2014/10/31 by Johanna Hardin, Roger W. Hoerl, Roger Hoerl +10 · 2 citations
Computer Science · Mathematics · Psychology · #Big data #Computer science #Curriculum #Data Analysis with R #Data mining #Data science #Mathematics education #Pedagogy #Political science #Psychology #Statistics Education and Methodologies #Statistics education #The Internet #Workforce #World Wide Web #stat.OT
paper · pdf · doi:10.1080/00031305.2015.1077729
arxiv created 2015/08/04 · openalex publication_date 2015/08/12 · arxiv updated 2020/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 use 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 article 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.[Received November 2014. Revised July 2015.]