Program Overview
Data Science
Explore evidence-based problem-solving as a data scientist
Detail of "Dialectic Triangulation: A Visual Philosophy," Agnes Denes. Allen Memorial Art Museum, Oberlin College.
Photo credit: ©Agnes Denes
Discover every aspect of the world through a scientific lens
More than 20 years of data on energy use collected via Oberlin’s Environmental Dashboard program
Pathways in Computer Science
Oberlin Computer Science provides a background in fundamentals of data science, logic, theory and programming, but we go beyond just coding. With an Oberlin CS degree, you will learn how the computer revolution is changing society and our lives, and how you can help shape its impact.
STEM in Color
Oberlin’s STRONG program (Science and Technology Research Opportunities for a New Generation) represents the college’s ongoing commitment to increasing the diversity of the STEM workforce.
Featured Courses
CSCI 144
Introduction to Data Science
The growth and use of data is increasingly vital for many disciplines, from the natural sciences to the social sciences, and from business to the humanities. This course introduces students to data science and informatics that study how to collect, manage, process, analyze, and visualize data from a computational perspective. Topics include computational thinking, understanding different types of data, database techniques, and a variety of data analysis approaches. Focus will be on gaining a breadth of knowledge and the exploration of applications of data science and informatics.
- Taught by
- Adam Eck
STAT 205
Statistics and Modeling
An introduction to statistics and, in particular, linear models for students with some background in statistics and a good background in mathematics. Topics covered include exploratory data analysis, probability, sampling, estimation, statistical inference, multiple regression, one-factor and multi-factor analysis of variance, and analysis of categorical data via logistic regression. Statistical software is used heavily.
- Taught by
- Jeffrey (Jeff) Witmer
ECON 255
Introduction to Econometrics
This is an introduction to the application of statistical methods to the estimation of economic models and the testing of economic hypotheses using non-experimental data. The central statistical tool is multivariate regression analysis. Topics covered include: the Gauss-Markov theorem, testing hypotheses, and correcting for heteroskedasticity, autocorrelation and simultaneous equation bias. In the weekly computer lab sessions econometric software is used to analyze real-world data.
- Taught by
- Maggie Brehm
PSYC 311
Advanced Methods in Diversity Science
This course introduces students to theoretical and methodological considerations for conducting research with diverse populations (e.g., ethnic and racial minorities). Content areas may include (but are not limited to) cultural socialization, interracial relationships, and mental health. Students will gain further experience with various stages of the research process (e.g., conceptualization, literature review, study design, data analysis, report-writing).
- Taught by
- Christine S. Wu
Student Profiles
The Frontier of Economic Science
Mark Walker, a junior majoring in Russian studies, economics, and mathematics, has recently been awarded the prestigious Beinecke Scholarship, which he plans to put toward a graduate program in economics.
Studying the Effects of Covid-19
Alexa Myles ’21 and Devin Williams ’21 t joined a team of students who assisted Professor of Psychology and Environmental Studies Cindy Frantz in a project that would lead to the study of COVID-19 on psychological factors.
From Oberlin to Google
Four Oberlin computer science majors have accepted jobs from the tech giant while still in their senior year. Sage Vouse ’19 is one of them.
