- Hardcover: 312 pages
- Publisher: Springer; 2013 edition (September 14, 2012)
- Language: English
- ISBN-10: 1461443423
- ISBN-13: 978-1461443421
- Product Dimensions: 6.4 x 0.9 x 9.2 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 7 customer reviews
- Amazon Best Sellers Rank: #918,881 in Books (See Top 100 in Books)
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R for Business Analytics 2013th Edition
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From the book reviews:
“This book focuses on how to use R software for basic statistics in a business context. … The use of GUIs in this book exposes the reader to some of the power of R for business planning and decision making in a user-friendly environment. … The content of this book is light from a statistical point of view, but does serve to provide a nice overview of GUIs that are helpful for anyone doing business analytics in R.” (Roger M. Sauter, Technometrics, Vol. 55 (3), August, 2013)
“I am enjoying reading this book. … After reading this book I feel more confident about getting data into R. … Each chapter has a summary at the end listing all the packages and functions used in the chapter. … a very useful book on business analytics.” (Cats and Dogs with Data, maryannedata.wordpress.com, August, 2013)
"If you are a beginner like me and want to learn everything about R and don’t know where to begin, then this book will do you wonders. It has everything you need to get started. This book will be your companion and will not disappoint you." (The R Blabber, May, 2013)
“The book has something for both beginning R users (who may be experienced in data science, but want to start learning how to apply R towards their field), and experienced R users … . the book has an extremely broad coverage of R’s many packages that can be used towards business data analysis, with a very hands on approach that can help many new users quickly come up to speed and running on utilizing R’s powerful capabilities.” (Intelligent Trading Tech, October, 2012)
From the Back Cover
R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.
This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.
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Top customer reviews
I was impressed with the book's clear organization, and the huge number of citations and reference lists.
I was concerned when I read the other reviews. But my experience has been different. Is this book going to teach you Business Analytics, or statistical theory - No. But I don't think that is not the author's intention. Does it provide a good overview of R - Yes. Does it take the reader step-by-step through a complete project from start to finish - No. Does it provide a good overview of R, and step-by-step guides for how to do various analytic tasks - Yes.
I found the data graphics chapter (chapter 5) quite strong, giving me new ideas for graph types that I hadn't previously considered.
This book clearly assumes that the reader knows statistics. But if one knows that when buying the book, then it is fine. E.g., the chatter on cluster analysis just jumps in and talks about the various ways to do a cluster analysis in R. It does not talk about what cluster analysis is, or what the business situations are in which one would want to do cluster analysis.
I think this book is great for:
1) Readers who have familiarity with Business Analytics, and want to transition from using another tool to using R.
2) New R users (who have statistical knowledge) who want an overview of R, with examples and guidance on the pros/cons of various options (e.g., GUIs, code editors, etc.), as well as lots of external references.
3) Current R users who are looking for new ideas to get the most out of R.
I think this book is not the right book for you if:
1) You want to learn statistics.
2) You have no background in business analytics
...But I think there are other books, online classes, and university courses that teach statistics and business analytics.
Lastly, I like the author's inclusion of interviews with a wide variety of industry people, R experts, and analytic software vendors. These interviews provide a nice variety of viewpoints and wide set of examples of how people have used R.
And, as I mentioned at the beginning -- the book contains a huge number of citations and reference lists. Many with lengthy code examples.
Overall, a very useful reference book, and a great introduction to R for people with existing statistical knowledge.
However, as a text for learning R and business analytics, it reads as if the author had kept a scrapbook of notes over the last 10 years and simply copy-pasted across. There is little coherency in the book. The underlying analytics is barely explained, code examples reference data sets to which the reader has no access and code is listed with almost no explanation.
I gave up reading after the following example. The author is demonstrating a function called from the command line. The function call and the result are displayed in the text. However, the function is incorrectly called so that the result is a command line error. The function call and the returned error are listed in the example. There is no suggestion that the example is supposed to demonstrate an error. It's simply lazy editing.
It's a great shame because the author clearly has extensive experience in the book subject area. Unfortunately this experience does not extend to writing books.
The majority of the book's discussion on actual data mining is focused on how you can use GUI tools that sit on top of R so that you can hit some buttons to do a one line command... There is nothing advanced in this book. Section 6.4.3 Marketing Propensity Models - sounds cool, right? "Marketing propensity models are a major use of regression models. They are used in multiple domains for marketing including web analytics, telecom, direct marketing, and marketing campaigns". That. Is. It.
The discussion on business analysis is so light that it's practically not there. There are six pages that show frameworks for analytical approaches but there is zero discussion on them.
I would not recommend this book to anyone.
The few redeeming qualities of the book:
- Brief survey of plots. A few I hadn't seen before.
- A few interesting interviews (Hadley Wickham, Oracle R, R Studio Creator)
A better alternative would be to read Machine Learning With R. It teaches you R (not through a GUI), but also grounds you in an analysis framework (thinking about the problem, training, testing, and improving your models).
Machine Learning with R - Second Edition