Supporting Data Science in the Statistics Curriculum

Abstract

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.

KEYWORDS:

• Data visualization
• Data wrangling
• R
• Relational databases
• Undergraduate curriculum

Acknowledgment

The authors thank the reviewers and associate editor for their helpful comments and suggestions.

Supplementary Material

• https://github.com/ds4stats - The current versions of the R Tutorials and case studies can be downloaded directly from GitHub.

• http://bit.ly/RTutorials - Shonda Kuiper’s website presenting links to each R Tutorial, along with necessary prerequisites. Instructors do not need to be familiar with GitHub to access materials here.

• Assessment - The description and results of the assessment of these materials is available in the supplemental materials.

Additional information

Funding

This work was completed with partial support from the Associated Colleges of the Midwest Faculty Career Enhancement (ACM FaCE) grant and Teagle Foundation through the Midwest Hybrid Learning Consortium.