Core References for Further Learning

Below is a curated list of core references used throughout this guide — grouped by relevance to general data science foundations, R, Python, and tooling. These works and tools support the skills and workflows demonstrated in each Q&A.

📘 General & Framework

• (Buza, 2025) — Complex Data Insights (CDI): A Q&A Approach to Learning Data Science
  
• (Xie, 2016) — bookdown: Technical documentation with R Markdown
  
• (Team, 2023) — Jupyter: Open-source interactive computing

🐍 Python Tools

• (McKinney, 2022) — Python for Data Analysis by Wes McKinney
  
• (Pedregosa et al., 2011) — Scikit-learn: Machine learning in Python

🅡 R Ecosystem

• (Wickham & Grolemund, 2016) — R for Data Science by Wickham & Grolemund
  
• (Wickham, 2023) — tidyverse: Unified R packages for data science
  
• (Wickham, 2016) — ggplot2: Elegant graphics for data analysis

📊 Statistical Learning

• (James et al., 2021) — An Introduction to Statistical Learning by James et al.

Full Linked References

Buza, T. M. (2025). Complex data insights: A q&a approach to learning data science. https://complexdatainsights.com/

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning (2nd ed.). Springer. https://www.statlearning.com/

McKinney, W. (2022). Python for data analysis (3rd ed.). O’Reilly Media. https://wesmckinney.com/book/

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. In Journal of Machine Learning Research (Vol. 12, pp. 2825–2830). https://scikit-learn.org/

Team, J. D. (2023). Project jupyter. https://jupyter.org/

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer. https://ggplot2.tidyverse.org/

Wickham, H. (2023). Tidyverse: R packages for data science. https://www.tidyverse.org/

Wickham, H., & Grolemund, G. (2016). R for data science. O’Reilly Media. https://r4ds.hadley.nz/

Xie, Y. (2016). Bookdown: Authoring books and technical documents with r markdown. https://bookdown.org/yihui/bookdown/