Data Science Major

The major consists of a minimum of 12 full courses (or the equivalent).

Note: Students must earn minimum grades of C- or P for all courses that apply toward the major.

View the catalog page for the data science program.

The data science major prepares students to be evidence-based decision-makers, critical consumers of information, and engaged citizens in a 21^st-century world that is frequently observed and digitized, constantly evolving, and requires multidisciplinary thinking. The major benefits from a liberal arts focus where students understand how to define and perceive challenging real-world problems within disciplinary contexts, and the major contributes to a liberal arts education with the skills necessary for interdisciplinary critical thinking, communication, and collaboratively constructing solutions to those real-world problems. Students study data science methods to better understand our complex, technology-driven world and to make evidence-based decisions. They also apply these methods within one or more disciplines across the liberal arts curriculum.

Summary of Requirements: Data Science Major with a Natural Sciences Concentration

Three Full Data Science Methods Courses
Two Full Statistical Methods Courses
Two Full Computational Methods Courses
One Full Project-Based Learning Course
One Full 300-Level or Above Natural/Social Sciences Advanced Methods Course
One Full Natural/Social Sciences Advanced Methods Elective Course
Two Full Natural Sciences Applications Courses

Summary of Requirements: Data Science Major with a Social Sciences Concentration

Three Full Data Science Methods Courses
Two Full Statistical Methods Courses
Two Full Computational Methods Courses
One Full Project-Based Learning Course
One Full 300-Level or Above Natural/Social Sciences Advanced Methods Course
One Full Natural/Social Sciences Advanced Methods Elective Course
Two Full Social Sciences Applications Courses

Summary of Requirements: Data Science Major with a Statistical Theory and Applications Concentration

Three Full Data Science Methods Courses
Two Full Statistical Methods Courses
Two Full Computational Methods Courses
One Full Project-Based Learning Course
Two Full Statistical Theory and Applications Advanced Methods Courses
DATA 336 - Mathematical Statistics
One Full Statistical Theory and Applications Applications Course

Learning Goals

Upon successful completion of the data science major, students will:

understand and be able to contribute to the entire data science pipeline for data-driven decision-making, including:
- applying appropriate data collection processes for both quantitative and qualitative data;
- managing and preparing large and small data sets for modeling;
- selecting suitable statistical and machine learning models for extracting useful information and patterns from data;
- making informed decisions supported by evidence, then communicating why those decisions were made and their implications to relevant multidisciplinary audiences; and
- revising this pipeline in order to adapt to changes in data and their sources;
contribute to interdisciplinary solutions to real-world problems by applying data science methods within and across other disciplines;
have foundational knowledge in statistics and computational problem solving necessary for working with uncertain, noisy, large, complex data in structured and unstructured formats from a variety of sources;
be able to identify sources of errors and biases that complicate decision-making and how those sources should be addressed across the data science pipeline;
be socially responsible by identifying ethical concerns and the threat of misinformation throughout data-driven decision-making and the real-world contexts within which decisions are made; and
work successfully both independently and collaboratively within multidisciplinary teams, being able to effectively communicate complex ideas and decisions across the vocabularies of different domains.

Transfer of Credit Toward the Major

Honors in Data Science

Detailed Major Requirements