Free Ebook Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

Free Ebook Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

In checking out Data Mining For Business Analytics: Concepts, Techniques, And Applications In R, now you could not also do traditionally. In this modern-day period, gizmo and also computer system will assist you so much. This is the moment for you to open the gizmo and also stay in this website. It is the ideal doing. You can see the connect to download this Data Mining For Business Analytics: Concepts, Techniques, And Applications In R here, can't you? Simply click the web link and also negotiate to download it. You could reach purchase the book Data Mining For Business Analytics: Concepts, Techniques, And Applications In R by on-line and also ready to download and install. It is very different with the typical method by gong to guide store around your city.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

Free Ebook Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

We could not have the ability to make you enjoy analysis, however Data Mining For Business Analytics: Concepts, Techniques, And Applications In R will certainly lead you to like reading starting from now. Book is the home window to open the brand-new globe. The world that you want remains in the far better stage as well as degree. Globe will always guide you to also the prestige phase of the life. You know, this is a few of how reading will certainly give you the kindness. In this situation, more books you learn more expertise you understand, however it could suggest additionally the bore is complete.

Leisure time ends up being an extremely priceless time for many people. This is the moment to shed all exhausted, tired, and also tired works or obligations. However, having as well long period of time will make you feel bored. Additionally, you will certainly feel that so when you have no activities. To deal with the tiny trouble, we reveal a publication Data Mining For Business Analytics: Concepts, Techniques, And Applications In R that can be a method to accompany you while remaining in the spare time. It can be reading material, not as the cushion naturally.

You can like to this publication since it is simple points to get rid of. It suggests that words and language to make use of in this Data Mining For Business Analytics: Concepts, Techniques, And Applications In R come in simpleness. This possible book will assist you quickly to earn far better idea of brand-new thought and upgraded information. When you truly want to get this book, juts discover it in this website. We will certainly assist you to check out the book link and afterwards get it as yours. This does not imply to overwhelm you to be in difficult situation.

Yet, the visibility of this publication comes with the means just how you really require the far better choice of the brand-new updates. This is exactly what to recommend for you in order to obtain the possibilities of making or developing brand-new publication. When Data Mining For Business Analytics: Concepts, Techniques, And Applications In R becomes one that is prominent today, you must be one part of such many individuals that constantly read this book and also get this as their best friend.

Review

"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject." Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Read more

From the Back Cover

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and governmentUpdates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their studentsMore than a dozen case studies demonstrating applications for the data mining techniques describedEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presentedA companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.comData Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Read more

See all Editorial Reviews

Product details

Hardcover: 574 pages

Publisher: Wiley; 1 edition (September 5, 2017)

Language: English

ISBN-10: 1118879368

ISBN-13: 978-1118879368

Product Dimensions:

7.3 x 1.2 x 10.2 inches

Shipping Weight: 2.5 pounds (View shipping rates and policies)

Average Customer Review:

3.7 out of 5 stars

9 customer reviews

Amazon Best Sellers Rank:

#158,051 in Books (See Top 100 in Books)

There are a lot of good things about this new book. First, this is a very good authoring team. They have a deep (both correct and very up-to-date) understanding that spans the technical aspects of data analytics, and how and why analytics is used in business settings to make business oriented decisions. Second, the book is well crafted. It is very well organized with very clear and to-the-point explanations. Of the five authors of this book, three of them (Shmueli, Bruce and Patel) have been together as a text writing team since 2007, and this is the 5th time they have updated and revised their business analytics text based on new developments in the field, and based on ongoing feedback from faculty and students who have used the text in both academic courses and professional training courses. Third, because this team has been deeply involved in data analytics for decades, they have a good sense of perspective. They understand the history and evolution of models from the statistics community, as well as the history and evolution of models from computing communities spanning both data mining and machine learning. The lead author, Galit Shmueli, has been publishing in both the statistics literature as well as the management science literature on the differences between explanation (and inference) versus prediction. This depth of perspective, and technically correct understanding of these important nuances, are reflected in how the material in this book is organized and presented. Fourth, they provide a unified and coherent approach to understanding these models. They have major sections on classification models and prediction models. They also have sections on time series forecasting, association models, and clustering. In each of these sections on model and application specifics, they draw on the appropriate models from across the communities of statistics, data mining and machine learning. In addition, the book contains sections on the front end (data prep, exploration, and dimension reduction), as well as on the backend of the modelling process (model evaluation). All examples are worked out in R.The specific chapters on each data analytics modeling method are relatively short and to-the-point, as there are numerous textbooks and professional books on every one of the individual methods covered in this text. Because these authors are now in their fifth iteration of the content, and because they get a lot of feedback on what users of this material do and do not clearly understand, this authoring team has a knack for adding special explanatory material for those things that people tend to not understand well, or often misunderstand. While the chapters on each method are by design brief and introductory, they are solidly sufficient and highly informative, even for people with prior background in these methods. They have a good way of knowing what is important to explain, and a good way of explaining what they present.In short, this is an excellent introductory text and also serves as a very good reference text for the most up-to-date thinking on the the modeling that underpins business analytics.

This is one of a handful of books on ML and R, and it’s by far the simplest in its coverage of content. For instance, it’s the only one I’ve looked at so far that has explained the difference between the Naive Bayes and Exact Bayes algorithms, although I haven’t looked at the Manning books on ML (e.g., Practical Data Science with R) some of which use R and some Python (e.g., Real-World Machine Learning). By a kindergarten approach to ML, I don’t mean to be disrespectful or to minimize the contribution of the book—presenting a topic at a simple basic level is worthwhile. The reasons I downgraded the book slightly were the large number of errors in the book (which look like material from previous editions that wasn’t caught) and some carelessness in what is presented. For some reason, in the chapter entitled “Overview of the Data Mining Process,” it bothered me a lot that the authors detail creation of dummy variables but don’t present factor variables until halfway through the book. Perhaps that was just the desire to present things at a basic level, however. I did learn some R functions I wasn’t familiar with from this book, and it does provide clear, explicit explanations of ML (or data mining) techniques. (For the life of me, I still don’t have a clear understanding of the differences between ML, data science, data analytics, and data mining—I suspect it is how the techniques and data are used that determines the difference, because the same techniques are used in all of these disciplines.) Hope this review was useful.

This book is very accessible for business professionals who want to develop their skills in data mining. I like how the "R" codes are presented and the ease in how it can be reproduced. Furthermore, each chapter is a stand-alone topic and does not generally require a sequenced order of reading from start to finish.

Very book

This is a very useful book for R beginner!Item arrived fast at the beginning of the semester, the package is also protected very wellAppreciate!

There were a few chapters in which did not deepened in some parts of the subject and I had to find information and examples on the internet to understand it better.

Came to know that the book has several misprints only after purchasing it.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R PDF
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R EPub
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R Doc
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R iBooks
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R rtf
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R Mobipocket
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R Kindle

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R PDF

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R PDF

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R PDF

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R PDF