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.
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.
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.
For a current list of faculty and staff advisors in the Department of Statistics, see Undergraduate Advising.
|Preparatory Subject Matter
|Computer Science Engineering
|Data, Logic, & Computing
|Introduction to Programming
|Introduction to Data Structures
|Statistical Data Science I
|Statistical Data Science II
|Statistical Data Science III
|Preparatory Subject Matter Subtotal
|Depth Subject Matter
|Computer Science Engineering
|Databases for Non-Majors
|Algorithms for Data Science
|Data Processing Pipelines
|Probability & Statistics
|Applied Statistical Methods: Regression Analysis
|Fundamentals of Statistical Data Science
or STA 131A
|Introduction to Probability Theory
|Applied Machine Learning for Non-Majors (Pending Approval)
|Mathematics for Data Analytics & Decision Making
|Statistical Learning I
|Applied Linear Algebra
or ECS 130
|Science & Technology Studies
|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.
|Depth Subject Matter Subtotal