Foundations of Predictive Analytics
Author: James Wu and Stephen Coggeshall Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery
Drawing on the authors’ two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling,
Hardcover
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Summary
Drawing on the authors’ two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling,
Description
Written by industry experts, this book introduces the various concepts, theorems, and algorithms widely used in statistical data analysis and data mining. It covers important topics in data mining, machine learning, and statistical pattern recognition, including linear and nonlinear regression models, time series analysis, and variable selection. The text also explores key topics that are not extensively covered in similar books, such as copula functions, incremental regression, censored data models, Dempster-Shafer theory, survival data analysis, and GARCH.
Critic Reviews
“"The book deals with the necessary knowledge for understanding the theoretical and practical aspects regarding the common techniques of exploratory data analysis and modeling. For a better understanding, the underlying assumptions, mathematical formulations, and the algorithms involved by these techniques are presented. The authors made the text self-contained, the book being designed as a supplemental and referential resource for the practitioners dealing with this domain. The book also discusses a variety of practical topics more or less present in the literature." --Book Review by Florin Gorunescu, appearing in Zentralblatt MATH, 1306 1”
"The book deals with the necessary knowledge for understanding the theoretical and practical aspects regarding the common techniques of exploratory data analysis and modeling. For a better understanding, the underlying assumptions, mathematical formulations, and the algorithms involved by these techniques are presented. The authors made the text self-contained, the book being designed as a supplemental and referential resource for the practitioners dealing with this domain. The book also discusses a variety of practical topics more or less present in the literature." -Book Review by Florin Gorunescu, appearing in Zentralblatt MATH, 1306 | 1
About the Author
James Wu is a Fixed Income Quant with extensive expertise in a wide variety of applied analytical solutions in consumer behavior modeling and financial engineering. He previously worked at ID Analytics, Morgan Stanley, JPMorgan Chase, Los Alamos Computational Group, and CASA. He earned a PhD from the University of Idaho.
Stephen Coggeshall is the Chief Technology Officer of ID Analytics. He previously worked at Los Alamos Computational Group, Morgan Stanley, HNC Software, CASA, and Los Alamos National Laboratory. During his over 20 year career, Dr. Coggeshall has helped teams of scientists develop practical solutions to difficult business problems using advanced analytics. He earned a PhD from the University of Illinois and was named 2008 Technology Executive of the Year by the San Diego Business Journal.
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