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Some Useful Resources

Books

  1. Dean, J. (2014). Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners. Wiley.
  2. Efron, B. and Tibshirani, R.J. (1993). \textit{An Introduction to the Bootstrap}. Chapman and Hall.
  3. Hastie, T., Tibshirani, R.J. and Friedman, J. (2009). The Elements of Statistical Learning, 2nd Ed. Springer.
  4. James, G., Witten, D., Hastie, T.J. and Tibshirani, R.J. (2013). An Introduction to Statistical Learning. Springer.
  5. Kuhn, M. and Johnson, K. (2013). Applied Predictive Modelling. Springer.
  6. Larose, D. and Larose C. (2014). Discovering knowledge in data : an introduction to data mining, 2nd Ed. Wiley.
  7. Ross, A. (2016). The Industries of the Future. Simon and Schuster.
  8. Witten, I., Frank E., and Hall, M. (2011). Data mining : practical machine learning tools and techniques, 3rd Ed. Morgan Kaufmann.

Papers and Web resources

  1. Ada L. Garcia, Karen Wagner, Torsten Hothorn, Corinna Koebnick, Hans-Joachim F. Zunft and Ulrike Trippo (2005), Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths. Obesity Research, 13(3), 626-634.
  2. Breiman, L. and Cutler, A. http://www.stat.berkeley.edu/~breiman/RandomForests/
  3. Davenport, T and Patil, D. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, 90, pp 70-76.
  4. Efron, B. (1983). Estimating the error rate of a prediction rule: some improvements on cross-validation, Journal of the American Statistical Association 78: 316-331.
  5. Efron, B. and Tibshirani, R. (1997). Improvements on cross-validation: the 632+ bootstrap: method, \textit{Journal of the American Statistical Association} 92: 548-560. 22(4), 477-505.
  6. Friedman, J. (2007). Data mining and Statistics: Whats the connection?
    7. http://statweb.stanford.edu/~jhf/ftp/dm-stat.pdf
  7. Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22, 477-505
  8. Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Mattthias Schmid and Benjamin Hofner (2010), Model-based Boosting 2.0. Journal of Machine Learning Research, 11, 2109-2113.
  9. Wikipedia (2016) Cross Industry Standard Process for Data Mining, https://en.wikipedia.org/wiki/Cross\_Industry\_Standard\_Process\_for\_Data\_Mining