16  References and Resources

16.1 Resources for Further Learning

These build on the infrastructure this book sets up. Each entry says what it is for, so you can pick rather than work through the list.

16.1.1 Python for Data Science

  1. Python for Data Analysis, 3rd edition by Wes McKinney
    pandas from its author. The third edition is free to read online and covers pandas 2.x; earlier editions predate several API changes.

  2. Python Data Science Handbook by Jake VanderPlas
    Covers NumPy, pandas, matplotlib, and scikit-learn end to end. Free online.

  3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd edition by Aurélien Géron
    Implementation-focused, with worked examples throughout.

  4. Fluent Python, 2nd edition by Luciano Ramalho
    For the Python language itself rather than the data stack. The 2nd edition covers type hints and async, both absent from the 1st.

16.1.2 R for Data Science

  1. R for Data Science, 2nd edition by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
    The standard introduction to the tidyverse. Free online. Note the domain: the 1st edition still lives at r4ds.had.co.nz and is what most search results point to.

  2. Advanced R by Hadley Wickham
    For those wanting to understand R at a deeper level and write more efficient code.

  3. The Big Book of R by Oscar Baruffa
    A curated collection of free R resources across various domains and specialties.

  4. ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
    The reference for ggplot2, from its author. Free online.

16.1.3 SQL and Databases

  1. SQL for Data Analysis by Cathy Tanimura
    A practical guide to using SQL for data science tasks.

  2. Database Design for Mere Mortals by Michael J. Hernandez
    Helps understand database design principles for more effective data modelling.

16.1.4 Version Control and Collaboration

  1. Pro Git by Scott Chacon and Ben Straub
    The reference work on Git, from first commit to internals, available for free online.

  2. Happy Git and GitHub for the useR by Jenny Bryan
    Written for exactly this book’s audience: researchers and analysts who need Git to work, not a full account of how it works. Free online, and its troubleshooting chapters are the ones to reach for when Git has you stuck.

16.1.5 Data Visualisation

  1. Fundamentals of Data Visualisation by Claus O. Wilke
    Principles for creating effective visualisations based on perception science.

  2. Storytelling with Data by Cole Nussbaumer Knaflic
    Focuses on the narrative aspects of data visualisation.

  3. Observable Plot documentation
    For web-based visualisation. Plot covers most of what analysts previously reached for D3 to do, in far less code; drop to D3 only when you need control Plot doesn’t expose.

16.1.6 Cloud Computing and DevOps

  1. Cloud Computing for Data Analysis by Noah Gift
    Project-based, with hands-on labs across AWS, GCP, and Azure.

  2. Docker Documentation
    Docker’s own getting-started material is current and free, which is more than can be said for most published Docker books — the field moves faster than print.

  3. Quarto documentation
    The reference for everything in the Reporting chapter, including the Typst and LaTeX output paths.

16.1.7 Workflow, Testing, and Reproducibility

  1. The Turing Way
    A community-written handbook on reproducible research. The reproducibility and project-design chapters are the closest thing to a canonical reference for the material in the Workflows chapter.

  2. Python Testing with pytest, 2nd edition by Brian Okken
    The standard pytest reference.

  3. R Packages, 2nd edition by Hadley Wickham and Jennifer Bryan
    Free online. Read it when your R code outgrows a folder of scripts — the testing and package-structure chapters apply well before you have anything worth publishing.

  4. Software Carpentry lessons
    Free, well-tested teaching material on the shell, Git, Python, and R, aimed at researchers. Useful both to learn from and to teach from.

16.1.8 Online Learning Platforms

  1. DataCamp
    Interactive courses on Python, R, SQL, and more.

  2. Coursera
    Offers specialisations in data science from top universities.

  3. Kaggle Learn
    Free mini-courses on data science topics with practical exercises.

16.1.9 Communities and Forums

  1. Stack Overflow
    For programming-related questions.

  2. Cross Validated
    For statistics and machine learning questions.

  3. Data Science Stack Exchange
    Specifically for data science questions.

  4. GitHub
    For finding open-source projects to learn from or contribute to.

  5. TeX Stack Exchange
    For questions about LaTeX and document preparation.

Several of the best resources above are free and maintained online, which is not a coincidence — books on tooling date faster than books on method. Where a printed reference and its authors’ website disagree, trust the website.

16.2 Works Cited

Knuth, Donald E. 1984. “Literate Programming.” Comput. J. (USA) 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.

16.3 Image Credits

Cover illustration generated using OpenAI’s DALL·E model via ChatGPT (April 2025).