Library & Resources

Analysis & Linear Algebra

Understanding Analysis

by Stephen Abbott

This is the pinnacle of textbook writing. Stephen Abbott's impressive treatise is rigorous and still manages to be a very lively introduction to the important field of analysis. Definitely a must-read in my opinion.

Principles of Mathematical Analysis

by Walter Rudin

While this book overlaps with Abbott's book in some ways, I consider it to be more advanced and a merited addition to your library. It is considered to be one of the standard books on the topic and well worth your money. Just make sure to skip the last three chapters or so...

Linear Algebra Done Right

by Sheldon Axler

This entirely new approach to linear algebra was surprisingly different from most linear algebra books but introduces topics in a truly wonderful way. In combination with a second textbook on the topic, I consider this to be the gold standard.

Probability Theory

Fundamental Probability

by Marc Paolella

Marc Paolella, the author of this book and one of the best professors I've ever had, used it as the basis for an absolutely amazing introductory class in probability theory. Definitely worth checking out if you're looking for a good place to start and appreciate an emphasis on computational aspects.

First Look at Rigorous Probability Theory

by Jeffrey S. Rosenthal

While I haven't personally read the vast majority of this book, I've heard nothing but high praise for it. Despite my lack of personal experience with this textbook, I've felt the need to include it here as it provides the perfect bridge between Marc Paolella's book and "Probability Essentials" by Jacod and Protter.

Probability Essentials

by Jean Jacod & Philip Protter

This very short paperback is jam-packed with information. It develops important concepts from measure theory on the fly and is highly rigorous and reads quite a bit like a set of lecture notes. It is definitely not perfect from a pedagogic point of view, but still provides a concise and affordable treatment of the subject.


  • Principles of Corporate Finance (by Brealey, Myers and Allen)
  • Investment Valuation: Tools and Techniques for Determining the Value of any Asset (by Damodaran)
  • The Little Book of Valuation (by Damodaran)
  • Options, Futures, and Other Derivatives (by Hull)


  • Introduction to Econometrics (by Stock and Watson)
  • Econometrics (by Hayashi)

Statistics, Machine Learning, and Data Science

  • All of Statistics: A Concise Course in Statistical Inference (by Wasserman)
  • Machine Learning: The Art and Science of Algorithms That Make Sense of Data (by Flach)
  • An Introduction to Statistical Learning (by James, Witten, Hastie and Tibshirani)
  • The Elements of Statistical Learning (by Friedman, Hastie and Tibshirani)
  • A Gentle Introduction to Effective Computing in Quantitative Research (by Paarsch and Golyaev)
  • Artificial Intelligence: A Modern Approach (by Russel and Norvig)

Programming and Computer Science

  • Introduction to Algorithms (by Cormen, Stein, Rivest and Leiserson)
  • Artificial Intelligence: A Modern Approach (by Russel and Norvig)


  • The Art of War (by Sun Tzu)
  • The Prince (by Niccolò Machiavelli)


  • Campbell Biology (by Reece, Urry, Cain, Wasserman, Minorsky and Jackson)


  • The Pyramid Principle: Logic in Writing and Thinking (by Minto)
  • The Elements of Style (by Strunk and White)