Business Analytics

Book in Great Condition, Arrived Early

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.

The book is good, but you really have to download the code. The author skips big blocks of code in the written text, so you can not follow along, entering R commands, by reading the book in isolation.The author in many cases offers sparse explanation for the technique and the analysis he offers is quite curt as well.Overall, it is a good book if you know stats, know R, and have a lot of time to study the book in conjunction with his notes. It is extremely tedious, but then again, consider the subject matter.Machine Learning by Lantz suffers from none of the issues I point out here and I would recommend starting with that first and then follow on to this.

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.

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.

Difficile trovare un libro sull'argomento del data mining e analytics che sappia conciliare la teoria con la pratica. Questo libro rappresenta un buon esempio di quanto, esponendo in modo chiaro ed efficace la teoria e, a seguire, esempi di applicazione con codice in linguaggio R. Peccato che l'autore non abbia avuto modo di esporre ogni metodo con la stessa valida efficacia, per cui la qualità di esposizione di alcuni metodi non e' allo stesso livello di profondità di altri (es. regressione vs. SVM). Libro valido anche per la quantità di metodi di data mining esposti, anche se alcuni argomenti meriterebbero ancora maggiore dettaglio.

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.

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.

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

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.