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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, by Galit Shmueli, Peter C. Bruce, Mia L. Stephens
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro� presents an� 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:
- Detailed summaries that supply an outline of key topics at the beginning of each chapter
- End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material
- Data-rich case studies to illustrate various applications of data mining techniques
- A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors
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.
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, 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, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner�, Third Edition, also published by Wiley.
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 and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner �, Third Edition, both published by Wiley.
Mia Stephens is Academic Ambassador at JMP�, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, 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. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner�, Third Edition, also published by Wiley.
- Sales Rank: #108873 in Books
- Published on: 2016-06-13
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.20" w x 7.30" l, .0 pounds
- Binding: Hardcover
- 464 pages
From the Back Cover
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro� presents an� 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:
- Detailed summaries that supply an outline of key topics at the beginning of each chapter
- End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material
- Data-rich case studies to illustrate various applications of data mining techniques
- A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors
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.
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, 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, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner�, Third Edition, also published by Wiley.
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 and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner �, Third Edition, both published by Wiley.
Mia Stephens is Academic Ambassador at JMP�, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, 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. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner�, Third Edition, also published by Wiley.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Kindle version not up to par
By FutureDoc
I purchased the Kindle version of this book, only to find tables cut off on the right margin and many bitmapped equations that were very difficult to read. I returned it, as I expect to be able to actually view the books I purchase. This has been a recurring problem with many Kindle mathematics books. They never seem to be able to get it right, and they seldom offer a sample that is large enough to assess the quality issues before purchase.
I'm giving the book 5 stars, because this is not the fault of the authors and I didn't want to unfairly penalize the overall content ratings.
0 of 0 people found the following review helpful.
I wish this was edited for Kindle format.
By Joseph Mills
My biggest complaint about this book isn't so much the content, it is the publisher's poor ebook delivery. This book is not edited to take advantage of Kindle Textbooks. It does not contain page numbers, which makes it a challenge to reference when talking with other readers who have the physical book. It is difficult to navigate without page support as well. And in some cases tables and figures are truncated. Other books I have bought by this publisher do not suffer these problems, it does not make sense why this one does.
2 of 2 people found the following review helpful.
First-rate Textbook at a Reasonable Price
By Thomas Tiahrt
I have taught, and teach, a two-course data mining sequence for the MBA business analytics specialization at my university using Dr. Shmueli’s Data Mining for Business Analytics and Practical Time Series Forecasting textbooks. Both the supporting materials and the textbooks are high-quality products that match up well with the mathematical facility and technical skills of my students. The exposition is accessible to students with a wide range of backgrounds, and balanced between enough mathematics to explain the methods but not so much that it raises barriers to understanding. I looked far and wide for comparable texts, but I know of no other combination that meets the needs of my students as well as these two texts. I unhesitatingly recommend them both.
See all 4 customer reviews...
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