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Data Science in Statistics Curricula: Preparing Students to “Think with Data”

Author

Listed:
  • J. Hardin
  • R. Hoerl
  • Nicholas J. Horton
  • D. Nolan
  • B. Baumer
  • O. Hall-Holt
  • P. Murrell
  • R. Peng
  • P. Roback
  • D. Temple Lang
  • M. D. Ward

Abstract

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.[Received November 2014. Revised July 2015.]

Suggested Citation

  • J. Hardin & R. Hoerl & Nicholas J. Horton & D. Nolan & B. Baumer & O. Hall-Holt & P. Murrell & R. Peng & P. Roback & D. Temple Lang & M. D. Ward, 2015. "Data Science in Statistics Curricula: Preparing Students to “Think with Data”," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 343-353, November.
    • Handle: RePEc:taf:amstat:v:69:y:2015:i:4:p:343-353
    DOI: 10.1080/00031305.2015.1077729
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    Cited by:

    1. Roger W. Hoerl & Ronald D. Snee, 2017. "Statistical Engineering: An Idea Whose Time Has Come?," The American Statistician, Taylor & Francis Journals, vol. 71(3), pages 209-219, July.
    2. Giani Ionel Gradinaru & Vasile Dinu & Catalin-Laurentiu Rotaru & Andreea Toma, 2024. "The Development of Educational Competences for Romanian Students in the Context of the Evolution of Data Science and Artificial Intelligence," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 26(65), pages 1-14, February.
    3. David Gil & Magnus Johnsson & Higinio Mora & Julian Szymański, 2019. "Review of the Complexity of Managing Big Data of the Internet of Things," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    4. Simons, Andrew M., 2020. "Making Business Statistics Come Alive: Incorporating Field Trial Data from a Cookstove Study into the Classroom," Applied Economics Teaching Resources (AETR), Agricultural and Applied Economics Association, vol. 2(3), July.
    5. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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