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Data Mining and Business Analytics with R, by Johannes Ledolter
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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.
- Sales Rank: #261613 in Books
- Brand: Ledolter, Johannes
- Published on: 2013-05-28
- Original language: English
- Number of items: 1
- Dimensions: 9.60" h x .92" w x 6.50" l, 1.63 pounds
- Binding: Hardcover
- 368 pages
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.
About the Author
JOHANNES LEDOLTER, PhD, is Professor in both the Department of Management Sciences and the Department of Statistics and Actuarial Science at the University of Iowa. He is a Fellow of the American Statistical Association and the American Society for Quality, and an Elected Member of the International Statistical Institute. Dr. Ledolter is the coauthor of Statistical Methods for Forecasting, Achieving Quality Through Continual Improvement, and Statistical Quality Control: Strategies and Tools for Continual Improvement, all published by Wiley.
Most helpful customer reviews
11 of 13 people found the following review helpful.
Contains discussion and practice and references elsewhere for details
By Richard C. Yeh
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: The Elements of Statistical Learning. For machine learning techniques, Segaran: Programming Collective Intelligence.
5 of 6 people found the following review helpful.
Review for Data Mining and Business Analytics with R
By Bovas Abraham
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.
2 of 2 people found the following review helpful.
Learning by example (if you already know basic R)
By I Teach Typing
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.
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