Degree Requirements 2022-2023: Data Science B.S.

1. Mathematical Foundations of Computing (CS 103, 5 units)
2. Programming Methodology (CS 106A, 5 units); if a student opts out of CS 106A, they will need to take a higher-level CS course.
3. Programming Abstractions (CS 106B or X, 5 units)
4. Two of the following: (inclusion of CS 161 is recommended)
   1. Introduction to Scientific Computing (CME 108, 3 units)
   2. Computer Organization and Systems (CS 107, 5 units)
   3. Introduction to Automata and Complexity Theory (CS 154, 4 units)
   4. Design and Analysis of Algorithms (CS 161, 5 units)

Option A: Optimization Core Set of Two

1. One of the following:
   1. Introduction to Optimization (Accelerated) (MS&E 211X, 3-4 units)
   2. Convex Optimization I (EE 364A, 3 units)
2. One of the following:
   1. Stochastic Modeling (MS&E 221, 3 units)
   2. Introduction to Stochastic Processes I (STATS 217, 3 units)

Option B: Optimization Core Set of Three

1. Choose three of the following:
   1. Introduction to Optimization (MS&E 111 or 111X, 3-4 units)
   2. Introduction to Stochastic Modeling (MS&E 121, 4 units)
   3. Introduction to Optimization Theory (MS&E 213, 3 units)
   4. Stochastic Modeling (MS&E 221, 3 units)
   5. Introduction to Stochastic Control with Applications (MS&E 251, 3 units) 

1. One of the following:
   1. Theory of Probability (STATS 116, 5 units)
   2. Theory of Probability I (STATS 117, 3 units) AND Theory of Probability II (STATS 118, 3 units)
   3. Introduction to Probability Theory (Math 151, 4 units)
2. Introduction to Statistical Inference (STATS 200, 4 units)
3. One of the following
   1. Introduction to Applied Statistics (STATS 191, 3 units)
   2. Introduction to Regression Models and Analysis of Variance (STATS 203, 3 units)
4. One of the following:
   1. Data Mining and Analysis (STATS 202, 3 units)
   2. Introduction to Statistical Learning (STATS 216, 3 units)
   3. Modern Applied Statistics: Learning (STATS 315A, 3 units)
   4. Topics in Causal Inference (STATS 209A/MS&E 327, 3 units)
   5. Design of Experiments (STATS 263)

1. One of the following:
   1. The Politics of Algorithms (COMM154 / COMM 254 / CSRE 154T / SOC 154, 5 units)
   2. Introduction to AI Safety (CS 120, 3 units)
   3. Human-Centered AI (CS 139, 3 units)
   4. Ethics, Public Policy, and Technological Change (CS 182, 5 units)*
   5. Introduction to Moral Philosophy (ETHICSOC 20, 4-5 units)
   6. Data Privacy and Ethics (MS&E 234, 3 units)
   7. Justice (POLISCI 103, 5 units)

*CS 182W cannot be double-counted for the ethics requirement and the WIM requirement. 

1. One of the following:
   1. Data Narratives (MCS 120 / DataSci 120)
   2. Data Science Practicum I (DATASCI 192A, 2 units) and Data Science Practicum II (DATASCI 192B, 3 units)
      DATASCI 192A/B can double-count to fulfill the WIM and Capstone requirements. 
   3. Equity and Governance for Artificial Intelligence (CS 121, 4 units)
   4. Applied Group Theory (MATH 109, 4 units)
   5. Applied Number Theory and Field Theory (MATH 110, 4 units)
   6. Groups and Rings (MATH 120, 4 units)
   7. Fundamental Concepts of Analysis (MATH 171, 4 units)
   8. Computers, Ethics and Public Policy (CS 181W, 4 units)
   9. Ethics, Public Policy, and Technological Change (CS 182W, 5 units)
      CS 182W cannot double-count for the ethics requirement and the WIM requirement.
   10. Modern Statistics for Modern Biology (STATS 155, 3 units)

Data Science electives (9 units)

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