Master’s Programs – Emphasis in Data Science

The Emphasis in Data Science consists of 33 units to be taken from the following list.

Core Courses (required 18 units)

COMP 526: Computational Methods for Scientists
COMP 536: Computational Modeling for Scientists or MATH 636: Mathematical Modeling
COMP 600: Student Industry Immersion Seminar
COMP 600: Professional Development for Computational Scientist Seminar I
COMP 600: Professional Development for Computational Scientist Seminar II
COMP 605/CS 605: Scientific Computing
COMP 607: Computational Database Fundamentals
COMP 670: Seminar: Problems in Computational Science

Elective Courses (required 9 units selected from below)

COMP 521: Introduction to Computational Science
COMP 581/PHYS 581: Introduction to Quantum Computing
COMP 589: Computational Imaging / CS 559: Computer Vision / EE 657: Digital Image Processing / or CS 553: Neural Networks
COMP 626: Applied Mathematics for Computational Scientists
COMP 681/PHYS 681: Quantum Computing
CS 649: Big Data Tools and Method
CS 652: Foundations of Deep Learning
CS 653: Data Mining
CS 654: Reinforcement Learning
CS 657: Intelligent Systems
CS 663: Algorithms for Big Data
CS 668: Large Language Models
CS 677: Data Quality Assurance
STAT 670A: Advanced Mathematical Statistics
STAT 670B: Advanced Mathematical Statistics
STAT 672: Non Parametric Statistics
STAT 673: Time Series Analyses
STAT 700: Data Analysis Methods
STAT 702: Data Mining Statistical Methods

Research Courses (required 3 units)

Culminating Project (required 3 units)

COMP 798: Special Study
Project: The student must develop a project with two faculty members from different departments. See Thesis/Project Proposal outline here.
Advancement to Candidacy: Approval Thesis/Project proposal by Student Thesis/Project committee. All students must satisfy the general requirements for advancement to candidacy, and the basic requirements for the Master’s degree. please see the Graduate Division’s Graduate Bulletin.