DTSC - Data Science Major (B.A.)

Courses for the major must be completed both in Computer Science and Mathematics. Elective courses must be approved by the program advisor. The capstone project statement of intent must be submitted by the assigned registration date in the Spring semester of the Junior year according to this template major guidelines. The capstone project must be approved by the program advisor. Students can apply up to 3 courses from another major.

Foundational requirement (2 courses):

• CPSC 115 Introduction to Computer Science

• MATH 212 Probability

Core data science requirement (4 courses):

• MATH 229 Applied Linear Algebra

• MATH 312 Statistical Learning

• CPSC 215 Data Structures and Algorithms

• CPSC 360 Deep Learning

Elective requirement (2 courses):

One elective must be from Computer Science and one from Mathematics; electives must be approved by the program advisor.

Suggested Math Electives:

• MATH 209 Stochastic Processes

• MATH 234 Differential Equations

• MATH 237 Mathematics of Finance

• MATH 309 Numerical Analysis

• MATH 316 Dynamical Systems

• MATH 334 Partial Differential Equations

Suggested CS Electives:

• CPSC 310 Software Design

• CPSC 340 Principles of Software Engineering

• CPSC 352 Artificial Intelligence

• CPSC 372 Database Fundamentals

• CPSC 395 Sensitive Information in a Connected World

• CPSC 415 Special Topics: Data Visualization

• CPSC 415 Special Topics: AI Integration

• CPSC 415 Special Topics: Data Analytics

Capstone Project (2 semesters, 2 credits):

All students complete a year-long capstone in the senior year, consisting of a 1-credit seminar in the Fall and a 1-credit project in the Spring. The Fall seminar, co-taught by faculty from Computer Science and Mathematics, establishes a shared interdisciplinary foundation and provides necessary preparation for the Spring project. This sequence is designed to offer students a cohesive senior experience that integrates the computational and mathematical aspects of the major. It supports a common identity for the program, strengthens its interdisciplinary core, and helps ensure that students can build meaningfully on their prior coursework rather than following divergent paths (CPSC / MATH). The capstone will integrate skills and knowledge from across the curriculum to address an original research question or practical data-driven challenge. Projects may include the development of software tools, design of data analysis pipelines, or applications in interdisciplinary domains. Affiliated faculty from both Computer Science and Mathematics will advise each project.