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Developing a New Interdisciplinary Computational Analytics Undergraduate Program: A Qualitative-Quantitative-Qualitative Approach

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  • Scotland Leman
  • Leanna House
  • Andrew Hoegh

Abstract

Statistics departments play a vital role in educating students on the analysis of data for obtaining information and discovering knowledge. In the last several years, we have witnessed an explosion of data, which was not imaginable in years past. As a result, the methods and techniques used for data analysis have evolved. Beyond this, the technology used for storing, porting, and computing big data has also evolved, and so now must traditionally oriented statistics departments. In this article, we discuss the development of a new computational modeling program that meets these demands, and we detail how to balance the qualitative and quantitative components of modern day data analyses for statistical education.[Received December 2014. Revised August 2015.]

Suggested Citation

  • Scotland Leman & Leanna House & Andrew Hoegh, 2015. "Developing a New Interdisciplinary Computational Analytics Undergraduate Program: A Qualitative-Quantitative-Qualitative Approach," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 397-408, November.
    • Handle: RePEc:taf:amstat:v:69:y:2015:i:4:p:397-408
    DOI: 10.1080/00031305.2015.1090337
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    References listed on IDEAS

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    1. Robert Gould, 2010. "Statistics and the Modern Student," International Statistical Review, International Statistical Institute, vol. 78(2), pages 297-315, August.
    2. Iddo Gal & Irena Ograjenšek, 2010. "Qualitative Research in the Service of Understanding Learners and Users of Statistics," International Statistical Review, International Statistical Institute, vol. 78(2), pages 287-296, August.
    3. David S. Moore, 1997. "New Pedagogy and New Content: The Case of Statistics," International Statistical Review, International Statistical Institute, vol. 65(2), pages 123-137, August.
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