Models for Second Courses in Data Science: Insights From Five Different Institutions

Authors

• Ben Baumer, Smith CollegeFollow
• Gillian D. Beltz-Mohrmann, Smith College
• Brianna C. Heggeseth, Macalester College
• Nicholas J. Horton, Amherst CollegeFollow
• Leslie Myint, Macalester College
• Michael A. Posner, Villanova University
• Paul Roback, St. Olaf College

Author ORCID Identifier

Benjamin Baumer: 0000-0002-3279-0516

Gillian Beltz-Mohrmann: 0000-0002-4392-8920

Brianna Heggeseth: 0000-0002-3946-1715

Nicholas J Horton: 0000-0003-3332-4311

Leslie Myint: 0000-0003-2478-0331

Michael Posner: 0009-0002-9990-559X

Paul Roback: 0009-0002-9262-1797

Document Type

Article

Publication Date

Fall 2026

Publication Title

Harvard Data Science Review

Volume

8

Issue

4

Abstract

As data science continues to mature as an academic discipline, curricular standardization remains elusive. While a number of groups have defined program-level learning outcomes for data science, there is no consensus surrounding course-level learning outcomes. We present five different models for a second course in data science, each of which comes with its own learning goals, coverage of topics, points of emphasis, prerequisites, and role in our respective (statistics and) data science curricula. We use the data science topic, subtopic, and competency/concept framework created by the MASDER project to analyze the content of our courses, and present areas of similarity and difference across them. While we cannot claim that our list of courses is comprehensive, we hope that the material we present will provide other institutions and instructors with vetted ideas for second courses in data science. We also hope that our analysis can move the field towards greater coordination (and perhaps even standardization) within the data science curriculum.

Recommended Citation

Baumer, Ben; Beltz-Mohrmann, Gillian D.; Heggeseth, Brianna C.; Horton, Nicholas J.; Myint, Leslie; Posner, Michael A.; and Roback, Paul, "Models for Second Courses in Data Science: Insights From Five Different Institutions" (2026). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/91

Rights

©2026 Benjamin S. Baumer, Gillian D. Beltz-Mohrmann, Brianna C. Heggeseth, Nicholas J. Horton, Leslie Myint, Michael A. Posner, and Paul Roback. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.

Version

Author's Accepted Manuscript

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.