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Teaching Stats for Data Science

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  • Daniel Kaplan

Abstract

“Data science” is a useful catchword for methods and concepts original to the field of statistics, but typically being applied to large, multivariate, observational records. Such datasets call for techniques not often part of an introduction to statistics: modeling, consideration of covariates, sophisticated visualization, and causal reasoning. This article re-imagines introductory statistics as an introduction to data science and proposes a sequence of 10 blocks that together compose a suitable course for extracting information from contemporary data. Recent extensions to the mosaic packages for R together with tools from the “tidyverse” provide a concise and readable notation for wrangling, visualization, model-building, and model interpretation: the fundamental computational tasks of data science.

Suggested Citation

  • Daniel Kaplan, 2018. "Teaching Stats for Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 89-96, January.
    • Handle: RePEc:taf:amstat:v:72:y:2018:i:1:p:89-96
    DOI: 10.1080/00031305.2017.1398107
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Kevin Cummiskey & Karsten Lübke, 2022. "Causality in statistics and data science education," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 277-286, December.

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