Analytics Curriculum for Undergraduate and Graduate Students

Abstract

In 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.

Transitioning a traditional introductory information systems course to a data analytics focused course

“…Clayton and Clopton (2019) describe the integration of analytics into the business curriculum of a private university with accreditation from the Association to Advance Collegiate Schools of Business International (AACSB), beginning with changes to their auditing course. Paul and MacDonald (2020) provide comprehensive undergraduate and graduate curriculum recommendations with templates to design new analytics programs or to appraise and alter existing programs. Urbaczewski and Keeling (2019) suggest that Management Information Systems (MIS) departments may eventually transition into analytics departments, given the mutual aim of providing support and improving the quality of decision‐making in organizations; this would entail dramatic curriculum alterations.…”

Introducing business analytics in context: An Excel project to analyze product and sales data for inventory purchase decisions

“…On the other hand, it has long been realized that the "key consumers" of analytics are business users, and undeveloped expertise in data analysis has created a gap within their professional needs (Kohavi et al, 2002). Universities responded to this realization with new BA programs and transitions to (related) information systems (IS) programs (e.g., Ceccucci et al, 2020;Mills et al, 2016;Paul & MacDonald, 2020;Urbaczewski & Keeling, 2019;Wymbs, 2016). Educators also responded at a pedagogical level with noteworthy analyses of the courses and content of BA programs, often examining and framing the skills attributable to analytics professionals (e.g., Cegielski & Jones, 2016;Johnson et al, 2020;Luo, 2016;Radovilsky et al, 2018;Stanton & Stanton, 2020).…”

Randomized Constraint Limit Linear Programming in Risk Management

“…The random values are selected in accordance with any specified probability distribution. That paper and this one present spreadsheet designs for teaching Monte Carlo simulation and risk analysis to graduate students in business analytics (see also Stanton and Stanton [3]; Johnson, Albizri and Jain [4]; Pual and MacDonald [5]), with no requirement for a specialized programming language.…”