Well two of them.
1. Awesome easy to grok book on DataScience- Thanks John for creating something so simple,. I guess your book did not receive the As from the cool kids who are stumbling on latest ML package. You chose the workhorse of them all to explain underpinning of every approach (clustering/regression/Bayes….) – Excel. Gasp.
But you know what you just wrote the most easy to understand – approach to Data Analysis. Wished this was included as applied ML – in coursera/edx etc.
From the days of the dealing with dealer to this – you just nailed it. Waiting for the next episode.
2. You taking on Tufte defending powerpoint. Glorious. Nobody messes with that God and all this while you were getting the mana from him. Priceless.
If you are one of those people who prefer a book to learn from and refer to than this book is one of them.He throws in R translation for good measure. For folks who have taken deeper course in ML techniques this will be a shocker as he does not delve into deep lasso or gibbs method or your favorite technique of reducing parameters.
It is a great companion to R book An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani .
For me this book is in category of learning cum reference book in likes of Kernighan, haskell, little schemer, Don Box’s essential com, Java concurrency book, clojure programming(chas emerick) or the unix book, Eric Sammer’s Hadoop Operations. I would dare put it in skiena’s category. That is just me. This books goes over pitfalls, explains the approach in a crisp way.
Next book to wait for – Nathan Marz.