F6-COMP6-4 - Teaching Introductory Data Analytics Course Using Microsoft Access® and Excel®

2. Research-to-Practice Full Paper
Faisal Aqlan1 , Abdulrahman Shamsan2, Joshua Nwokeji3
1 Pennsylvani State University Erie, PA
2 Binghamton University Binghamton, NY
3 Gannon University, Erie PA

This Research to Practice Full Paper provides an approach and practical examples that can used to teach and learn data analytics. Data analytics has been recently adopted by many researchers and professionals working with data in both academic and industry. With the increase in demand for data analysts, there has been a parallel growth in data analytics training programs within companies and educational institutions. But one major challenge in teaching analytics is the overhead and high learning curve of sophisticated tools such as SAS and Tableau. The aim of this paper is to address this challenge. Hence, we  introduce the concepts in data analytics and present practical examples using Microsoft Access and Excel. The four types of data analytics (i.e., descriptive, diagnostic, predictive, and prescriptive) are discussed and an example is provided for each type. For descriptive analytics, we discuss the data properties and models and present examples of database design and implementation in Microsoft Access. The example for diagnostic analytics involves an ergonomic assessment application in Microsoft Excel to identify the sources of ergonomic risks in work environments. Predictive analytics examples include regression and clustering models implementation in Microsoft Excel. Finally, the prescriptive analytics example involves optimizing the snow removal process by developing an optimization model and its implementation in Excel. These examples can be re-used by those who intend to teach analytics and can help students to understand data analytics and be able to implement the different data analytics models in Microsoft Access and Excel.