Analytics Curriculum for Undergraduate and Graduate Students

Abstract

ABSTRACTIn recent years, analytics has gained considerable momentum in both industry as well as academia. This study aims to develop an analytics curriculum for undergraduate and graduate programs by identifying skill‐based gaps between industry and academia and then clustering them based on methodological and semantic similarities among other criteria. Specifically, we compare industry requirements and related skills for analytics jobs to three types of analytics domains, that is, descriptive, predictive, and prescriptive analytics. We also address several sought‐after intangible student characteristics, such as communication skills, intellectual curiosity, and business acumen. We then cluster these skills by similarity based upon methodology, skill type, and semantics to develop courses based on these clusters. Our study thus provides a comprehensive analysis linking specific industry needs to specific coursework that allows any university to create a well‐rounded analytics program at both the undergraduate and graduate level. Finally, realizing the significant difference between undergraduate and graduate students in terms of expectations and maturity, we use personality‐job fit theory to recommend strategies to better promote the field to undergraduate students. Regarding the latter, we recommend best practices that graduate analytics program can employ to be easy to market and sell to prospective students.