May 05, 2026

[PRELIMINARY] Course Catalog 2026-2027

Data Science Major

The major consists of a minimum of 12 full courses (or the equivalent). Students pursuing the natural sciences concentration who complete BIOL 210 and BIOL 211 will take an additional half course.

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

This major may be completed under either the Bachelor of Arts or the Bachelor of Science.

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

Note(s) on Requirements

Students who complete BIOL 210 and BIOL 211 will take an additional half course.
Courses counting toward the project-based learning and natural sciences applications requirements must be taken at Oberlin.

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

Note(s) on Requirements

Courses counting toward the project-based learning and social sciences applications requirements must be taken at Oberlin.

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

Note(s) on Requirements

Courses counting toward the project-based learning and statistical theory and applications applications requirements must be taken at Oberlin.

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

The data science program will accept up to the equivalent of two full courses of transfer credit to count toward the data science major requirements. Two types of approval are needed. (1) Approval from the Office of the Registrar that the transfer course meets Oberlin’s transfer-of-credit eligibility standards and (2) approval from the department/program chair that the course(s) may count to the major. Current students taking post-matriculation credit must secure this approval before registering for course(s). Students who enter Oberlin as transfer students will have their pre-matriculation credit evaluated by the Office of the Registrar for elective credit, and they can later seek approval from the department/program for applying coursework to major requirements.

Course of Study

Students can begin exploring the data science major through any of the three categories of methods courses: data science (DATA 101 ), statistics (DATA 113 ), or computation (CSCI 150 ). Students can also explore possible application areas of data science at any time, although they will form stronger connections of how data science is a useful tool for both understanding the world around us and evidence-based decision making after taking courses from each of the three categories of methods and at least one advanced methods course. Most students will complete the project-based course in their senior year, although for some students, a junior experience might be more appropriate.

Honors in Data Science

Honors in Data Science is not offered at this time.

Detailed Major Requirements