2B: Designing Introductory Statistics to Attract Minority Students to Data Science

Abstract

The introductory statistics course represents the main source of quantitative training for undergraduates in the United States. Thus, in the era of data science, efforts should focus on designing introductory statistics to help promote data science literacy and attract students to pursue data science education and/or careers. Such efforts should be particularly supported at minority serving institutions to help with closing the diversity gap in data science. In this breakout session, we will discuss the potential of using introductory statistics to attract underrepresented minority (URM) students to data science, with specific focus on Historically Black Colleges and Universities (HBCUs).

The session intends to (1) describe the current status of introductory statistics at the largest HBCU in the nation, highlight students’ learning gains from the introductory statistics course, and describe the data science programs available at the institution, (2) introduce the audience to the potential of using introductory statistics to promote data science literacy among URM students, and (3) share a proposed introductory statistics course design that seamlessly integrates commonplace data science tools and data science knowledge to enhance students’ statistical skills and promote data science literacy.

This interactive session will engage the audience in a series of discussions on how to design introductory statistics to better serve URM students and attract them to data science. These discussions will encourage participants to comment on the status of introductory statistics and data science at their institutions and on the best practices they use for promoting data science through their teaching of statistics.