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The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What?

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  • Amy L. Phelps
  • Kathryn A. Szabat

Abstract

Business analytics continues to become increasingly important in business and therefore in business education. We surveyed faculty who teach statistics or whose institutions offer statistics to business students and conducted web searches of business analytics and data science programs that are offered by these faculties associated with schools of business. The intent of the survey and web searches was to gain insight on the current landscape of business analytics and how it may work synergistically with data science at institutions of higher education, as well as inform the role that statistics education plays in the era of big data. The study presents an analysis of subject areas (Statistics, Operations Research, Management Information Systems, Data Analytics, and Soft Skills) covered in courses offered by institutions with undergraduate degrees in business analytics or data science influencing statistics taught to business students. Given the notable contribution of statistics to the study of business analytics and data science and the importance of knowledge and skills acquired in statistics-based courses not only for students pursuing a major or minor in the discipline, but also for all business majors entering the current data-centric business environment, we present findings about who is teaching what in business statistics education.

Suggested Citation

  • Amy L. Phelps & Kathryn A. Szabat, 2017. "The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What?," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 155-161, April.
    • Handle: RePEc:taf:amstat:v:71:y:2017:i:2:p:155-161
    DOI: 10.1080/00031305.2016.1277160
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    References listed on IDEAS

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    1. McCullough, B.D. & Heiser, David A., 2008. "On the accuracy of statistical procedures in Microsoft Excel 2007," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4570-4578, June.
    2. Nicholas Chamandy & Omkar Muralidharan & Stefan Wager, 2015. "Teaching Statistics at Google-Scale," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 283-291, November.
    3. Nicholas J. Horton & Johanna S. Hardin, 2015. "Teaching the Next Generation of Statistics Students to “Think With Data”: Special Issue on Statistics and the Undergraduate Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 259-265, November.
    4. George Cobb, 2015. "Mere Renovation is Too Little Too Late: We Need to Rethink our Undergraduate Curriculum from the Ground Up," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 266-282, November.
    5. Michael F. Gorman & Ronald K. Klimberg, 2014. "Benchmarking Academic Programs in Business Analytics," Interfaces, INFORMS, vol. 44(3), pages 329-341, June.
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    Cited by:

    1. Melissa R. Bowers & Jeffrey D. Camm & Goutam Chakraborty, 2018. "The Evolution of Analytics and Implications for Industry and Academic Programs," Interfaces, INFORMS, vol. 48(6), pages 487-499, November.
    2. Dan Wu & Hao Xu & Yaqi Sun & Siyu Lv, 2023. "What should we teach? A human‐centered data science graduate curriculum model design for iField schools," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(6), pages 623-640, June.

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