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Data Mining and Business Analytics with R Hardcover – 28 Jun 2013

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Product details

  • Hardcover: 368 pages
  • Publisher: Wiley-Blackwell; 1 edition (28 Jun. 2013)
  • Language: English
  • ISBN-10: 111844714X
  • ISBN-13: 978-1118447147
  • Product Dimensions: 16.5 x 2.3 x 24.4 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (10 customer reviews)
  • Amazon Bestsellers Rank: 868,773 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

Review

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.

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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
I am finding this book extremely useful. I am not using in for academic purposes, rather to improve some logit modelling I already do. I have only currently read only the chapters which are relevant to what I do but have picked up some good ideas just upon a first reading. I don't have any problem with data mining for instance so could happily ignore that. The more advanced chapters wait until I have completed the initial work.

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.
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
This book aims to be graduate-level, and in terms of the explanations given for the various concepts the level of statistical understanding assumed is consistent with that. Having said that, someone with less of a solid mathematical background could potentially read through the overview of various concepts and follow the examples to get a working knowledge of how to create a range of plots of data and perform classification, dimension reduction, network analysis and more.
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.
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Format: Hardcover Vine Customer Review of Free Product ( What's this? )
There are many groups of people who might choose to be interested in R Statistical software for different reasons: statisticians, information designers, business analysts, students and specialist researchers. My own interest was sparked by a search for a way of displaying charts in a much more flexible way than was possible with Excel.

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
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By Andrew Dalby VINE VOICE on 23 Nov. 2013
Format: Hardcover Vine Customer Review of Free Product ( What's this? )
I have been a user of the R statistics program for a number of years. One of the problems of R is that it isn't very easy to learn and it does not have an effective user interface like SPSS or Excel and so getting data in has been the problem. Once you have data there then R has a huge wealth of statistical analysis techniques that you can use. The challenge then is to find the right one for you.

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.
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