Data Science in Statistics Curricula: Preparing Students to "Think with Data"

TL;DR

This paper emphasizes the importance of integrating data science into undergraduate statistics curricula, showcasing diverse approaches and practical resources to prepare students for data-driven roles.

Contribution

It introduces curricular innovations and provides case studies and assignments to help instructors incorporate data science into statistics education.

Findings

Seven institutions' approaches to teaching data science.

Examples of assignments fostering student engagement with data.

Demonstration of curricular innovations for data science integration.

Abstract

A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to utilize databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this paper is to motivate the importance of data science proficiency and to provide examples and resources for instructors to implement data science in their own statistics curricula. We provide case studies from seven institutions. These varied approaches to teaching data science demonstrate curricular innovations to address new needs. Also included here are examples of assignments designed for courses that foster engagement of undergraduates with data and data science.