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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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From the Back Cover
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." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization "…extremely well organized, clearly written and introduces all of the basic ideas quite well." – Robert L. Phillips, Professor of Professional Practice, Columbia Business School Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft® Office Excel® with XLMiner®, Third Edition presents 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: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides Data Mining for Business Analytics: Concepts, Techniques, and Applications in Microsoft® Office Excel® with XLMiner®, Third Edition 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. 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. 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 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, 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.
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About the Author
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. 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 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, 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.
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Product details
Hardcover: 560 pages
Publisher: Wiley; 3 edition (April 18, 2016)
Language: English
ISBN-10: 1118729277
ISBN-13: 978-1118729274
Product Dimensions:
7.3 x 1.4 x 10.2 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
3.3 out of 5 stars
30 customer reviews
Amazon Best Sellers Rank:
#21,388 in Books (See Top 100 in Books)
It's okay. You have to realize its written to support commercial software. It was required reading for a class, and I don't think I could have followed the concepts without an instructors help. Its written in such a way you would think the author(s) were really trying to impress themselves with their vocabulary, and how complex they could make trivial tasks seem.
The subject of the book I'm very interested in and have read quite a bit of information on the topic, but I can safely say this is probably the worst thing I've read in the genre. The language is convoluted and the core examples aren't great. I had to reread everything multiple times to understand what was being said, even with their examples. I wouldn't recommend this book to anyone.
It's easy enough to skim through this book and use it as a guide to the stats functions of the software, but if you want to take a deeper dive into the subject matter, the information is there for you as well. There is really no "fluff" in this book, and I'll be keeping it as a reference.
I'm using this book for class and honestly the examples are okay, but they don't do a good job of explaining how to execute them in excel using xl miner.
I used the book as a textbook for my class "Data Mining for Business" and I am very satisfied. The examples are rich, concepts are explained in a non-technical way and are easy to follow. For the class, I used R instead of XLMiner without any problems for the students and teaching process.
This book makes data mining much easier to comprehend. The examples are thorough and the writing style is simple to understand such a difficult topic. Would recommend!
Excellent book to whom uses XLMiner software. The author explains in a easy manner what are the main features employed by the Industry in Data Mining using XLMiner as add in in Excel spreadsheet. Highly recommended book to whom wants to use XLMiner.However I would recommend the author to include more examples in the use of the add-in.
Covers more stuff than a similar book called "Data Mining for Business Intelligence ...". Slightly more technical, but still focuses on qualitative traits of common DM techniques. Statistics and implementation details are not the emphasis of the book.
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