Integrating Data Science into the Curriculum: Seven Prototypes
To illustrate how to develop novel data science curricula, we surveyed a number of faculty about their approaches to integrating data science into the statistics curricula. The descriptions presented here represent a number of innovative approaches. They are similar in that they all share a goal of having students become proficient in data technologies and programming tools for problem solving with data. However, their approaches vary in terms of mode of delivery, topics, and learning outcomes. Our goal in providing these examples is that these “existence proofs” can be useful to those who are working to integrate data science approaches into their own statistics curriculum.
The instructors have also shared their syllabi and some course materials to provide more concrete guidance about the types of modules, units, and assignments for others to adapt and adopt. The materials include lecture notes, class projects, homework assignments, etc. These resources are noted at the end of each prototype description. The syllabi have all been collected on the website, http://hardin47.github.io/DataSciStatsMaterials/. The data science exemplars are presented alphabetically by author.