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.