PDF Ebook Data Mining and Business Analytics with R

PDF Ebook Data Mining and Business Analytics with R

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Data Mining and Business Analytics with R

PDF Ebook Data Mining and Business Analytics with R

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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)

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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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Product details

Hardcover: 368 pages

Publisher: Wiley; 1 edition (May 28, 2013)

Language: English

ISBN-10: 9781118447147

ISBN-13: 978-1118447147

ASIN: 111844714X

Product Dimensions:

6.2 x 1 x 9.3 inches

Shipping Weight: 1.6 pounds (View shipping rates and policies)

Average Customer Review:

4.1 out of 5 stars

19 customer reviews

Amazon Best Sellers Rank:

#257,290 in Books (See Top 100 in Books)

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.

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.

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.

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.

Data Mining and Business Analytics with R by Johannes Ledolter is a practical and useful introduction to the broad and increasingly important topic of data analytics using the powerful statistical software,R. After recently taking a rigorous MITx EdX data analytics course, I found this book of particular value. The use of the open source statistical software,R, provides a powerful platform for the analysis of large data sets and having book which can take a novice through the steps using many detailed examples is valuable. While the book goes through the R code used in some detail, it would probably be of more value to the reader if they had already been exposed to introductory R.The book contains 19 chapters and covers the essential areas for getting a user up to speed using R for data analytics. Examples of major topics include: Linear Regression (including multivariate analysis); Logistics Regression; Binary Classification; Classification using a Nearest Neighbor (k-Nearest Neighbor Algorithm); Decision Trees; Clustering; and Text Analytics. The book includes a fair amount of the R code used in the examples and many different types of graphs which highlight some of the extensive graphing capabilities found in R. A website link is also provided, allowing the reader to download both the data and the associated R code to either go through the examples or reuse it for other data analytic projects.Overall, I recommend the book for those users who want to fine tune their data analytic capabilities using R.

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Data Mining and Business Analytics with R PDF