Data Science
B.S. Department of Statistics · UC Irvine · 2026-2027 Catalog
The Data Science Major prepares students for a career in data analysis, combining foundational statistical concepts with computational principles from computer science. In the first two years of the program students will take core courses in both the Statistics and Computer Science Departments, providing a strong foundation in the principles of each field. In the 3rd and 4th years of the program, students will take more specialized courses, on topics such as design of algorithms, machine learning, information visualization, and Bayesian statistics. A major component of this degree is the final year capstone project course, a 2-quarter course that teaches students how to apply statistical and computational principles to solve large-scale real-world data analysis problems.
Admission
Freshman Applicants: See the Undergraduate Admissions section.
Transfer Applicants: See the Undergraduate Admissions section.
Requirements
All students must meet the University Requirements.
Data Science Major Requirements
| Course | Title | Units |
|---|---|---|
| Lower-division: | ||
| A. Select one of the following series: | ||
| I&C SCI 31 & I&C SCI 32 & I&C SCI 33 | Introduction to Programmingand Programming with Software Librariesand Intermediate Programming | |
| or | ||
| I&C SCI H32 & I&C SCI 33 | Python Programming and Libraries (Accelerated)and Intermediate Programming | |
| B. Complete: | ||
| I&C SCI 45C | Programming in C/C++ as a Second Language | |
| I&C SCI 46 | Data Structure Implementation and Analysis | |
| I&C SCI 51 | Introductory Computer Organization | |
| SWE 43 | Introduction to Software Engineering | |
| C. Complete: | ||
| MATH 2A | Single-Variable Calculus I | |
| MATH 2B | Single-Variable Calculus II | |
| MATH 2D | Multivariable Calculus I | |
| MATH 3A | Introduction to Linear Algebra | |
| orI&C SCI 6N | Computational Linear Algebra | |
| I&C SCI 6B | Boolean Logic and Discrete Structures | |
| I&C SCI 6D | Discrete Mathematics for Computer Science | |
| STATS 5 | Seminar in Data Science | |
| STATS 6 | Introduction to Data Science | |
| STATS 7 | Basic Statistics | |
| orSTATS 8 | Introduction to Biological Statistics | |
| orSTATS 67 | Introduction to Probability and Statistics for Computer Science | |
| Upper-division: | ||
| A. Data Science core requirements: | ||
| STATS 110 | Statistical Methods for Data Analysis I | |
| STATS 111 | Statistical Methods for Data Analysis II | |
| STATS 112 | Statistical Methods for Data Analysis III | |
| STATS 115 | Introduction to Bayesian Data Analysis | |
| STATS 120A | Introduction to Probability and Statistics I | |
| STATS 120B | Introduction to Probability and Statistics II | |
| STATS 120C | Introduction to Probability and Statistics III | |
| I&C SCI 139W | Critical Writing on Information Technology | |
| COMPSCI 122A | Introduction to Data Management | |
| COMPSCI 161 | Design and Analysis of Algorithms | |
| COMPSCI 178 | Machine Learning and Data-Mining | |
| IN4MATX 143 | Information Visualization | |
| B. Three elective courses from the list below: | ||
| MATH 130B | Probability II | |
| MATH 130C | Stochastic Processes | |
| STATS 140 | Multivariate Statistical Methods | |
| I&C SCI 53 | Principles in System Design | |
| COMPSCI 111 | Digital Image Processing | |
| COMPSCI 115 | Computer Simulation | |
| COMPSCI 116 | Computational Photography and Vision | |
| COMPSCI 121/SWE 141 | Information Retrieval | |
| COMPSCI 122B | Project in Databases and Web Applications | |
| COMPSCI 122C | Principles of Data Management | |
| COMPSCI 122D | Beyond SQL Data Management | |
| COMPSCI 125 | Next Generation Search Systems | |
| COMPSCI 131 | Parallel and Distributed Computing | |
| COMPSCI 134 | Computer and Network Security | |
| COMPSCI 163 | Graph Algorithms | |
| COMPSCI 165 | Project in Algorithms and Data Structures | |
| COMPSCI 169 | Introduction to Optimization | |
| COMPSCI 171 | Introduction to Artificial Intelligence | |
| COMPSCI 172B | Neural Networks and Deep Learning | |
| COMPSCI 177 | Applications of Probability in Computer Science | |
| COMPSCI 179 | Algorithms for Probabilistic and Deterministic Graphical Models | |
| IN4MATX 131 | Human Computer Interaction | |
| IN4MATX 161 | Social Analysis of Computing | |
| C. Data Science capstone team-based project courses: STATS 170A and STATS 170B | ||
Sample Program
Freshman
| Fall | Winter | Spring |
|---|---|---|
| I&C SCI 31 | I&C SCI 32 | I&C SCI 33 |
| MATH 2A | MATH 2B | MATH 2D |
| STATS 6 | STATS 5 | STATS 7, 8, or 67 |
| WRITING 40 | WRITING 50 | WRITING 60 |
Sophomore
| Fall | Winter | Spring |
|---|---|---|
| I&C SCI 45C | I&C SCI 46 | I&C SCI 51 |
| I&C SCI 6B | I&C SCI 6D | STATS 120C |
| STATS 120A | MATH 3A | General Education VI |
| General Education III | STATS 120B |
Junior
| Fall | Winter | Spring |
|---|---|---|
| COMPSCI 122A, 161, or 178 | COMPSCI 122A, 161, or 178 | COMPSCI 122A, 161, or 178 |
| SWE 43 | I&C SCI 139W | IN4MATX 143 |
| STATS 110 | STATS 111 | STATS 112 |
| General Education IV/VIII | General Education III/VII |
Senior
| Fall | Winter | Spring |
|---|---|---|
| Data Science Major Elective | STATS 115 | STATS 170B |
| Data Science Major Elective | STATS 170A | Data Science Major Elective |
| General Education III | General Education IV | General Education IV |
Career Opportunities
A wide variety of careers and graduate programs are open to graduates of the Data Science major. Demand for graduates with skills in both statistics and computer science currently outpaces supply - thus, students with these skills typically find employment quickly, across a wide variety of sectors, including internet companies, finance, engineering, business, medicine, and more. Data Science graduates are well-qualified for job titles such as “data scientist,” “data analyst,” or “statistician,” both in the public and private sectors. Graduate school in area such as Computer Science or Statistics is also a possible career path.