Data Science, Bachelor of Science College of Letters & Science
The Data Science program admissions process is capped. For information about the requirements for continuing students to change majors into Data Science, see Department of Statistics.
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.
Foundations Track B.S.
The B.S. degree program has one track, the Foundations Track, which 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.
The major requirements below are in addition to meeting University Degree Requirements & College Degree Requirements; unless otherwise noted. The minimum number of units required for the Data Science Bachelor of Science is 92.
Foundations Track
| Code | Title | Units |
|---|---|---|
| Preparatory Subject Matter | ||
| Statistics | 12 | |
| Statistical Data Science I and Statistical Data Science II and Statistical Data Science III | ||
| Mathematics | 16 | |
| Calculus and Calculus and Calculus | ||
| Linear Algebra | ||
or MAT 067 | Modern Linear Algebra | |
or MAT 027A | Linear Algebra with Applications to Biology | |
or BIS 027A | Linear Algebra with Applications to Biology | |
| Computer Science Engineering | 12 | |
| Data, Logic, & Computing (Preferred) | ||
or ECS 020 | Discrete Mathematics For Computer Science | |
| Introduction to Programming (Preferred) | ||
or ECS 036A | Programming & Problem Solving | |
| Introduction to Data Structures (Preferred) | ||
or ECS 036C | Data Structures, Algorithms, & Programming | |
| Preparatory Subject Matter Subtotal | 40 | |
| Depth Subject Matter | ||
| Science & Technology Studies | 4 | |
| Data & Society | ||
| Probability & Statistics | 12 | |
| Applied Statistical Methods: Regression Analysis | ||
| Fundamentals of Statistical Data Science | ||
| Probability | ||
or STA 131A | Introduction to Probability Theory | |
| Computer Science Engineering | 12 | |
| Databases for Non-Majors | ||
| Algorithms for Data Science | ||
| Data Processing Pipelines | ||
| Machine Learning | ||
| Choose one: | 4 | |
| Applied Machine Learning for Non-Majors | ||
| Mathematics for Data Analytics & Decision Making | ||
| Statistical Learning I | ||
| Statistical Learning II | ||
| Mathematics | 8 | |
| Applied Linear Algebra | ||
or ECS 130 | Scientific Computation | |
| Optimization | ||
| Domain Emphasis | ||
| Three elective courses in a related discipline. A list of pre-approved electives can be found at: | 12 | |
| Note: A course used to fulfill a core requirement cannot be used as an elective. | ||
| Depth Subject Matter Subtotal | 52 | |
| Total Units | 92 | |
Major GPA Requirements
- Minimum 2.000 GPA in UC Davis courses used in the major.
- Minimum 2.000 GPA in upper division UC Davis courses used in the major.
Sample Academic Plan
A sample academic plan can be found at B.S. Data Science Academic Plan. This plan can be used as a guide, but students should meet with an advisor on a regular basis to make a customized plan that works best for their unique needs and priorities.