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Am Stat. 2018;72(4):382-391. doi: 10.1080/00031305.2017.1356747. Epub 2018 Nov 14.

A Guide to Teaching Data Science.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA.
2
Department of Biostatistics, Harvard School of Public Health, Boston, MA.

Abstract

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is that computing should play a more prominent role. We strongly agree with this recommendation, but advocate the main priority is to bring applications to the forefront as proposed by Nolan and Speed (1999). We also argue that the individuals tasked with developing data science courses should not only have statistical training, but also have experience analyzing data with the main objective of solving real-world problems. Here, we share a set of general principles and offer a detailed guide derived from our successful experience developing and teaching a graduate-level, introductory data science course centered entirely on case studies. We argue for the importance of statistical thinking, as defined by Wild and Pfannkuch (1999) and describe how our approach teaches students three key skills needed to succeed in data science, which we refer to as creating, connecting, and computing. This guide can also be used for statisticians wanting to gain more practical knowledge about data science before embarking on teaching an introductory course.

KEYWORDS:

active learning; applied statistics; computing; data science; reproducibility; teaching principles

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