2017/10/30 by Mine Çetinkaya-Rundel, Colin Rundel · 2 citations
Mathematics · Decision Sciences · Computer Science · #Statistics Education and Methodologies #Scientific Computing and Data Management #Data Analysis with R
paper · doi:10.1080/00031305.2017.1397549
Modern statistics is fundamentally a computational discipline, but too often this fact is not reflected in our statistics curricula. With the rise of big data and data science, it has become increasingly clear that students want, expect, and need explicit training in this area of the discipline. Additionally, recent curricular guidelines clearly state that working with data requires extensive computing skills and that statistics students should be fluent in accessing, manipulating, analyzing, and modeling with professional statistical analysis software. Much has been written in the statistics education literature about pedagogical tools and approaches to provide a practical computational foundation for students. This article discusses the computational infrastructure and toolkit choices to allow for these pedagogical innovations while minimizing frustration and improving adoption for both our students and instructors. Supplementary materials for this article are available online.