# B.S. Degree Requirements

##
B.S. Core Courses

##
Math Core (19 units)

- One of the following sequences:
- Multivariable Calculus and Linear Algebra

Linear Algebra, Multivariable Calculus, and Modern Applications (Math 51, 5 units)

Integral Calculus of Several Variables (Math 52, 5 units)

Ordinary Differential Equations with Linear Algebra (Math 53, 5 units) - Modern Mathematics: Continuous Methods (a proof-oriented sequence)

MATH 61CM (5 units)

MATH 62CM (5 units)

MATH 63CM (5 units) - Modern Mathematics: Discrete Methods (a proof-oriented sequence)

MATH 61DM (5 units)

MATH 62DM (5 units)

MATH 63DM (5 units)

- Multivariable Calculus and Linear Algebra
- One of the following:
- Applied Matrix Theory (Math 104, 4 units)
- Linear Algebra and Matrix Theory (Math 113, 4 units)

##
Computation Core (23-25 units)

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

##
Optimization Core (6-10 units)

Option A: Optimization Core Set of Two

- One of the following:
- Introduction to Optimization (Accelerated) (MS&E 211X, 3-4 units)
- Convex Optimization I (EE 364A, 3 units)

- One of the following:
- Stochastic Modeling (MS&E 221, 3 units)
- Introduction to Stochastic Processes I (STATS 217, 3 units)

Option B: Optimization Core Set of Three

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

##
Statistics Core (13-14 units)

- Theory of Probability (STATS 116, 5 units, or Math 151, 4 units)
- Introduction to Statistical Inference (STATS 200, 3 units)
- One of the following
- Introduction to Applied Statistics (STATS 191, 3 units)
- Introduction to Regression Models and Analysis of Variance (STATS 203, 3 units)

- One of the following:
- Data Mining and Analysis (STATS 202, 3 units)
- Introduction to Statistical Learning (STATS 216, 3 units)
- Modern Applied Statistics: Learning (STATS 315A, 3 units)
- Topics in Causal Inference (STATS 209A/MS&E 327, 3 units)
- Design of Experiments (STATS 263)

##
Ethics Core (3-5 units)

- One of the following:
- Justice (POLISCI 103, 5 units)
- Ethics, Public Policy, and Technological Change (CS 182, 5 units)*
- Data Privacy and Ethics (MS&E 234, 3 units)
- Introduction to Moral Philosophy (ETHICSOC 20, 4-5 units)
- The Politics of Algorithms (Comm 154 / COMM 254 / CSRE 154T / SOC 154, 5 units)

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

##
Additional Degree Requirements

##
Writing in the Major (WIM) (3-5 units)

- One of the following:
- Applied Group Theory (MATH 109, 4 units)
- Applied Number Theory and Field Theory (MATH 110, 4 units)
- Groups and Rings (MATH 120, 4 units)
- Fundamental Concepts of Analysis (MATH 171, 4 units)
- Ethics, Public Policy, and Technological Change (CS 182W, 5 units)*
- Statistical Methods in Computational Genetics (STATS 155, 3 units)
- Data Narratives (MCS 120 / DataSci 120)

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

##
Data Science Electives (9 units)

##
Capstone Experience (3 or more units)

Data Science B.S. majors will have the opportunity to integrate the knowledge and skills acquired during their studies and think independently and creatively using the tools of the discipline during a capstone experience, which is an essential part of the undergraduate program. There are a variety of ways to complete the Capstone requirement, both within and outside Data Science. In particular, a number of the capstone experiences are shared across the B.S. and B.A. Data Science majors.

Possible ways of satisfying the capstone requirement will include:

- Project-based classes where students work individually or in small groups to tackle real-life questions using data
- A data science experience course that exposes students to the experiences of data scientists working in different fields and that facilitates a self-reflection on their own activities as data scientists
- Individual research projects (which in some cases might be completed during a study abroad quarter, or might extend summer research carried out in the university)
- Notation in science communication