Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), by Daniel T. Larose, Chantal D. Larose
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Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), by Daniel T. Larose, Chantal D. Larose
Best Ebook Online Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), by Daniel T. Larose, Chantal D. Larose
Learn methods of data analysis and their application to real-world data setsThis updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets.Data Mining and Predictive Analytics, Second Edition:
- Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language
- Features over 750 chapter exercises, allowing readers to assess their understanding of the new material
- Provides a detailed case study that brings together the lessons learned in the book
- Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content
Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), by Daniel T. Larose, Chantal D. Larose- Amazon Sales Rank: #605626 in Books
- Brand: Larose, Daniel T./ Larose, Chantal D.
- Published on: 2015-03-16
- Original language: English
- Number of items: 1
- Dimensions: 9.55" h x 1.90" w x 6.40" l, .0 pounds
- Binding: Hardcover
- 824 pages
From the Back Cover
Learn methods of data analysis and their application to real-world data setsThis updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets.Data Mining and Predictive Analytics:
- Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language
- Features over 750 chapter exercises, allowing readers to assess their understanding of the new material
- Provides a detailed case study that brings together the lessons learned in the book
- Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content
Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.
About the Author Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. He has published several books, including Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Wiley, 2007) and Discovering Knowledge in Data: An Introduction to Data Mining (Wiley, 2005). In addition to his scholarly work, Dr. Larose is a consultant in data mining and statistical analysis working with many high profile clients, including Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc.Chantal D. Larose is a Ph.D. candidate in Statistics at the University of Connecticut. Her research focuses on the imputation of missing data and model-based clustering. She has taught undergraduate statistics since 2011, and is a statistical consultant for DataMiningConsultant.com, LLC.
Where to Download Data Mining and Predictive Analytics (Wiley Series on Methods and Applications in Data Mining), by Daniel T. Larose, Chantal D. Larose
Most helpful customer reviews
5 of 6 people found the following review helpful. A must have companion for every data miner By ns The best way to get into data mining profession is to have the right education of it first. Data Mining and Predictive Analytics (DMPA) does the job very well by getting you into data mining learning mode with ease.This book has been a big help for me so far. Even though several key area of data mining is math and statistics dependent, this book helped me get into refresher mode and get going with my data mining classes. The biggest plus of this book is its very practical way of explaining all the key concepts of data mining and then it pushes you to do the real life data/statistical analysis with the help of their companion website dataminingconsultant . com . The R Zones are very well explained.Every newcomer to this subject should first go through the Appendix A which is about Data Summarization and Visualizations. In this section Authors explains about the basic concepts of statistics needed to understand the critical concepts of data mining and predictive analytics. I would suggest reading the Appendix A first to whoever picks this book.A very good book.
2 of 2 people found the following review helpful. Exceptional textbook By Diverse Interests Bought this text for a class. I wanted to be sure I was interested in the material and adequately prepared to do the work. I read a third of the book on my own. It is well written, technically thorough, mathematically understandable, and actually quite interesting. I marveled at the amount of material in it with only a single author. I just started the data mining course. I am happy I decided to take it.
2 of 2 people found the following review helpful. Great reference text for data mining and predictive analytics By PDX Reader A comprehensive and well-written presentation of data science principles and practice. The explanations are easy to understand and there are a lot of examples. The only drawback is that while there is some guidance at the end of each chapter in applying the concepts in R, there is not nearly enough and the examples are very specific with not much explanation.
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