Resources
Course material repositories
- Data Science in a Box: datasciencebox.org
Course websites
- STA 199 at Duke: https://sta199-fa21-003.netlify.app
- STA 210 at Duke: https://sta210-s22.github.io/website
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
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.
- Three principles for modernizing an undergraduate regression analysis course. arXiv preprint arXiv:2205.11026. https://doi.org/10.48550/arXiv.2205.11026
- Tackett, M.