Practical Data Science with R has more of a data science spin than machine learning. Part 1 is introductory looking at loading data into R. Part 2 starts off with model evaluation and works through models in increasing complexity through k-NN, Naive

Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and Javascr ipt

Illustrates a range of statistical computations in R using the Rcpp package Provides a general introduction to extending R with C++ code Features an appendix for R users new to the C++ programming language Rcpp packages are presented in the context

Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. With Six Sigma, we are solving problems and improving processes using as a basis one of the most powerful tools of human development: the scientific metho

The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of data-driven decision making are plentiful and in

Biostatistics with R is designed to mimic the interaction between theory and application in statistics. Most topics are motivated by real examples first, and after discussing a topic, the author shows how it can be applied to the problem that motiva