Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum

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. Add...

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Bibliographic Details
Published inThe American statistician Vol. 72; no. 1; pp. 58 - 65
Main Authors Çetinkaya-Rundel, Mine, Rundel, Colin
Format Journal Article
LanguageEnglish
Published Alexandria American Statistical Association 01.02.2018
Subjects
Computation
Curricula
Data science
Infrastructure
Pedagogy
Regression analysis
Reproducibility
Software
Statistical analysis
Statistical methods
Statistics
Students
Teachers
Toolkits
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Summary: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.
ISSN:0003-1305
1537-2731
DOI:10.1080/00031305.2017.1397549