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Statistics & Big Data 25-26 Labs

References

21.6 Statistical Methods and R Programming

  • Everitt, B., Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. Springer-Verlag.

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2015). An Introduction to Statistical Learning, with Applications in R. Springer.

  • Timbers, T., Campbell, T., & Lee, M. (2022). Data Science: A First Introduction. Online version

  • Wickham, H., & Grolemund, G. (2018). R for Data Science. O’Reilly. Freely available online

  • Dauber, D. (2022). R for non-programmers. Free book

21.7 Advanced R Programming

  • Higgins, P. D. R. (2022). Reproducible Medical Research with R. Free book

  • Armstrong, J. K. (2022). Fundamentals of Wrangling Healthcare Data with R. Free book

  • Wickham, H. (2015). Advanced R. CRC Press. Free book

21.8 Statistical Software and Tools

  • R Core Team (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

  • RStudio Team (2024). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA.

  • Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC.

21.9 Course Materials

  • Salvini, N. (2025). Statistics & Big Data 25-26 Labs. Course website and materials.

  • Dabo-Niang, S. (2025). Advanced Modeling Techniques. Intensive session materials.

21.10 Additional Resources