Resources

Course material repositories

• Data Science in a Box: datasciencebox.org

Course websites

• STA 199 at Duke: https://sta199-f22-1.github.io/
• STA 210 at Duke: https://sta210-s22.github.io/website

Textbooks

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

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

• Tidy modeling with R by Max Kuhn and Julia Silge

• Modern Data Science with R by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton

Papers

• Designing introductory data science curricula:

  • 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.

  • 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.

• Teaching reproducibility and version control:
  • 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.
• Teaching with the tidyverse:
  • 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.
• Organizing teaching materials:
  • 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.
• Teaching modern modeling:
  • Tackett, M. (2022). Three principles for modernizing an undergraduate regression analysis course. arXiv preprint arXiv:2205.11026. https://doi.org/10.48550/arXiv.2205.11026