Data Science, Bachelor of Science College of Letters & Science
The Data Science program has a capped admission process. Information about the requirements for continuing students to change majors into Data Science can be found at Department of Statistics.
The Major Program
Data Science combines computational, mathematical and statistical reasoning to draw conclusions based on data. Data science techniques and methods can be applied to problems from virtually any discipline; for example, in agricultural and environmental sciences, biological sciences, engineering, medical sciences and social sciences.
The Program
Data Science majors receive a Bachelor of Science degree. The program requires both theoretical and applied course work to underscore the strong interdependence of technical foundations in computer science, engineering, mathematics and statistics, and their applications to any field of inquiry relying on quantitative data analysis. The B.S. degree program has one track, the Foundations track.
B.S. in Data Science-Foundations Track emphasizes the underlying computer science, engineering, mathematics and statistics methodology and theory, and is especially recommended as preparation for graduate study in data science or related fields.
Career Opportunities
Inferential and computational techniques are used in many fields, including the agricultural and environmental sciences, biological sciences, social sciences, and health sciences, business, and engineering. The wide applicability of data science is reflected in the strong demand for graduates with data science training in both the public and private sectors. Employment opportunities include careers in data & policy analysis in government & industry, tech industry, insurance & healthcare industry, engineering, public health, biological & pharmaceutical research, law, and education. Students with an undergraduate degree in data science may enter advanced studies in data science, computer science, applied mathematics, statistics, economics, finance, psychology, medicine, business management & analytics, and other professional school programs.
Major Advisor
For a current list of faculty and staff advisors in the Department of Statistics, see Undergraduate Advising.
Foundations Track
| Code | Title | Units |
|---|---|---|
| Preparatory Subject Matter | ||
| Computer Science Engineering | ||
| ECS 017 | Data, Logic, & Computing | 4 |
| ECS 032A | Introduction to Programming | 4 |
| ECS 032B | Introduction to Data Structures | 4 |
| Mathematics | ||
| MAT 021A | Calculus | 4 |
| MAT 021B | Calculus | 4 |
| MAT 021C | Calculus | 4 |
| MAT 022A | Linear Algebra | 3 |
| Statistics | ||
| STA 035A | Statistical Data Science I | 4 |
| STA 035B | Statistical Data Science II | 4 |
| STA 035C | Statistical Data Science III | 4 |
| Preparatory Subject Matter Subtotal | 39 | |
| Depth Subject Matter | ||
| Computer Science Engineering | 12 | |
| Databases for Non-Majors | ||
| Algorithms for Data Science | ||
ECS 119 | (Pending Approval) | |
| Probability & Statistics | 12 | |
| Applied Statistical Methods: Regression Analysis | ||
| Fundamentals of Statistical Data Science | ||
| Probability | ||
or STA 131A | Introduction to Probability Theory | |
| Machine Learning | ||
| Choose one: | 4 | |
| Applied Machine Learning for Non-Majors (Pending Approval) | ||
| Mathematics for Data Analytics & Decision Making | ||
| Statistical Learning I | ||
| Mathematics | 8 | |
| Optimization | ||
| Applied Linear Algebra | ||
or ECS 130 | Scientific Computation | |
| Science & Technology Studies | 4 | |
| Data & Society | ||
| Upper Division Electives | ||
| Three elective courses in a related discipline. A list of pre-approved electives can be found on the Department of Statistics website. | 12 | |
| Depth Subject Matter Subtotal | 52 | |
| Total Units | 91 | |