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

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

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

About the Author

Dean Abbott is President of Abbott Analytics, Inc. in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology. Mr. Abbott is also Chief Scientist of SmarterRemarketer, a startup company focusing on behaviorally- and data-driven marketing attribution and web analytics.

Mr. Abbott is a highly-regarded and popular speaker at Predictive Analytics and Data Mining conferences. He has served on the program committee for the KDD Industrial Track and Data Mining Case Studies workshop and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate and the UCSD Data Mining Certificate programs. Mr. Abbott has taught applied data mining and text mining courses using IBM SPSS Modeler, SAS Enterprise Miner, Statsoft Statistica, Tibco Spotfire Miner, IBM Affinium Model, Megaputer Polyanalyst, Salford Systems CART, KNIME, and RapidMiner.