Teaching bayesian and markov methods in business analytics curricula: An integrated approach

Marina E. Johnson, Ram Misra, Mark Berenson

Research output: Contribution to journalArticlepeer-review

Abstract

In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods—Bayesian analysis and Markov chains—can be combined to enhance student learning using the Analytics Project Life Cycle Management (APLCM) approach and a case study involving qualitative forecasting. The theoretical frameworks for combining Bayesian and Markov methods are developed, and a forecasting solution is implemented in both MS Excel and Python. Based on an assessment of student learning, applying this pedagogical approach helps students better use these disjoint methods and appreciate the value of integrating them. Although this teaching brief is designed and most appropriate for graduate students with previous BA courses, it can also be used in upper-level courses within an undergraduate BA curriculum. Finally, this teaching brief provides the instructors wishing to use this pedagogical approach in their appropriate courses with the necessary resources (i.e., case study, in-class example, and the MS Excel and Python templates).

Original languageEnglish
JournalDecision Sciences Journal of Innovative Education
DOIs
StateAccepted/In press - 2021

Keywords

  • and Qualitative Forecasting
  • Business Analytics Curricula
  • Integrating Bayesian Analysis with Markov chains

Fingerprint

Dive into the research topics of 'Teaching bayesian and markov methods in business analytics curricula: An integrated approach'. Together they form a unique fingerprint.

Cite this