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Books > Computing & IT > Applications of computing > Databases > Data mining
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Data Mining for Business Analytics - Concepts, Techniques, and Applications with JMP Pro(R) (Hardcover, 3rd Revised edition)
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Data Mining for Business Analytics - Concepts, Techniques, and Applications with JMP Pro(R) (Hardcover, 3rd Revised edition)
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Data Mining for Business Analytics: Concepts, Techniques, and
Applications with JMP Pro(R) presents an applied and interactive
approach to data mining. Featuring hands-on applications with JMP
Pro(R), a statistical package from the SAS Institute, the book uses
engaging, real-world examples to build a theoretical and practical
understanding of key data mining methods, especially predictive
models for classification and prediction. Topics include data
visualization, dimension reduction techniques, clustering, linear
and logistic regression, classification and regression trees,
discriminant analysis, naive Bayes, neural networks, uplift
modeling, ensemble models, and time series forecasting. Data Mining
for Business Analytics: Concepts, Techniques, and Applications with
JMP Pro(R) also includes: * Detailed summaries that supply an
outline of key topics at the beginning of each chapter *
End-of-chapter examples and exercises that allow readers to expand
their comprehension of the presented material * Data-rich case
studies to illustrate various applications of data mining
techniques * A companion website with over two dozen data sets,
exercises and case study solutions, and slides for instructors Data
Mining for Business Analytics: Concepts, Techniques, and
Applications with JMP Pro(R) is an excellent textbook for advanced
undergraduate and graduate-level courses on data mining, predictive
analytics, and business analytics. The book is also a one-of-a-kind
resource for data scientists, analysts, researchers, and
practitioners working with analytics in the fields of management,
finance, marketing, information technology, healthcare, education,
and any other data-rich field. Galit Shmueli, PhD, is Distinguished
Professor at National Tsing Hua University s Institute of Service
Science. She has designed and instructed data mining courses since
2004 at University of Maryland, Statistics.com, Indian School of
Business, and National Tsing Hua University, Taiwan. Professor
Shmueli is known for her research and teaching in business
analytics, with a focus on statistical and data mining methods in
information systems and healthcare. She has authored over 70
journal articles, books, textbooks, and book chapters, including
Data Mining for Business Analytics: Concepts, Techniques, and
Applications in XLMiner(R), Third Edition, also published by Wiley.
Peter C. Bruce is President and Founder of the Institute for
Statistics Education at www.statistics.com He has written multiple
journal articles and is the developer of Resampling Stats software.
He is the author of Introductory Statistics and Analytics: A
Resampling Perspective and co-author of Data Mining for Business
Analytics: Concepts, Techniques, and Applications in XLMiner (R),
Third Edition, both published by Wiley. Mia Stephens is Academic
Ambassador at JMP(R), a division of SAS Institute. Prior to joining
SAS, she was an adjunct professor of statistics at the University
of New Hampshire and a founding member of the North Haven Group
LLC, a statistical training and consulting company. She is the
co-author of three other books, including Visual Six Sigma: Making
Data Analysis Lean, Second Edition, also published by Wiley. Nitin
R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in
Cambridge, Massachusetts. A Fellow of the American Statistical
Association, Dr. Patel has also served as a Visiting Professor at
the Massachusetts Institute of Technology and at Harvard
University. He is a Fellow of the Computer Society of India and was
a professor at the Indian Institute of Management, Ahmedabad, for
15 years. He is co-author of Data Mining for Business Analytics:
Concepts, Techniques, and Applications in XLMiner(R), Third
Edition, also published by Wiley.
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