Books I've learned from and annual reports I've read. Updated as I go.
📚 Books
The Intelligent Investor
Benjamin Graham
Chapter 8 on Mr. Market is the best mental model for dealing with volatility in any domain — data, markets, or your own career. The rest is a long argument for doing homework before buying things, which shouldn't need 600 pages but apparently does.
finance investing
Investment Valuation
Aswath Damodaran
The textbook behind my
DCF calculator. Damodaran is rare — an academic who admits his models are wrong and explains exactly how. The terminal value chapters should be required reading for anyone who's ever put a growth rate in a spreadsheet and called it a forecast.
finance valuation
Designing Data-Intensive Applications
Martin Kleppmann
Made me understand, retroactively, why every design decision in the Airbus pipeline was the way it was. If you're going to read one book on data engineering, this is the one. If you've already built a pipeline and it broke, this is the book that will tell you why.
engineering data
Thinking, Fast and Slow
Daniel Kahneman
Explains why certain dashboards work and others don't, better than any data viz course. Read it before Lenovo, re-read it after watching executives make decisions from my charts. System 1 is the audience for every deck you'll ever build.
psychology decisions
The Man Who Solved the Market
Gregory Zuckerman
Read it in a weekend. The takeaway isn't "quant trading is cool." It's that Renaissance found signal by hiring people who knew nothing about finance but everything about pattern recognition. The best financial returns came from outsiders. Encouraging, if you're an outsider.
finance quant
Python for Data Analysis
Wes McKinney
The Pandas bible. I reopen it every two weeks when I forget how multi-indexing works. At this point the spine is purely decorative.
technical python