Introduction: Pedagogy in Analytics and Data Science

  • Nicholas Evangelopoulos
  • Joseph W. Clark
  • Sule Balkan
Chapter
Part of the Annals of Information Systems book series (AOIS)

Abstract

Keeping with the “Exploring the Information Frontier” theme of the ICIS 2015 conference, the Pre-ICIS Business Analytics Congress workshop sought forward-thinking research in the areas of data science, business intelligence, analytics and decision support with a special focus on the state of business analytics from the perspectives of organizations, faculty, and students. The teaching track aimed to promote comprehensive research or research-in-progress in teaching and learning addressing topics including business analytics curriculum development, pedagogical innovation, organizational case studies, tutorial exercises, and the use of analytics software in the classroom. This work has been summarized in this chapter.

Keywords

Data science Analytics Business intelligence Decision support systems Curriculum design Pedagogy 

References

  1. Deloitte (2016) Analytics trends: the next evolution. Downloaded on October 31, 2016, from http://www.deloitte.com/us/AnalyticsTrends
  2. Dunaway MM (2017) An examination of ERP learning outcomes: a text mining approach. In: Deokar A, Gupta A, Iyer L, Jones MC (eds) Analytics and data science: advances in research and pedagogy. Springer annals of information systems series (http://www.springer.com/series/7573), Vol. 21, 2016–2017
  3. Huguenard BR, Ballou DJ (2017) Neural net tutorial. In: Deokar A, Gupta A, Iyer L, Jones MC (eds) Analytics and data science: advances in research and pedagogy. Springer annals of information systems series (http://www.springer.com/series/7573), Vol. 21, 2016–2017
  4. Kollwitz C, Dinter B, Krawatzeck R (2017) Tools for academic business intelligence & analytics teaching—results of an evaluation. In: Deokar A, Gupta A, Iyer L, Jones MC (eds) Analytics and data science: advances in research and pedagogy. Springer annals of information systems series (http://www.springer.com/series/7573), Vol. 21, 2016–2017
  5. Ransbotham S, Kiron D, Prentice P (2015) Minding the analytics gap. MIT Sloan Manag Rev 56(3):63–68Google Scholar
  6. Schuff D (2017) Data science for all: a university-wide course in data literacy. In: Deokar A, Gupta A, Iyer L, Jones MC (eds) Analytics and data science: advances in research and pedagogy. Springer annals of information systems series (http://www.springer.com/series/7573), Vol. 21, 2016–2017

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Nicholas Evangelopoulos
    • 1
  • Joseph W. Clark
    • 2
  • Sule Balkan
    • 3
  1. 1.University of North TexasDentonUSA
  2. 2.University of MaineOronoUSA
  3. 3.Portland State UniversityPortlandUSA

Personalised recommendations