Comparative Analysis of Undergraduate Data Science Degree Programs’ Curricula

The interdisciplinary field of data science, which applies techniques from computer science and statistics to address questions across domains, has enjoyed recent considerable growth and interest. This emergence also extends to undergraduate education, whereby a growing number of institutions now offer degree programs in data science. However, there is considerable variation in what the field actually entails and, by extension, differences in how undergraduate programs prepare students for data-intensive careers. The higher education theories of academic capitalism and isomorphism help elucidate what is driving differences between seminal data science curricular frameworks and offered curricula. We used two seminal frameworks for data science education to evaluate undergraduate data science programs at a subset of four-year institutions in the United States; developing and applying a rubric, we assessed how well each program met the guidelines of each of the frameworks. Most programs scored high in statistics and computer science and low in domain-specific education, ethics, and areas of communication. Moreover, the academic unit administering the degree program significantly influenced the course-load distribution of computer science and statistics/mathematics courses. Our analyses demonstrate undergraduate programs approach data science education as a largely "hard-skills-intensive," technical endeavor as academic capitalism theory predicts. Forthcoming study findings will evaluate whether current data science programs appear to demonstrate reflexive responses to isomorphic processes. Critical curricular omissions potentially create a Promethean workforce prepared to use a variety of computational and statistical tools in socially inappropriate ways.

Education

Curricula

Data science

Ethics

Statistics 

Beginner

Education

Symposium on Data Science and Statistics (SDSS) 2023