[Ebook PDF] Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1st Edition

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[Ebook PDF] Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1st Edition

Authors: by Galit Shmueli (Author), Peter C. Bruce (Author), Inbal Yahav (Author), Nitin R. Patel (Author), Kenneth C. Lichtendahl (Author)

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.

Preface to the R edition
This textbook first appeared in early 2007 and has been used by numerous students and practitioners and in many courses, ranging from dedicated data mining classes to more general business analytics courses (including our own experience teaching this material both online and in person for more than 10 years). The first edition, based on the Excel add-in XLMiner, was followed by two more XLMiner editions, a JMP edition, and now this R edition, with its companion website, www.dataminingbook.com.
This new R edition, which relies on the free and open-source R software, presents output from R, as well as the code used to produce that output, including specification of a variety of packages and functions. Unlike computerscience or statistics-oriented textbooks, the focus in this book is on data mining concepts, and how to implement the associated algorithms in R. We assume a basic facility with R.
For this R edition, two new co-authors stepped on board—Inbal Yahav and Casey Lichtendahl—bringing both expertise teaching business analytics courses using R and data mining consulting experience in business and government.
Such practical experience is important, since the open-source nature of R software makes available a plethora of approaches, packages, and functions available for data mining. Given the main goal of this book—to introduce data mining concepts using R software for illustration—our challenge was to choose an R code cocktail that supports highlighting the important concepts. In addition to providing R code and output, this edition also incorporates updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma, and executive courses, and from their students as well.
One update, compared to the first two editions of the book, is the title:
we now use Business Analytics in place of Business Intelligence. This reflects the change in terminology since the second edition: Business Intelligence today refers mainly to reporting and data visualization (“what is happening now”), while Business Analytics has taken over the “advanced analytics,” which include predictive analytics and data mining. In this new edition, we therefore use the updated terms.
This R edition includes the material that was recently added in the third edition of the original (XLMiner-based) book:
• Social network analysis
• Text mining
• Ensembles
• Uplift modeling
• Collaborative filtering
Since the appearance of the (XLMiner-based) second edition, the landscape of the courses using the textbook has greatly expanded: whereas initially, the book was used mainly in semester-long elective MBA-level courses, it is now used in a variety of courses in Business Analytics degrees and certificate programs, ranging from undergraduate programs, to post-graduate and executive education programs. Courses in such programs also vary in their duration and coverage. In many cases, this textbook is used across multiple courses. The book is designed to continue supporting the general “Predictive Analytics” or “Data Mining” course as well as supporting a set of courses in dedicated business analytics programs.
A general “Business Analytics,” “Predictive Analytics,” or “Data Mining” course, common in MBA and undergraduate programs as a one-semester elective, would cover Parts I–III, and choose a subset of methods from Parts IV and V. Instructors can choose to use cases as team assignments, class discussions, or projects. For a two-semester course, Part VI might be considered, and we
recommend introducing the new Part VII (Data Analytics).