B.S. Degree Requirements 2024-2025

1. All of the following:
   1. Linear Algebra, Multivariable Calculus, and Modern Applications (Math 51, 5 units)
      Can substitute: Math 61CM or Math 61DM
   2. Integral Calculus of Several Variables (Math 52, 5 units)
      Can substitute: Math 62CM or Math 63DM
2. One of the following:
   1. Applied Matrix Theory (Math 104, 4 units)
   2. Linear Algebra and Matrix Theory (Math 113, 4 units)
3. One proof-writing course from the list below:
   1. Mathematical Foundations of Computing (CS 103, 5 units)
      Can substitute: CS 154
   2. Proofs and Modern Mathematics (MATH 56, 4 units)
   3. Modern Mathematics: Continuous Methods (MATH 61CM, 5 units)
      Can substitute: Math 62CM or Math 63CM
   4. Modern Mathematics: Discrete Methods (MATH 61DM, 5 units)
      Can substitute: Math 62DM Math 63DM
   5. Linear Algebra and Matrix Theory (MATH 113, 4 units)
   6. Functions of a Real Variable (MATH 115, 4 units)
      Can substitute: Math 171
   7. Stochastic Processes (MATH 136/STATS 219, 4 units)

The proof-writing course may double count with other requirements in the major. Otherwise, substitutions are not permitted in the Math core.

1. Programming Methodology (CS 106A, 5 units)
   Students who have experience with programming but not Python can take CS 193Q instead.
2. Programming Abstractions (CS 106B, 5 units)

Students with prior experience in Python, who successfully complete DATASCI 112 without taking CS 106A, can petition to have the 106A requirement waived.

1. One optimization course from the list below:
   1. Introduction to Optimization (MS&E 111/211, 4 units)
   2. Introduction to Optimization: Data Science (MS&E 111DS/211DS, 3-4 units)
   3. Introduction to Optimization (Accelerated) (MS&E 111X/211X, 4 units)
   4. Convex Optimization I (EE 364A, 3 units)
2. One stochastic modeling course from the list below:
   1. Stochastic Modeling (MS&E 221, 3 units)
   2. Introduction to Stochastic Processes I (STATS 217, 3 units)

Substitutions in the Modeling and Optimization core are not permitted.

1. One probability sequence from the following:
   1. Introduction to Probability Theory (STATS 117*, 3 units) AND Probability Theory for Statistical Inference (STATS 118, 3 units)
      Students could have alternatively filled this requirement by taking STATS 116 before it was discontinued.
   2. Introduction to Probability Theory (Math 151, 4 units)
2. Introduction to Theoretical Statistics (STATS 200, 4 units)
3. One course on regression and modeling from the list below:
   1. Introduction to Applied Statistics (STATS 191, 3 units)
   2. Regression Models and Analysis of Variance (STATS 203, 3 units)
      Can substitute: STATS 305A

*Students may substitute CS 109, EE 178, or MS&E 120 in place of STATS 117. These are the only approved substitutions in the Statistics Core.

1. One course that explores the intersection between data, technology, and ethics:
   1. The Politics of Algorithms (COMM 154 / SOC 154, 5 units)
   2. Human-Centered AI (CS 139, 3 units)
   3. Computers, Ethics, and Public Policy (CS 181, 4 units)
   4. Ethics, Public Policy, and Technological Change (CS 182, 5 units)*
   5. Data Privacy and Ethics (MS&E 234, 3 units)
   6. Social and Ethical Issues in the Neurosciences (NBIO 101, 3 units)

Students who identify another course that explores the intersection between data, technology, and ethics may petition to have that course count toward this requirement, with permission from the Program Director.

1. One of the following options fulfills the WIM and Capstone requirements:
   1. WIM: Data Narratives (DATASCI 120, 3 units)
      Capstone: The Data Science Experience (DATASCI 190, 1 unit)
   2. WIM and Capstone: Data Science Practicum I (DATASCI 192A, 2 units) and Data Science Practicum II (DATASCI 192B, 3 units)
   3. WIM: Data Narratives (DATASCI 120, 3 units) 
      Capstone: A Data Science in Context class, numbered DATASCI 194(letter) (DATASCI 194_, 3 units)
   4. WIM: Data Narratives (DATASCI 120, 3 units)
      Capstone: Pre-Approved Independent Research Project
   5. WIM: Data Narratives (DATASCI 120, 3 units) 
      Capstone: Honors Program
   6. WIM: Data Narratives (DATASCI 120, 3 units) 
      Capstone: Notation in Science Communication

Learn more about the Data Science B.S. Capstone Options

1. One course at the intersection of computation and biology: 
   1. Introduction to Scientific Computing (CME 108, 3 units)
   2. Mathematical Modeling of Biological Systems (CME 209, 3 units)
   3. Design and Analysis of Algorithms (CS 161, 5 units)
   4. Foundations of Computational Human Genomics (CS 173A, 3-4 units)
   5. Modeling Biomedical Systems (CS 270, 3 units)
   6. Representations and Algorithms for Computational Molecular Biology (CS 274, 3-4 units)
   7. Computational Biology: Structure and Organization of Biomolecules and Cells (CS 279, 3 units)  
2. One of the following sequences: 
   1. Biology Sequence:
      1. Genetics (BIO 82, 4 units)
      2. Biochemistry & Molecular Biology (BIO 83, 4 units)
      3. Physiology (BIO 84, 4 units)
      4. Cell Biology (BIO 86, 4 units) 
   2. Human Biology Sequence:
      1. Genetics, Evolution, and Ecology (HUMBIO 2A, 5 units)
      2. Cell and Developmental Biology (HUMBIO 3A, 5 units)
      3. The Human Organism (HUMBIO 4A, 5 units)

1. One mathematical preparation course from the list below:
   1. Differential Equations with Linear Algebra, Fourier Methods, and Modern Applications (MATH 53, 5 units)
      Can substitute: MATH 63CM or MATH 131P
   2. Introduction to Linear Dynamical Systems (EE 263, 3 units)
2. One Introduction to Neuroscience course from the list below:
   1. Introduction to Cognitive Neuroscience (PSYCH 50, 4 units)
   2. Cognitive Neuroscience (PSYCH 202, 3 units)
   3. Molecular and Cellular Neurobiology (BIO 154, 4 units)
3. One Introduction to Cognitive Psychology course from the list below:
   1. Introduction to Perception (PSYCH 30, 4 units)
   2. Minds and Machines (PSYCH 35 / SYMSYS 1, 4 units)
   3. Introduction to Learning and Memory (PSYCH 45, 3 units)
4. One Machine Learning for Neuroscience course from the list below:
   1. Machine Learning Methods for Neural Data Analysis (STATS 220, 3 units)
   2. Machine Learning for Neuroimaging (BIODS 227, 3-4 units)
   3. Large-Scale Neural Network Modeling for Neuroscience (PSYCH 249 / CS 375, 3 units)
5. Two Advanced Neuroscience Electives from the list below, for a total of 6-8 units:
   1. Another course from the “Machine Learning for Neuroscience” category above
   2. Data Science and Neuroscience (DATASCI 194N, previously numbered DATASCI 125, 3 units)
      Note: This course cannot be double-counted for this requirement and the capstone requirement. 
   3. Educational Neuroscience (EDUC 266, 3 units)
   4. Measuring Learning in the Brain (EDUC 464, 3 units)
   5. Psychophysics and Music Cognition (MUSIC 251, 3-5 units)
   6. Basics in Auditory and Music Neuroscience (MUSIC 451A, 3-5 units)
   7. Judgment and Decision-Making (PSYCH 154, 3 units)
   8. Brain Decoding (PSYCH 164, 3 units)
   9. Advanced Seminar on Memory (PSYCH 169, 3 units)
   10. Neural Network Models of Cognition (PSYCH 209, 4 units)
   11. Theoretical Neuroscience (PSYCH 242, 3 units)
   12. Cognitive Neuroscience: Vision (PSYCH 263, 3 units)
   13. Philosophy of Neuroscience (PHIL 167D, 4 units)
   14. Probabilistic models of cognition: Reasoning and Learning (CS 428A / PSYCH 220A, 3 units)
   15. Probabilistic Models of Cognition: Language (CS 428B / PSYCH 220B, 3 units)

1. One of the following:
   1. Differential Equations with Linear Algebra, Fourier Methods, and Modern Applications (Math 53, 5 units)
      Can substitute: Math 63CM or 62DM
   2. A MATH class numbered 100 or above
2. Two additional computation courses from the list below:
   1. Introduction to Scientific Computing (CME 108, 3 units)
   2. Computer Organization and Systems (CS 107, 5 units)
   3. Data Management and Data Systems (CS 145, 4 units)
   4. Introduction to the Theory of Computation (CS 154, 4 units)
   5. Design and Analysis of Algorithms (CS 161, 5 units)
3. One additional statistical learning or causality course from the list below:
   1. Statistical Learning and Data Science (STATS 202/202F/202V, 3 units)
      Can substitute: STATS 315A
   2. Introduction to Causal Inference (STATS 209, 3 units)
      Can substitute: STATS 361
   3. Introduction to Statistical Learning (STATS 216, 3 units)
   4. Design of Experiments (STATS 263, 3 units)
4. Two additional technical electives from the list below for at least 6 units.
   1. See the Technical Electives section

No substitutions other than those noted here are permitted. 
 

1. Economic Analysis I (ECON 50, 5 units)
2. One Time Series course from the list below:
   1. Introduction to Time Series Analysis (STATS 207, 3 units)
   2. Introduction to Stochastic Processes II (STATS 218, 3 units)
   3. Machine Learning for Sequence Modeling (STATS 232 / CS 229B, 3-4 units)
   4. Financial Statistics (MS&E 349, 3 units)
3. Two finance electives from the list below, for a total of 6-9 units:
   1. Cryptocurrencies and blockchain technologies (CS 251, 3 units)
   2. Foundations of Finance (ECON 135, 3 units)
   3. Financial Markets (ECON 141, 5 units)
   4. Debt Markets (FINANCE 320, 3 units)
   5. Financial Markets I (FINANCE 620, 3 units)
   6. Topics in Financial Math: Market microstructure and trading algorithms (MATH 237A, 3 units)
   7. Mathematical Finance (MATH 238, 3 units)
   8. Introduction to Finance and Investment (MS&E 145, 4 units)
      Can substitute: MS&E 245A or MS&E 245B
   9. Corporate Financial Management (MS&E 146, 4 units)
   10. Financial Risk Analytics (MS&E 246, 3 units)
   11. Blockchain and Crypto Currencies (MS&E 248, 3 units)
   12. Algorithms for Decentralized Finance (MS&E 339, 3 units)
   13. Financial Statistics (MS&E 349, 3 units)
4. One additional technical elective (3-5 units)
   1. See Technical Electives section