Minor in Data Science

Data science now informs research and decision-making across every academic discipline: from the social and behavioral sciences to the humanities, engineering, and business. Notre Dame's data science minor provides students with a substantive foundation in programming, statistical modeling, and machine learning, paired with training in responsible data practices and clear communication of analytical results. All coursework is designed for students across majors and assumes no prior programming experience, though one semester of probability and statistics is required.

Core coursework covers Python programming, exploratory data analysis, and the data science pipeline in full: data wrangling, feature engineering, supervised and unsupervised machine learning, model evaluation, and application deployment. Three electives allow students to apply these methods within their own disciplines. Current elective offerings span econometrics, geographic information systems, natural language processing, network analysis, data ethics, and other applied areas across the College and university.

For students interested in the analytic and modeling dimensions of the field, the minor also offers a specialized analytics track.

The data science minor is open to all Notre Dame students and is housed in the College of Arts and Letters' Technology & Digital Studies Hub.

View the Minor Requirements

You will learn how to...

• Write code in the Python programming language using standard libraries for data manipulation, visualization, and modeling
• Wrangle messy, real-world data into tidy, analysis-ready structures
• Conduct exploratory data analysis to surface patterns, anomalies, and research hypotheses
• Build, tune, and evaluate machine learning models for tasks such as regression and classification
• Apply unsupervised techniques clustering and dimensionality reduction to find structure in unlabeled data
• Work with text data using natural language processing and large language models
• Design and deploy interactive, data-driven web applications
• Reason critically about bias, fairness, privacy, and responsible data practice
• Communicate analytical results through narrative, visualization, and interactive dashboards for both technical and non-technical audiences