Here’s an inspirational, and perhaps true, quote by Stephen Boyd:

When you end up being trained in just a handful of areas, you can be unbelievably effective across fifty fields. If you know linear algebra, optimization, probability and statistics, and computer science – those are the topics. You don’t have to know anything else.

If you know those things, and really know them, and if you’ve seen a bunch of applications across different fields, you are so valuable it’s crazy. You can be hired instantly into a hundred fields. But here’s the even cooler thing, and this is the Silicon Valley part of it: you can create new fields that don’t even exist. Please train yourself broadly. Learn these things.

Below is a reading list for aspiring data scientists and others interested in mathematical modeling, statistics, optimization, scientific programming, etc.

• Only my favorite books are included. Books I would recommend.
• Only general topics useful to many practitioners are included. Category theory, game theory and geostatistics is cool – but not that useful to the average practitioner.
• I have read the majority of the material, but not everything.
• The resources are sorted by difficulty within reach category, but only roughly.

The nomenclature is:

• 📗 Book.
• 📄 Paper.
• 📺 Video lecture(s).
• 🤖 Book contains many code examples.
• ⭐ Life changing.

Mathematics

• 📗 Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares by Boyd et al (2018).
• 📗 Advanced Engineering Mathematics by Michael Greenberg (1998).
• 📗 ⭐ Introduction to Applied Mathematics by Gilbert Strang (1986).
• 📗 The Princeton Companion to Mathematics by Gowers et al (2008).
• 📄 An Introduction to the Conjugate Gradient Method Without the Agonizing Pain by Jonathan Richard Shewchuk (1994).

Scientific computing

• 📗 Numerical Methods for Scientists and Engineers by R. W. Hamming (1987).
• 📗 🤖 From Python to Numpy by Nicolas P. Rougier (2017).
• 📗 Numerical Linear Algebra by Trefethen et al (1997).
• 📗 Matrix Computations by Golub et al (2013).

Python and programming

• 📗 The Pragmatic Programmer by Hunt et al (2019).
• 📗 🤖 Fluent Python: Clear, Concise, and Effective Programming by Luciano Ramalho (2015).
• 📗 🤖 Effective Python: 90 Specific Ways to Write Better Python by Brett Slatkin (2019).
• 📺 So you want to be a Python expert? by James Powell (2017).
• 📺 Transforming Code into Beautiful, Idiomatic Python by Raymond Hettinger (2013).

Machine learning

• 📗 Artificial Intelligence: A Modern Approach by Norvig et al (2009).
• 📗 Pattern Classification by Duda et al (2000).
• 📗 Pattern Recognition and Machine Learning by Christopher M. Bishop (2006).
• 📗 The Elements of Statistical Learning by Hastie et al (2016).
• 📄 A Few Useful Things to Know about Machine Learning by Pedro Domingos (2012).
• 📄 Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead by Cynthia Rudin (2018).
• 📄 Machine Learning: The High Interest Credit Card of Technical Debt by Sculley et al (2014).

Deep learning etc.

• 📗 🤖 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron (2022).
• 📗 Reinforcement Learning by Sutton (2018).
• 📺 DeepMind x UCL | Reinforcement Learning Course by David Silver (2015).

Statistics

• 📗 Statistical Inference by Casella et al (2001).
• 📗 Regression and Other Stories by Gelman et al (2020).
• 📗 Data Analysis Using Regression and Hierarchical Models by Gelman et al (2006).
• 📗 All of Statistics by Larry Wasserman (2010).
• 📗 🤖 ⭐ Statistical Rethinking by Richard McElreath (2020).
• 📺 Statistical Rethinking by Richard McElreath (2022).
• 📄 The garden of forking paths by Gelman et al (2019).
• 📄 Statistical Modeling: The Two Cultures by Leo Breiman (2001).
• 📄 Hierarchical generalized additive models in ecology by Pedersen et al (2019).
• 📄 What are the most important statistical ideas of the past 50 years? by Gelman et al (2021).
• 📄 A Conceptual Introduction to Hamiltonian Monte Carlo by Michael Betancourt (2018).

Optimization

• 📗 The Basics of Practical Optimization by Adam B. Levy (2022).
• 📗 Optimization Modelling: A Practical Approach by Sarker et al (2007).
• 📗 Essentials of Metaheuristics by Sean Luke (2012).
• 📗 Linear Programming: Foundations and Extensions by Robert J. Vanderbei (2021).
• 📗 Convex Optimization by Boyd et al (2004).
• 📗 Numerical Optimization by Nocedal et al (2006).
• 📄 Formulating Integer Linear Programs: A Rogues’ Gallery by Brown et al (2007).
• 📄 Bus Routing Optimization Helps Boston Public Schools Design Better Policies by Bertsimas et al (2020).

Algorithms

• 📗 The Algorithm Design Manual by Steven S. Skiena (2021).
• 📗 ⭐ Algorithms by Dasgupta et al (2006).
• 📗 🤖 Competitive Programming by Halim et al.
• 📗 Introduction to Algorithms by Cormen et al (2022).
• 📗 Network Flows: Theory, Algorithms, and Applications by Ahuja et al (1993).

Popular science

• 📗 Flaws and Fallacies in Statistical Thinking by Stephen K. Campbell (2012).
• 📗 Innumeracy: Mathematical Illiteracy and Its Consequences by John Allen Paulos (2001).
• 📗 Science Fictions by Stuart Ritchie (2020).
• 📗 Concepts of Modern Mathematics by Ian Stewart (1995).
• 📗 Euler: The Master of Us All by William Dunham (1999).
• 📗 The Book of Why by Judea Pearl et al (2018).

Miscellaneous

• 📗 The Visual Display of Quantitative Information by Edward R. Tufte (2001).
• 📗 On Writing Well: The Classic Guide to Writing Nonfiction by William Zinsser (2016).
• 📗 Handbook of Writing for the Mathematical Sciences by Nicholas J. Higham (2019).
• 📗 How to Solve It by Polya et al (2014).
• 📗 The Matrix Cookbook by Pedersen et al (2012).
• 📗 Opt Art: From Mathematical Optimization to Visual Design by Robert Bosch (2019).
• 📄 School Choice: A Mechanism Design Approach by Abdulkadiroğlu et al (2003).