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Data Mining for Business Analytics - Concepts, Techniques, and Applications in R (Hardcover)
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Data Mining for Business Analytics - Concepts, Techniques, and Applications in R (Hardcover)
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Data Mining for Business Analytics: Concepts, Techniques, and
Applications in R presents an applied approach to data mining
concepts and methods, using R software for illustration Readers
will learn how to implement a variety of popular data mining
algorithms in R (a free and open-source software) to tackle
business problems and opportunities. This is the fifth version of
this successful text, and the first using R. It covers both
statistical and machine learning algorithms for prediction,
classification, visualization, dimension reduction, recommender
systems, clustering, text mining and network analysis. It also
includes: Two new co-authors, Inbal Yahav and Casey Lichtendahl,
who bring both expertise teaching business analytics courses using
R, and data mining consulting experience in business and government
Updates and new material based on feedback from instructors
teaching MBA, undergraduate, diploma and executive courses, and
from their students More than a dozen case studies demonstrating
applications for the data mining techniques described
End-of-chapter exercises that help readers gauge and expand their
comprehension and competency of the material presented A companion
website with more than two dozen data sets, and instructor
materials including exercise solutions, PowerPoint slides, and case
solutions www.dataminingbook.com Data Mining for Business
Analytics: Concepts, Techniques, and Applications in R is an ideal
textbook for graduate and upper-undergraduate level courses in data
mining, predictive analytics, and business analytics. This new
edition is also an excellent reference for analysts, researchers,
and practitioners working with quantitative methods in the fields
of business, finance, marketing, computer science, and information
technology.
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