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)

Lidos / Read

• 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)

Na fila / Queue

• Distributed Systems — Maarten van Steen, Andrew Tanenbaum (2017)