Livros / Books

Books I’ve read, am reading, or plan to read — related to data, engineering, statistics, and culture.

Lendo / Reading

Software Engineering at Google — Titus Winters, Tom Manshreck, Hyrum Wright (2020) — **

The Pragmatic Programmer — David Thomas, Andrew Hunt (1999) — **
Principles of Data Mining — David Hand, Heikki Mannila, Padhraic Smyth (2001) — **
Data Points: Visualization That Means Something — Nathan Yau (2013) — **
Nerds on Wall Street — David Leinweber (2009) — **
The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) — **
Pattern Recognition and Machine Learning — Christopher Bishop (2006) — **
Probabilidade e Inferência Estatística — DeGroot, Schervish (2012) — **
Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016) — **
Designing Data-Intensive Applications — Martin Kleppmann (2017) — **
The Signal and the Noise — Nate Silver (2012) — **
Thinking, Fast and Slow — Daniel Kahneman (2011) — **
The Book of Why — Judea Pearl, Dana Mackenzie (2018) — **
Weapons of Math Destruction — Cathy O’Neil (2016) — **
Feature Engineering for Machine Learning — Alice Zheng, Amanda Casari (2018) — **
A Philosophy of Software Design — John Ousterhout (2018) — **
Clean Code — Robert C. Martin (2008) — **
Building Machine Learning Powered Applications — Emmanuel Ameisen (2020) — **
Naked Statistics — Charles Wheelan (2013) — **
An Introduction to Statistical Learning — James, Witten, Hastie, Tibshirani (2013) — **