This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science—such as visualization, data manipulation, and database usage—that instructors at a wide-range of institutions can incorporate into existing statistics courses. The resulting materials are flexible enough to serve both introductory and advanced students, and aim to provide students with the skills to experiment with data, find their own patterns, and ask their own questions. In this article, we discuss a tutorial on data visualization and a case study synthesizing data wrangling and visualization skills in detail, and provide references to additional class-tested materials. R and R Markdown are used for all of the activities.
CITATION STYLE
Loy, A., Kuiper, S., & Chihara, L. (2019). Supporting Data Science in the Statistics Curriculum. Journal of Statistics Education, 27(1), 2–11. https://doi.org/10.1080/10691898.2018.1564638
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