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

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.