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Data Mining and Business Analytics with R Hardcover – 28 Jun 2013
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I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business–oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text. (Journal of the American Statistical Association, 1 January 2014)
From the Back Cover
Showcases R′s critical role in the world of business
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible robust computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high–dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.
Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty–based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:
- A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools
- Illustrations of how to use the outlined concepts in real–world situations
- Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials
- Numerous exercises to help readers with computing skills and deepen their understanding of the material
Data Mining and Business Analytics with R is an excellent graduate–level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.See all Product Description
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Top Customer Reviews
The book is intimately bound with the "R" software which is free, and all the data sets and code in the book are available online. I am currently working my way through the examples in the book. I suspect I will get more out of the book with a more methodical approach understanding each step along the way than cherry picking a few bits and pieces that I can grasp without. It is very much a "hands on" book with worked examples using current available amd free to download software.
Unfortunately I have a deadline in which to review this book and there are many hours of work ahead of me before I can report the true benefit of the book to me. However it at this stage it looks like exactly what I want to improve and understand my models better.
To be clear on what it doesn't cover - this is not an introductory text for R - the examples assume a working knowledge of R, so without this they may be hard to follow. Sophisticated terms are regularly used without explanation, so you may also (like myself) find yourself having to look up the occasional term. It also doesn't try to cover things like bias as data collection in general is outside the scope of the book - it assumes suitable data for analysis already exists.
The book is concise (to the point of losing context and clarity on occasion), meaning that despite being only a few hundred pages or so, it covers a significant range of techniques, both in outlining the theory and mathematical basis and in worked examples. These examples show graphical and text outputs and account for possibly half of the book, but despite this it is hard to say that it's overdoing this because it keeps the scripts as brief as possible while illustrating practical use, and this includes loading available datasets, performing analysis and showing useful outputs, whether graphical or text. Recommended if you're up for a challenge and feel you can follow sophisticated concepts.
R fans should consider carefully whether Johannes Ledolter's book is worth the cost and shelf space versus other printed and online sources. The good news is that it is full of illustrated case studies and R code samples which could help readers see the potential of the tool and which could serve as a starting point if they happen to be working on a similar piece of analysis. Those who are looking for an explanation that that does not assume a pre-existing knowledge of the statistical techniques used - or those who are interested in the visual questions of how best to lay out the data - will be disappointed however. The text is a rather meandering in nature, there is never really a "basics" section and some of the examples are neither beautiful or clear. It is not a book for even the geekiest person's coffee table.
The book appears to be most clearly aimed at readers studying for quantitative modules of an MBA that involve data mining techniques. For them it could be a good choice. Others might want to look at alternative books and tutorials
This is where this book is invaluable in showing you how to use R for business analytics. It gives you all of the methods and the techniques that you should apply to your data. Hopefully this will encourage more users from a wider range of subjects to use R. By giving "recipes" for data analysis this book saves you the time and difficulty of going through the somewhat impenetrable online documentation. By using an IDE like R-studio it is also becoming much easier to manage the packages and the command line interface. So I think that this is an invaluable resource for anyone who wants to use R for their analytics, but it is not a general text on analytics themselves.
Most Recent Customer Reviews
As a business intelligence consultant I use this book often, and it has great ideas for further analysis of your data.Published on 18 Jun. 2014 by Alexander Jacobsen
This feels like a college text book and if you aren't doing the course it is a struggle. The writing style is dense and the material assumes a lot of knowledge of the field. Read morePublished on 15 Jan. 2014 by A. J. Gauld
I'm still working through this book but have found it to assume prior knowledge of R. If you don't have any it night not give enough information to be useful. Read morePublished on 15 Dec. 2013 by Reader
My main gripe/concern with this book is the title. In particular one word. The word 'with'. See, the problem is that no matter which way I look at it, 'with' should say 'in'. Read morePublished on 5 Dec. 2013 by r2uzenblot
This is a book with a lot of content and so I haven't read it all. Instead I've sampled some chapters that looked interesting. This is what I've noticed.
It's in colour! Read more