A Project Based Approach to Statistics and Data Science

TL;DR

This paper proposes a project-based model for teaching statistics and data science in interdisciplinary settings, emphasizing real-world datasets, student communication skills, and collaboration across departments.

Contribution

It introduces a practical framework for non-statistician faculty to effectively teach statistics through projects involving real data and interdisciplinary collaboration.

Findings

Students gain experience with real-world datasets

Improved interdisciplinary research readiness

Enhanced technical writing and communication skills

Abstract

In an increasingly data-driven world, facility with statistics is more important than ever for our students. At institutions without a statistician, it often falls to the mathematics faculty to teach statistics courses. This paper presents a model that a mathematician asked to teach statistics can follow. This model entails connecting with faculty from numerous departments on campus to develop a list of topics, building a repository of real-world datasets from these faculty, and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines. The end result is students who are well prepared for interdisciplinary research, who are accustomed to coping with the idiosyncrasies of real data, and who have sharpened their technical writing and speaking skills.