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This document provides learning-by-doing materials for Analytics software skill development using SAS JMP. It integrates Analytics concepts and techniques with real-world scenarios based on the COVID-19 pandemic to illustrate how real-world data can be transformed into actionable insights to offer decision support for COVID-19 related issues. A holistic treatment of the Analytics process from data acquisition and cleansing to data analysis and interpretation is emphasized using five studies:

  1. Characterize COVID-19 mortality demographic risk factors,
  2. Visualize COVID-19 mortality demographics,
  3. Conduct COVID-19 mortality time series forecasting,
  4. Predict COVID-19 mortality, and
  5. Analyze COVID-19 vaccine acceptance, uptake, and experiences.

Each study is structured with guiding questions to engage students to think critically, relate Analytic concepts to the given situation, and arrive at their own answers/solutions for active knowledge exploration and discovery.

Publication Date



University of Kentucky Libraries


Lexington, KY


About the Author(s)

Anita Lee-Post is an associate professor at the University of Kentucky Gatton College of Business and Economics. Her research interests include sustainability, supply chain management, e-learning, and knowledge management. She has published extensively in journals such as Decision Support Systems, OMEGA, Decision Sciences: Journal of Innovative Education, Computers and Industrial Engineering, International Journal of Production Research, and Information and Management. She is the author of Knowledge-based FMS Scheduling: An Artificial Intelligence Perspective. She serves on the editorial review boards of Production Planning and Control, International Journal of Business Information Systems, International Journal of Data Mining, Modeling and Management, and Journal of Managerial Issues. She is the recipient of the eLearning Innovation Initiative Grant, Fulbright U.S. Scholar Grant, Human Innovative Teaching Award, Teaching and Technology Innovation Program Award, and Kentucky Science and Engineering Foundation’s Research and Development Excellence Program.


© 2021 Anita Lee-Post

This document is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided that the author and publication source are credited, that changes (if any) are clearly indicated, and that the derivative work is distributed under the same license.

A set of teaching notes are available upon request.

Real-World Applications for Analytics Teaching and Learning