Data Science Summer Reading List 2013

Machine Learning: A Probabilistic Perspective, by Kevin Murphy.

Boosting: Foundations and Algorithms, by Robert E. Schapire.

Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, by Emanuel Derman.

Doing Data Science, by Cathy O'Neil and Rachel Schutt.

Big Data: A Revolution that Will Transform How We Live, Work and Think, by Viktor Mayer-Schonberger and Kenneth Cukier.

Eleven Rings, by Phil Jackson.

Nine Algorithms That Changed the Future, by John MacCormick.

The Best and the Brightest, by David Halberstam.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel.

Data Points: Visualization Secrets, by Nathan Yau.

Antifragile, by Nassim Nicholas Taleb.

Who Owns The Future?, by Jaron Lanier.

The Creative Destruction of Medicine, by Dr. Eric Topol.

How to Lie with Statistics, by Darrell Huff.

1984, by George Orwell.

The New Digital Age, by Eric Schmidt and Jared Cohen.

Bad Pharma: How Drug Companies Mislead Doctors and Harm Patients, by Dr. Ben Goldacre.

The Smartest Guys in the Room, by Bethany McLean and Peter Elkind.

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Tags: 2013, Algorithms, Data, Learning, List, Machine, Reading, Science, Summer


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Comment by Michael Malak on July 11, 2013 at 6:19am

Doing Data Science won't be available until Sep. 22, which is after the fall equinox :-)

Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs, published just this past February, is a good book on managing a data analytics team and the process, but no nitty-gritty hands-on details if that's what you're looking for.

Applied Predictive Modeling by Max Kuhn, which just came out a week ago from Springer, combines intense math with practical application in R, but surprisingly doesn't even mention ARIMA or any of its variants, which has in recent years become the "go to" firs t step for predictive analytics in R!

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