# New Book: Data Science for Business,by Provost and Fawcett

This book is for those who need to understand data science/data mining broadly and those who want to develop their skill at data-analytic thinking. It presents fundamental principles which are the foundation for many data mining techniques, and the basis for approaching business problems data-analytically.

**Data Science for Business**,

by Foster Provost and Tom Fawcett O’Reilly, August 2013

This book is intended for (i) those who need to understand data science/data mining broadly and (ii) those who want to develop their skill at data-analytic thinking.

It is not a book about algorithms. Instead it presents a set of fundamental principles for getting business value by extracting useful knowledge from data. These fundamental principles are the foundation for many data mining techniques, but they also are the basis for frameworks for approaching business problems data-analytically, evaluating data science solutions, and evaluating general plans for data analytics.

The book builds up the reader’s understanding of data science by discussing the fundamental principles in the context of business examples, and then shows specifically how the principles can provide understanding of many of the most common methods and techniques used in data science. After reading the book, the reader should be able to (i) discuss data science intelligently with data scientists and with other stakeholders, (ii) better understand proposals for data science projects and data science investments, and (iii) participate integrally in data science projects.

As one example, a fundamental principle of data science is that solutions for extracting useful knowledge from data must carefully consider the problem from the business perspective. This may sound obvious at first, but the notion underlies many choices that must be made in the process of data analytics, including problem formulation, method choice, solution evaluation, and general strategy formulation. Another fundamental principle is that some data items can give us information about other data items. This principle manifests itself throughout data science: in the basic notion of finding “correlations” among variables, in the specific design of many particular data mining procedures, and more generally as the basis for all predictive analytics.

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