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Teaching Statistics at Google-Scale

Author

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  • Nicholas Chamandy
  • Omkar Muralidharan
  • Stefan Wager

Abstract

Modern data and applications pose very different challenges from those of the 1950s or even the 1980s. Students contemplating a career in statistics or data science need to have the tools to tackle problems involving massive, heavy-tailed data, often interacting with live, complex systems. However, despite the deepening connections between engineering and modern data science, we argue that training in classical statistical concepts plays a central role in preparing students to solve Google-scale problems. To this end, we present three industrial applications where significant modern data challenges were overcome by statistical thinking.[Received December 2014. Revised August 2015.]

Suggested Citation

  • 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.
    • Handle: RePEc:taf:amstat:v:69:y:2015:i:4:p:283-291
    DOI: 10.1080/00031305.2015.1089790
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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. Tim C. Hesterberg, 2015. "What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 371-386, November.
    3. 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.

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