Infrastructure and tools for teaching computing throughout the statistical curriculum
- Published
- Accepted
- Subject Areas
- Computer Education, Data Science, Graphics, Scientific Computing and Simulation, Software Engineering
- Keywords
- R markdown, git / github, reproducibility, data science, workflow, R language, Continuous integration, RStudio, teaching, cirriculum
- Copyright
- © 2017 Cetinkaya-Rundel et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Infrastructure and tools for teaching computing throughout the statistical curriculum. PeerJ Preprints 5:e3181v1 https://doi.org/10.7287/peerj.preprints.3181v1
Abstract
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 both 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.
Author Comment
This is part of the 'Practical Data Science for Stats' Collection.