Data Mining (with Excel)

Science & Math

Galit Shmueli

Peter, Bruce

Mathematics

Thomson/Brooks/Cole

An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: <ul> <li>Real-world examples to build a theoretical and practical understanding of key data mining methods&nbsp;</li> <li>End-of-chapter exercises that help readers better understand the presented material</li> <li>Data-rich case studies to illustrate various applications of data mining techniques</li> <li>Completely new chapters on social network analysis and text mining</li> <li>A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides <a href="https://www.dataminingbook.com">https://www.dataminingbook.com</a></li> <li>Free 140-day license to use XLMiner&nbsp;for Education software</li> </ul> Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition&nbsp;is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "...full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."- Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." - ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience."&nbsp; - Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization 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, The 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.&nbsp; She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at <a href="http://www.statistics.com/">www.statistics.com</a>. 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, 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.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Xlminer

Nitin R. Patel

Peter C. Bruce

wiley

Data Mining for Business Analytics: Concepts, Techniques, and Applications with Xlminer

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an&nbsp; applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, 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® also includes: <ul> <li>Detailed summaries that supply an outline of key topics at the beginning of each chapter</li> <li>End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material</li> <li>Data-rich case studies to illustrate various applications of data mining techniques</li> <li>A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors <a href="http://www.dataminingbook.com/">www.dataminingbook.com</a></li> </ul> Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® 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.

Data Mining for Business Analytics: Concepts, Techniques, and Applications with Jmp Pro

Mia L. Stephens

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: <ul> <li>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</li> <li>Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students</li> <li>More than a dozen case studies demonstrating applications for the data mining techniques described</li> <li>End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented</li> <li>A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions&nbsp; <a href="http://www.dataminingbook.com/">www.dataminingbook.com</a></li> </ul> 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.

Data Mining for Business Intelligence: Concepts, Techniques, and Applications in R

John Wiley&Sons, Inc.

Data Mining for Business Analytics: Concepts, Techniques and Applications in Python

Peter Gedeck

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner

Amit V. Deokar

Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

This book arose out of a data mining course at MIT's Sloan School of Management. Preparation for the course revealed that there are a number of excellent books on the business context of data mining, but their coverage of the statistical and machine learning algorithms and theoretical underpinnings is not sufficiently detailed to provide a practical guide for users who possess the raw skills and tools to analyze data. This book is intended for the business student (and practitioner) of data mining techniques, and the goal is threefold: (1) to provide both a theoretical and practical understanding of the key methods of classification, prediction, reduction and exploration that are at the heart of data mining; (2) to provide a business decision-making context for these methods; and (3) using real business cases and data, to illustrate the application and interpretation of these methods. The book employs the use of an Excel add-in, XLMinerTM, at no cost to registered instructors, in order to illustrate and interpret the various data sets that are presented throughout. Real-life business cases are also presented so that readers can implement algorithms with a very low learning hurdle.Table of ContentsForewordPrefaceAcknowledgments1. Introduction2. Overview of the Data Mining Process3. Data Exploration and Dimension Reduction4. Evaluating Classification and Predictive Performance5. Multiple Linear Regression6. Three Simple Classification Methods7. Classification and Regression trees8. Logistic Regression9. Neural Nets10. Discriminant Analysis11. Association Rules12. Cluster Analysis13. CasesReferencesIndex

Wiley-India

Data Mining For Business Analytics, (an Indian Adaptation): Concepts, Techniques And Applications In Python

O.p. Wali

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Books by Galit Shmueli