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

Galit Shmueli (Author), Nitin R. Patel (Author), Peter C. Bruce (Author)

Book Description

Publication Date: December 11, 2006 | ISBN-10: 0470084855 | ISBN-13: 978-0470084854 | Edition: 1

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence

In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.

Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.

Data Mining for Business Intelligence:

• Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
• Features a business decision-making context for these key methods
• Illustrates the application and interpretation of these methods using real business cases and data

This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

Editorial Reviews

Review

"Shmueli et al. have done a wonderful job in presenting the field of data mining…a welcome addition to the literature." (Computing Reviews.com, August 15, 2007)

"This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices." (IT Professional, January/February 2007)

"The book contains real case studies, providing yet further demonstrations of the extraordinary data wealth of the modern commercial world." (International Statistical Review, 2007)

"…full of vivid and thought-provoking anecdotes…needs to be read by anyone with a serious interest in research and marketing." (Research Magazine, August 2007)

From the Back Cover

Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence

In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models.

Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.

Data Mining for Business Intelligence:

• Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis
• Features a business decision-making context for these key methods
• Illustrates the application and interpretation of these methods using real business cases and data

This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

See all Editorial Reviews

Product Details

• Hardcover: 298 pages
• Publisher: Wiley-Interscience; 1 edition (December 11, 2006)
• Language: English
• ISBN-10: 0470084855
• ISBN-13: 978-0470084854
• Product Dimensions: 10.3 x 7.3 x 0.8 inches
• Shipping Weight: 1.5 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (10 customer reviews)
• Amazon Best Sellers Rank: #573,843 in Books (See Top 100 in Books)

More About the Author

Galit Shmueli is SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business. Dr. Shmueli's research focuses on statistical and data mining methods for modern data structures, with a focus on "statistical strategy" - issues related to how data analytics are used in scientific research. Her main field of application is information systems (in particular, electronic commerce).

Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature. She has authored over fifty journal articles and book chapters, has co-authored several books, and is on the editorial boards of several journals. She presents her work nationally and internationally.

After receiving her PhD in Statistics from the Israel Institute of Technology in 2000, Dr. Shmueli was visiting faculty at Carnegie Mellon University's statistics department, where she became involved in the early research in biosurveillance. After moving to the University of Maryland, she initiated with Dr. Jank a new research field on the interface of statistics and information systems called "statistical methods in eCommerce". This now highly active interdisciplinary field has generated important advancements in empirical eCommerce research.

Dr. Shmueli is passionate about teaching statistics and data mining and improving their application in the business environment. Her recent co-authored textbook "Data Mining for Business Intelligence" is part of her effort to create analytically-savvy MBAs.

Customer Reviews

Most Helpful Customer Reviews

28 of 31 people found the following review helpful:

1.0 out of 5 stars Useless, July 20, 2009

By 

lew "lwndw123" (Connecticut, USA) - See all my reviews

This review is from: Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Hardcover)

Pretty shallow presentation of very basic statistical algorithms. Algorithms presented without any explanation of mathematical backgrounds and assumptions when they can be used. Almost no formulas in text, just data and plots. These algorithms are standard statistical algorithms; it is not clear from the text whether "data mining" and "statistics" is the same or not.

What is worse, there is Excel library that must be used with the book. All examples are in the context of this library. LICENSE LASTS ONLY 6 MONTH. Means, after 6 month you can put quite expensive ($100) book in trash. Or spend few thousand bucks for full license, what taking into account the "sophistication" of the library would be wasting of money. Take R language that is free and infinitely more powerful.

Warning if you purchase used book: each copy of the book has unique license ID that must be used to download the software. Once downloaded, this software cannot be downoaded second time. This means that it is not possible to download software if you purchased used book

12 of 15 people found the following review helpful:

5.0 out of 5 stars An Excellent Introduction, Works with Excel, March 18, 2007

By 

John Matlock "Gunny" (Winnemucca, NV) - See all my reviews
(REAL NAME)   

This review is from: Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Hardcover)

Data mining is the extraction of useful information from large amounts of data. Perhaps the best example of this is Amazon. If you go to Amazon to look at a book, you'll find such tidbits of information as a section on the page headlined 'Customers who bought this item also bought' and another 'What do customers ultimately buy after viewing this item?'

That's datamining, dozens or hundreds, or thousands of people looked at the page about this item. Then they went on to take these other actions. Among all the data that Amazon has collected they mine their database and pull out information to fill in these blocks.

This book, intended for MBA level students gives an excellent introduction to data mining. It further includes access to an Excel add-in called XLMiner that is specifically set up to allow the student to use Excel to learn how data mining is done.

The one thing I would ask the authors to do in their next edition is to provide a brief review of the commercially available data mining software products that are available. If not all of the software, perhaps just the top half dozen or so. In real life we aren't going to use Excel for data mining, our data resides in a database somewhere.

8 of 10 people found the following review helpful:

5.0 out of 5 stars Excellent MBA/B-School Data Mining Book, January 15, 2008

By 

Ravi Bapna (Hyderabad, India) - See all my reviews
(REAL NAME)   

This review is from: Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner (Hardcover)

I've used this as textbook for three years (even before it was in print) for my "Business Intelligence Using Data Mining" elective MBA course at the Indian School of Business. Till last Fall, I used to structure my class around the four major data-mining techniques explained well in this book; classification, prediction, clustering and association rules (what goes with what). The last time I switched completely to driving the class using the six or seven excellent cases at the back of the book, and the Business students loved that.

The cases and the associated data are rich; providing a business context to anchor the learning for students in the B-School. They allow the instructor to naturally cover important practical issues, such as over-sampling (when events that one is interested in -- say load defaults -- are rare), and asymmetric classification costs.

My class typically has a group project, where students have to pull everything together, from identifying a data mining opportunity, to collecting the data (beg, borrow or crawl:-), to performing exploratory data analysis (a key chapter in the book), to analyzing and presenting the results. Its usually more work than the students expect, but also typically much more learning than they expect.

In summary, a great resource for teaching the principles of data mining to anyone, and particularly useful for those in a Business School setting.