Resources

Caution

This page is under construction and will be updated until the start of the tutorial.

Getting started with WebR

• Empowering Your Quarto Documents with R through webR

• Creating your first webR-powered Quarto Document (Video)

Curriculum and pedagogy

• Baumer, B. (2015). A data science course for undergraduates: Thinking with data. The American Statistician, 69(4), 334-342. https://doi.org/10.1080/00031305.2015.1081105.
• Çetinkaya-Rundel, M., & Ellison, V. (2021). A fresh look at introductory data science. Journal of Statistics and Data Science Education, 29(sup1), S16-S26. https://doi.org/10.1080/10691898.2020.1804497.
• Çetinkaya-Rundel, M., Hardin, J., Baumer, B., McNamara, A., Horton, N., & Rundel, C. (2022). An educator’s perspective of the tidyverse. Technology Innovations in Statistics Education, 14(1). http://dx.doi.org/10.5070/T514154352.
• Meyer, E., & Çetinkaya-Rundel, M. (2025). A Blueprint to Design Curriculum and Pedagogy for Introductory Data Science. arXiv. https://doi.org/10.48550/arXiv.2508.03952
• Nolan, D., & Temple Lang, D. (2010). Computing in the statistics curricula. The American Statistician, 64(2), 97-107.

Curriculum guidelines

• Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report - 2016

• Guidelines for Assessment and Instruction in Statistics Education College Report - 2025 Revision (in progress)

• Two-Year College Data Science Summit Report (2018)

• Curriculum Guidelines for Undergraduate Programs in Data Science (2017)

Computing infrastructure

• Çetinkaya-Rundel, M., & Rundel, C. (2018). Infrastructure and tools for teaching computing throughout the statistical curriculum. The American Statistician, 72(1), 58-65.

• RStudio in a Docker Container (at Duke) by Mark McCahill

• Posit Cloud

Teaching with AI tools

• Bien, J., & Mukherjee, G. (2025). Generative AI for Data Science 101: Coding Without Learning To Code. Journal of Statistics and Data Science Education, 33(2), 129-142.
• Generative AI in Statistics and Data Science Education (Journal of Statistics and Data Science collection)
• Leveraging LLMs for student feedback in introductory data science courses by Mine Çetinkaya-Rundel (USCOTS presentation)
• Learning the tidyverse with the help of AI tools by Mine Çetinkaya-Rundel (Tidyverse blog)

Teaching with git and GitHub

• Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., & Tackett, M. (2021). Implementing version control with Git and GitHub as a learning objective in statistics and data science courses. Journal of Statistics and Data Science Education, 29(sup1), S132-S144. https://doi.org/10.1080/10691898.2020.1848485.

Making teaching materials with Quarto

• Dogucu, M., & Çetinkaya-Rundel, M. (2022). Tools and Recommendations for Reproducible Teaching. arXiv preprint arXiv:2202.09504. https://doi.org/10.48550/arXiv.2202.09504
• Organizing Teaching Materials by Maria Tackett & Mine Çetinkaya-Rundel
• Reproducible documents, presentations, and websites with Quarto by Hunter Glanz, Emily Robinson & Allison Theobold
• Hello Quarto: Share, Collaborate, Teach, Reimagine by Mine Çetinkaya-Rundel & Julia Lowndes
• quarto.org

Course material repositories

• Data Science in a Box: datasciencebox.org

Course websites

• Introduction to Foundations of Data Science with R (Spring 2025), Elijah Meyer
• Regression Analysis (Spring 2025), Maria Tackett
• Introduction to Data Science and Statistical Thinking (Fall 2024), Mine Çetinkaya-Rundel

Textbooks

• R for Data Science by Hadley Wickham, Garret Grolemund, and Mine Çetinkaya-Rundel.

• Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin

• Modern Data Science with R by Ben Baumer, Daniel Kaplan, and Nick Horton

• Tidy modeling with R by Max Kuhn and Julia Silge