I attended an interesting webinar on teaching statistical computing skills. The seminar touched on one key paper written in 2010 and a special issue of The Journal of Statistics and Data Science Eduction, published in 2021.
You should start with this article.
- Deborah Nolan and Duncan Temple Lang (2010) Computing in the Statistics Curricula, The American Statistician, 64:2, 97-107, DOI: 10.1198/tast.2010.09132. This article is behind a paywall, but you can view the abstract in html format.
It has a nice Venn diagram that summarizes all of the subtopics that might be covered in a Statistical Computing class.
The special issue has a main article that summarizes the general topics in Statistical Computing and has links in the bibliography to the individual articles.
- Nicholas J. Horton & Johanna S. Hardin (2021) Integrating computing in the statistics and data science curriculum: Creative structures, novel skills and habits, and ways to teach computational thinking, Journal of Statistics and Data Science Education, DOI: 10.1080/10691898.2020.1870416. Available in html format or pdf format.
I won’t include the full bibliographic details of the individual pages, but here are the titles.
- Implementing version control with Git and GitHub as a learning objective in statistics and data science courses
- What is happening on Twitter? a framework for student research projects with tweets
- Teaching statistical concepts and modern data analysis with a computing-integrated learning environment
- A fresh look at introductory data science
- Web scraping in the statistics and data science curriculum: Challenges and opportunities
- Teaching creative and practical data science at scale
- The data mine: Enabling data science across the curriculum
- ‘Playing the whole game’: A data collection and analysis exercise with Google Calendar’
- Easy-to-use cloud computing for teaching data science
- Computing in the statistics curricula: A 10-year retrospective
- Expanding the scope of statistical computing: Training statisticians to be software engineers
- Data science in 2020: Computing, curricula, and challenges for the next 10 years
- Designing data science workshops for data-intensive environmental science research
- How students use statistical computing in problem solving
In addition to the articles in the special issue, the main article cited some from earlier issues or different journals.
- Cobb, G. (2015), Mere renovation is too little too late: We need to rethink our undergraduate curriculum from the ground up’, The American Statistician 69(4), 266-282. Available in html format. You can also find links to a pre-print plus links to the various comments and the rejoinder here. National Academies of Science, Engineering, and Medicine (2018), Data Science for Undergraduates: Opportunities and Options. Available in pdf format. Wing, J. M. (2006), Computational thinking, Communications of the ACM 49(3). Available in html format.
An earlier version of this page was published on new.pmean.com.