Data science programs in U.S. higher education: An exploratory content analysis of program description,curriculum structure,and course focus

Abstract

In this study, an exploratory content analysis of 30 randomly selected Data Science (DS) programs from eight disciplines revealed significant gaps in current DS education in the United States. The analysis centers on linguistic patterns of program descriptions, curriculum requirements, and DS course focus as pertaining to key skills and domain knowledge. The results show that a range of unique terms was used in individual program descriptions, with common terms being shared across disciplines. DS programs required varying numbers of credit hours, including practicum and capstone. Most DS courses covered the basic level of analytical skills, but upper-level skills were inadequately addressed. Programs in eight disciplines delivered information skills through their core courses, and four addressed communication skills. Six disciplines covered visualization skills through their core courses, yet just three in elective courses. The course offering on mathematics/statistics was rather weak in iSchools. While core courses in iSchools provided communication and visualization skills, their electives courses did not address such skills. These findings have implications for improving DS education in iSchools and across other disciplines.