Jason A. French

Northwestern University

Summer Reading on Data Science

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One of the benefits of being at Insight has been a reasonably large library stocked with great material for learning data science. If you’re looking to brush up on your skills or break into the industry, I recommend checking out the following:

  • Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.

  • Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The elements of statistical learning (Vol. 2, No. 1). New York: Springer.

  • Russell, M. A. (2013). Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O’Reilly Media, Inc.

  • McDowell, G. L. (2013). Cracking the Coding Interview: 150 Programming Questions and Solutions. CareerCup.

  • Chang, W. (2012). R graphics cookbook. O’Reilly Media, Inc.

I actually read through Winston’s cookbook before Insight, but it’s been an invaluable resource. Why write 20 lines of matplotlib or R base graphics when you can accomplish a better graph using 5 lines of ggplot2?