Data Mining and Business Analytics with R

This is a timely and excellent book. Its greatest strength lies in the carefully presented statistical models coupled with diverse and interesting real-world examples. Ledolter effectively sets the stage in Chapter 1 for what is to follow by explaining the difference between traditional statistics applications and the problems for which data mining techniques are necessary. He highlights the nature of data mining problems and describes the techniques for addressing them that are discussed in subsequent chapters. The early chapters review traditional regression and logistic regression models with applications. Then the book moves quickly to lesser known techniques that are particularly useful for dealing with large data sets. These methods include nearest neighbor analysis, Bayesian analysis, regression and classification trees, clustering, and market basket analysis. The book ends with a comprehensive set of exercises. The last eight of the exercises are particularly valuable because they provide detailed worked examples and in a number of cases include alternative statistical approaches to the same problem. All of the final exercises are tied to the book's chapters, while all examples and exercises make use of the powerful and free R Statistical Software. The complete R code is available on the book and author websites.

Back in school and going after a second Master's degree now. Needed this book for the class material. I find the class enjoyable enough, and this book is definitely helping me understand the class concepts.

Book in Great Condition, Arrived Early

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.

This graduate-level textbook gives students very good exposure to the use of open-source statistical software R in data analysis, data exploration, and data model construction. Readers must already know some R basics (e.g., how to install R packages, read help files for packages and functions, and work with basic R data structures such as data frames, etc.) and statistical concepts such as hypothesis testing, significance levels, etc.The book chapters are organized mostly around statistical techniques such as linear regression, clustering, text analysis and social network analysis. Each chapter usually begins with a discussion of concepts important to understanding the statistical technique in question, followed by descriptions of the datasets and R packages to be used in the hands-on problem-solving exercises. By following along, readers will acquire useful knowledge on what data modeling problem(s) a particular statistical technique can be applied to, what pitfalls (e.g., overfitting) to avoid, how to utilize the covered R packages and use the provided code examples as templates for studying similar problems.The book has an "applied" emphasis -- discussions of the mathematical details underpinning a statistical technique are kept to a minimum and to a relatively high, conceptual, and practical level. The datasets are quite varied, covering a wide range of domains relevant to the fields of engineering, business and marketing, economics, and health care.All datasets and code examples are available for download from websites mentioned in the book. The code examples have comments, but there is room for improvement (for example, readers who are relative R novices may not know why a call to set.seed(x) is required before calling some specific R functions). A similar observation can be made regarding the graphics presented in the book: providing figure captions and, in some cases, better x- and y-axis labeling (for example, instead of just labeling the x-axis with a 0 and a 1, use labels that indicate what the 0 and the 1 represent or mean), in my opinion, could help enhance the reading experience.Overall, however, I thought the book is written at a level that its intended audience will find accessible.