - Algorithms for Decentralized Finance
- MS&E 349 - Financial Statistics
Technical Elective:
Complete one additional technical elective:
- See the Technical Electives page for approved and recommended courses.
Each course must be at least 3 units. Students may use a maximum of one independent study/research course (3 units) as a technical elective if the research is related to data science and approved by the program director.