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Today we all have access to a lot of data. Even more crucially, we also have easy access, through our personal computers and powerful free software packages, to the means to process the corpus of data and extract intelligence from it. Quite needlessly though, the necessary knowledge skills remain the exclusive preserve of a few, which this book sets out to change.
Although most data analytics techniques have a mathematical basis, people with a grasp of high school mathematics can gain a deep intuitive understanding of the underlying techniques and apply them correctly and effectively. To make this possible, the book:
- Focuses on intuitive explanations with examples, while avoiding deep mathematics;
- Provides numerous examples, tables and figures (over 200 figures and 110 tables), to help readers grasp the concepts and techniques;
- Introduces the R statistical programming environment and provides step-by-step guidance to learn R and apply it to the techniques covered; After working through the book readers will be able to independently apply the techniques covered on their own data. After completing the book, readers would have mastered an important subset of the R language.
- Recognizing that people master new topics only by doing, the book provides many instructive labs, -lab assignments and review questions with detailed guidance and explanations. Rather than just providing the steps in the form of "what" to do, the book also explains "why?"
- All the data files needed to work through the labs and lab assignments are available as free downloads from the book's web site.
- To shield those who are new to any form of computer programming, the book comes with many convenience functions that can serve to automate what might otherwise be confusing procedures.
- Quick introduction to R programming -- assumes no prior background in R;
- Important data analytics concepts;
- Exploratory data analysis and graphing with R;
- Affinity analysis;
- Classification techniques like K nearest neighbors, Naive Bayes and Classification trees;
- Regression techniques like simple and multiple linear regression; K nearest neighbors for regression and regression trees;
- Time series analysis; and
- Data reduction techniques like Principal Component analysis (PCA) and cluster analysis (k-means clustering)
- Sales Rank: #649330 in Books
- Published on: 2015-08-29
- Original language: English
- Number of items: 1
- Dimensions: 11.00" h x .95" w x 8.50" l, 2.14 pounds
- Binding: Paperback
- 420 pages
About the Author
Dr. Viswa Viswanathan teaches Computing and Decision Sciences at Seton Hall University in New Jersey, USA. In addition to his significant teaching experience at the Indian Institute of Management, Kolkata and at Seton Hall University, he has spent a decade in the software industry in the US and in India. He enjoys developing and deploying significant web-applications. Viswa has authored several peer-reviewed scholarly articles in leading journals like Operations Research, IEEE Software, International Journal of Artificial Intelligence in Education and Computers and Industrial Engineering. His work has touched many fields including information systems, artificial intelligence, Operations Research, software development and computer based learning. Viswa's keen interest in understanding how people learn and his evidence-based conviction that people learn best by doing has led him to explore new approaches to helping people to learn advanced concepts. Through his writing, he hopes to make seemingly advanced concepts accessible to many people.
Most helpful customer reviews
3 of 3 people found the following review helpful.
It goes into a fair amount of detail and has pretty clear explanations so as not to lose a person ...
By axulpaix
This book gives you the baby steps to working with R and doing some business-related data analysis. It goes into a fair amount of detail and has pretty clear explanations so as not to lose a person who has little background in data analysis. Practice Excel sheets that have already been data cleansed are provided online. I agree with the other commenter that this is for someone who has little to no experience working with R, this book is a great start. Really I want to give this book a 4.5. I am docking .5 because the layout of some of the graphs in the book are awkwardly placed in relation to the directions that it goes with. There were also a couple of typing errors. Other than that, I can only imagine that a second edition will fix this. Anyway, if you have never used R before, you can learn how to use it to do Classification, Bayes Analysis, Predictive, and other data analysis stuff.
4 of 4 people found the following review helpful.
this book is a perfect fit! Not only do you get hands-on exposure ...
By BobS
I recently got my hands on this book and am pleasantly surprised at the detail and content provided. Most surprising is the level of simplicity in which the content is presented. For someone who has little to no experience working with R, this book is a perfect fit! Not only do you get hands-on exposure to working with R, but the content and hands-on exercises reinforce the concepts and value. From a business analytics standpoint, you'll find some very neat concepts presented like, Affinity Analysis; Classification; Bayes Analysis; Predictive; and others. A must get for those seeking to learn data analytics and working with R without all of the the anxiety of doing so!
1 of 1 people found the following review helpful.
Makes data analysis with R easy to learn and understand
By Marlon Zuniga
As someone with 0 experience in any analytics or programming language, I was surprised at how simple it was to learn the data analytics techniques in R by following the instruction in this book. Some of the techniques covered include classification trees, regression analysis, time series analysis in addition to others. The book provides real life examples of when each technique may be applicable and then provides step by step instruction of the R commands necessary to conduct the analysis with the data provided. I also liked that it did not place great emphasis on the underlying mathematics behind the calculations (that's R's function) but instead focus on how to interpret the results. Overall, I found this book to be very helpful and recommend it to anyone interested in learning how to conduct data analysis using the R language.
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