Exploratory Analysis to Identify Concepts, Skills, Knowledge, and Tools to Educate Business Analytics Practitioners

Abstract

Recent technological advancements in data storage and processing have changed how companies conduct their business. An increasing number of firms have started putting their efforts in extracting information from their databases to improve profitability and reduce costs using quantitative approaches. Thus, the job market has been experiencing a rapidly growing demand for business analytics (BA) practitioners, and universities across the globe are increasingly responding to this newly emerged field by offering both undergraduate and graduate level degrees as well as certificate programs. Thus, this research aims to provide a framework for academic institutions to develop a state‐of‐the‐art master's in business analytics (MSBA) curriculum by identifying concepts, skills, knowledge, and tools (CSKT) that industry seeks in BA practitioners. Our data‐driven methodology utilizes peer institution analysis, indeed.com web scraping, and focus group analysis with mid‐ and senior‐level analytics leaders from major companies. Our contribution to the literature and recommendations to institutions developing MSBA programs are offered at the end.

Teaching binary logistic regression modeling in an introductory business analytics course

“…Studies have linked the effective use of predictive analytics to revenue generation, cost minimization, operational efficiency, and enhanced customer service (Dey & Kumar, 2010). Thus, business graduates need knowledge and skills in predictive analytics, especially on how predictive analytics can be used to generate insights that enhance business decision‐making in a variety of contexts (Cegielski & Jones‐Farmer, 2016; Johnson et al., 2020; Waller & Fawcett, 2013).…”

“…The choice of computational software tools is essential to the success of an IBA course (Johnson & Berenson, 2019; Johnson et al., 2020; Rienzo & Chen, 2018). The Guidelines for Assessment and Instructions in Statistics Education College Report identifies the use of computational software as one of six recommendations to enhance the pedagogical delivery of statistics courses (Carver et al., 2016).…”

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

“…Many colleges in the US have developed new programs incorporating Big Data, AI, and advanced computing concepts into the undergraduate and graduate curriculum to meet this demand. Numerous institutions, including universities and private firms, have started offering Massive Open Online Courses and online degrees in the field of data analytics to make Big Data education more accessible (Cegielski & Jones-Farmer, 2016;Rosendale, 2017;Johnson et al, 2020). Primarily, two interdisciplinary areas-data science and business analytics-have recently emerged to address the talent shortage in the job market in the USA.…”

Teaching Bayesian and Markov methods in business analytics curricula: An integrated approach

“…Various studies, including those authored by Chiang et al (2012), Gorman and Klimberg (2014), Wilder and Ozgur (2015), Cegielski and Jones-Farmer (2016), Rienzo and Chen (2018), and Johnson et al (2020), investigate the best practices for designing and improving analytics curricula. These studies notably identify the methods that should be taught in analytics programs and emphasize the skills that analytics students should attain.…”