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Data Mining and Business Analytics with R [Kindle Edition]

Johannes Ledolter

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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)


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.


  • Format: Kindle Edition
  • Dateigröße: 6337 KB
  • Seitenzahl der Print-Ausgabe: 368 Seiten
  • Verlag: Wiley; Auflage: 1 (28. Mai 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Amazon Bestseller-Rang: #416.846 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

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Die hilfreichsten Kundenrezensionen auf (beta) 4.1 von 5 Sternen  17 Rezensionen
4 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Review for Data Mining and Business Analytics with R 22. Dezember 2013
Von Bovas Abraham - Veröffentlicht auf
Format:Gebundene Ausgabe
This is an excellent book which is very accessible to readers in several fields. It gives a very good summary of different statistical techniques which are used for data mining. It also gives some good large data sets and show how the tools can be implemented. It begins with a chapter on summarizing the data to have an initial feel about the data. Then it gives discussion on regression (linear, polynomial, nonparametric). Then the book goes into techniques such as LASSO, logistic regression, classification, nearest neighbour analysis, decision trees, clustering, dimension reduction with principle components and partial least squares etc. These are all illustrated by examples with the help of the software R which is freely available on the internet. The book also presents, binary classification and multinomial logistic regression. It has a chapter on text mining as well. The last chapter discusses two examples of network data.
The book is well written with an applied audience in mind and hence details are avoided to focus on the techniques which are explained well. The examples are well chosen and illustrates the techniques very well. The data sets and the R code for all examples are on a webpage accompanying the book. Exercises and several large practice data sets are given at the end of the book. These are good resources for applying the techniques and getting practice.
Overall the book is very good for practitioners in diverse fields such as business, marketing, social sciences, and engineering.
It can also be used as a text in Management programmes, Applied Statistics etc. I recommend it highly.
6 von 7 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Contains discussion and practice and references elsewhere for details 15. April 2014
Von Richard C. Yeh - Veröffentlicht auf
Format:Gebundene Ausgabe|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
This is meant to be a practical book. The author's "objective is to provide a thorough discussion of the most useful data-mining tools that goes beyond the typical 'black box' description, and to show why these tools work". I think the result of reading and doing the exercises in this book is:

1. I will have acquired some familiarity with regression techniques and a few of the problems they can help with
2. I will have performed the regression techniques in R

Over half the text focuses on various kinds of regression. Then there is a little bit on classification, decision trees, clustering, principal components analysis.

However, I also think:

1. The math is so fast --- mostly one definition after another --- that its inclusion is superfluous. If you know what the equations are, there is no need to see them here. If you don't, this presentation isn't a good way to learn them. These sections often say to check out the author's 2006 text "Introduction to Regression Modeling" for more details.

2. The mentions of alternate software (Minitab, SAS, SPSS) are useless throwaways and should either be removed or expanded. Who cares if I have two brands of calculators that give me the same answer for 3 + 4? Likewise, there is no need to say that R gives the same answer as Minitab in a single example (pp. 88-92) or that some feature exists in SAS and SPSS (CHAID, p. 186).

3. The exercises are extremely important for practicing.

4. Examples sometimes have long program output. In my experience, it takes some practice to read the program output and understand what each number means, and this discussion is not really done in the text.

From a statistics perspective, I would instead recommend Tibshirani, Hastie, Friedman: "Elements of Statistical Learning". For machine learning techniques, Segaran: "Programming Collective Intelligence".
2 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A solid, readable book on data analytics, with some business applicaitons 2. April 2014
Von Ian Kaplan - Veröffentlicht auf
Format:Gebundene Ausgabe|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
When I saw the title "Business Analytics" I thought that this might be a book that targeted MBA students who are uncomfortable with graduate level applied math and statistics. Books that follow this pattern provide a cook book approach to packages that do linear regression or clustering, without providing much background. The virtue of these books is that they tend to be more readable than book like The Elements of Statistical Learning by Hastie et al. Elements of Statistical Learning is a classic but it covers complex topics in a few paragraphs or a page. There are parts of this book that I have read again and again before fully understanding the material.

Data Mining with Business Analytics is a much gentler introduction to many of the topics in "Elements" (statistical analysis, linear models and clustering, among other topics). Johannes Ledolter writes clearly and walks the fine line of discussing the mathematical background without providing a deep discussion of the mathematics.

As the title suggests, the examples are in the R statistical language. I have been using R for several years and have become an R fanboy. I see R as an indispensable platform for doing data analysis. The R examples are generally well developed. R includes a number of data sets and many authors use these data sets to illustrate analytic techniques. I am starting to feel that the prostate cancer data set, which is used in this book, is getting a bit old and I propose a moratorium on its use.

When I studied linear modeling we covered a lot of the mathematical formalism and proofs that this book leaves out. While this did give me a deeper understanding of linear modeling, the cost was some topics, like logistic regression were omitted. This book has a good chapter on logistic regression.

Logistic regression is probably the most popular way to analytically do credit analysis. The logistic regression chapter includes examples of logistic regression applied to lending and credit.

The book does not "talk down" to the reader. A basic background in statistics is assumed. One of my professors said once that "MBA students don't like linear algebra" and I found it interesting that topics like linear regression were presented without linear algebra (e.g., as finite math using summations).

The book provides a solid introduction on the techniques and their implementation in R. For anyone using these techniques this will serve as a starting point. For example, the discussion of K-nearest neighbors clustering gives the reader a feel for clustering. For many applications, clustering is more complicated and there are books on this topic.

The prices of math and programming books can be hard to swallow. I think that most readers who want an a solid overview of data mining will find that this books does pay back its substantial cost.
1 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Learning by example (if you already know basic R) 29. März 2014
Von I Teach Typing - Veröffentlicht auf
Format:Gebundene Ausgabe|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
While this book is expensive, if you know some R already and you are looking for more examples for covering statistical learning/modeling methods, with sample code, this is an excellent buy. If you don't know R you will need to spend a bunch of time getting up to speed before you hit this book.

Even though it is targeted at the business world the examples and code are widely applicable. From the table of contents and index you can get a feel for the topics covered. If you want to see the actual code used check the books website. It is solid and includes the code, the data sets and an errata. If you are curious to know what R libraries are touched, they include: arules, car, class, cluster, elipse, igraph, lars, lattice, leaps, locfit, MASS, mixOmics, mixtools, nutsheell, ROCR, startnet, textir, tree and VGAM.

This is a pretty book with great well annotated graphics to help you learn. The writing is pleasantly clear and direct without being too terse. While the code could be better commented (for the R novices) in general it is good and the text which surrounds the code is very good.

There are formulas here. The math complements the writing rather than being a deep dive.

The references to outside work are on target but the author does not include some obvious choices like An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) for a deeper look at the math or Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) for additional examples.

Overall, this is an excellent book for someone who knows basic statistics and the fundamentals of R and who wants to learn modern methods using examples.
1 von 1 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen great book, but definitely is advanced 18. Mai 2014
Von Amazon Customer - Veröffentlicht auf
Format:Gebundene Ausgabe|Vine Kundenrezension eines kostenfreien Produkts (Was ist das?)
there are many data mining books out there that start from a general beginning perspective and work you up, and those that start at an advanced level. this is definitely the latter. it presupposes a sophisticated math background and proceeds from there. if you fit that category, you'll be highly pleased with the book. if you do not have the background, find a different book and look forward to reading this later.
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