Data Mining for Business Analytics is used at over 560 universities and colleges, and has been translated into Korean and Chinese. It has been adapted for four software environments (R, Python, Excel and JMP) and, since it was first published in 2007, has been through 11 editions.

Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers at multiple levels of exposure to basic probability and statistics.

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective.

Practical Statistics explains how to apply key statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

In one comprehensive resource, Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications provides complete coverage of statistical and analytical concepts, techniques, and applications for text mining.

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation.

Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the ast decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges—from investment timing to drug discovery, and fraud detection to recommendation systems—where predictive accuracy is more vital than model interpretability.