No abstract

The nature of statistics is changing significantly with many opportunities to broaden the discipline and its impact on science and policy. To realize this potential, our curricula and educational culture must change. While there are opportunities for significant change in many dimensions, we focus more narrowly on computing and call for computing concepts to be integrated into the statistics curricula at all levels. Computational literacy and programming are as fundamental to statistical practice and research as mathematics. We advocate that our field needs to define statistical computing more broadly to include advancements in modern computing, beyond traditional numerical algorithms. Information technologies are increasingly important and should be added to the curriculum, as should the ability to reason about computational resources, work with large data sets, and perform computationally intensive tasks. We present an approach to teaching these topics in combination with scientific problems and modern statistical methods that focuses on ideas and skills for statistical inquiry and working with data. We outline the broad set of computational topics we might want students to encounter and offer ideas on how to teach them. We also discuss efforts to share pedagogical resources to help faculty teach this modern material.

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.

No abstract

The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science.