Applied Predictive Analytics : Principles and Techniques for the Professional Data Analyst

Abbott, Dean

ISBN 10: 1118727967 / ISBN 13: 9781118727966

Edité par Wiley, 2014

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Détails bibliographiques

Titre : Applied Predictive Analytics : Principles ...

Éditeur : Wiley

Date d'édition : 2014

Reliure : Couverture souple

Etat : good

A propos de ce titre

Synopsis :

Learn the art and science of predictive analytics ― techniques that get results

Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included.

• The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today
• This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions
• Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish
• Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios
• A companion website provides all the data sets used to generate the examples as well as a free trial version of software

Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.

À propos de l?auteur:

DEAN ABBOTT is President of Abbott Analytics, Inc. (San Diego). He is an internationally recognized data mining and predictive analytics expert with over two decades experience in fraud detection, risk modeling, text mining, personality assessment, planned giving, toxicology, and other applications. He is also Chief Scientist of SmarterRemarketer, a company focusing on behaviorally- and data-driven marketing and web analytics.