General Data Science

A small collection of resources about Data Science.

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Data Science Body of Knowledge & Competences

• Computing Competencies for Undergraduate Data Science Curricula ACM Data Science Task Force. ACM (Association for Computer Machinery).
• The EDISON Data Science Framework.
• Data Science for Undergraduates: Opportunities and Options(2018). National Academies Press.

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• 50 years of Data Science. David Donoho.
• A Guide for Ethical Data Science. Royal Statistical Society.
• Algorithms. Jeff Erickson.
• An Introduction to Earth and Environmental Data Science. Ryan Abernathey.
• Applied Data Science. Ian Langmore and Daniel Krasner, Columbia University.
• Computer Age Statistical Inference. Algorithms, Evidence and Data Science. Bradley Efron and Trevor Hastie. Stanford University.
• Cookie Cutter Data Science. Driven Data.
• Data Management in Large-Scale Education Research. Crystal Lewis.
• Data Science and Machine Learning. Mathematical and Statistical Methods. Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman.
• Data Science. Concepts and Practice. 2nd. Ed. Vijay Kotu and Bal Deshpande.
• Data Science: Theories, Models, Algorithms, and Analytics. S.R. Das.
• Data Science for Psychologists. Hansjörg Neth.
• Data Science in Education using R, 2nd. Ed.. Ryan A. Estrellado, Emily A. Freer, Joshua M. Rosenberg, and Isabella C. Velásquez.
• Democratizing Innovation. Erik von Hippel. MIT Press, 2005.
• Data Visualization. A practical introduction. Kieran Healy.
• Foundations of Data Science. Avrim Blum, John Hopcroft, and Ravindran Kannan.
• Fundamentals of Data Visualization. Claus O. Wilke.
• Geographic Data Science with R: Visualizing and Analyzing Environmental Change. Michael C. Wimberly.
• Geographic Data Science with Python. Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf.
• Lies, Damned Lies, and Data Science. Béatrice Moissinac.
• Linear Algebra for Data Science. Shaina Race Bennett.
• Mathematics for Computer Science. Eric Lehman, Tom Leighton, and Albert Meyer.
• The Fourth Paradigm. Data-Intensive Data Discovery. Eds. Tony Hey, Stewart Tansley, and Kristin Tolle. Microsoft, 2009.
• The Science of Visual Data Communication: What Works. Franconeri, S. L., Padilla, L. M., Shah, P., Zacks, J. M., & Hullman, J. (2021). Psychological Science in the Public Interest, 22(3), 110-161.
• Thinking with Data. An Intuitive Approach to Data Science. A beginner's guide to data science with Python and SQL. thinkwithdata.org.
• Veridical Data Science. Bin Yu, Rebecca L. Barter. (Supplementary Code and Data)
• What makes a visualization elegant. Andy Kirk.

Other readings

• Making Kin with the Machines. by Jason Edward Lewis, Noelani Arista, Archer Pechawis, and Suzanne Kite. Journal of Design and Science (2018).

On Research Software Engineering

• An RSE Group Model: Operational and Organizational Approaches From Princeton University’s Central Research Software Engineering Group. Ian Cosden.
• The Four Pillars of Research Software Engineering. J. Cohen, D. S. Katz, M. Barker, N. Chue Hong, R. Haines and C. Jay.
• Towards Sustainable Research Software. Daniel S. Kats.

Microsoft Courses

• Data Science for beginners

Recommended DS learning paths

• Data Scientist Handbook 2024. Andres Vourakis.

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Updated: 09/24/2025 (C. Lizarraga)