Teaching Experiential Data Analytics Using an Election Simulation

Abstract

Abstract Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of analytics has famously grown to include major league sports and even U.S. election campaigns. Universities have raced to include data analytics in their curricula as the need for data scientists has become more acute. Unfortunately, many data science courses in college curricula suffer from various deficiencies: some lack a hands-on component, others are insufficiently experiential, and yet others leave students with too few transferable skills. This article describes an experiential approach to teaching data analytics at the college level that uses an election simulation, MISSimulation.com—to communicate key data science concepts in a competitive setting. Many universities actively use the simulation, combined with analytic tools such as Tableau and Excel, to implement team competition. We explore key techniques used and knowledge learned during the typical teaching of a data analytics course using the simulation. We end with pedagogical review of data analytics skills transferred during the course and student feedback.