R for Data Science by Dan Toomey
English | ISBN13: 9781784390860| 2015 | 364 pages | PDF + EPUB | 14MB
Learn and explore the fundamentals of data science with R                                                
About This Book

Familiarize yourself with R programming packages and learn how to utilize them effectively
Learn how to detect different types of data mining sequences
A step-by-step guide to understanding R scripts and the ramifications of your changes

Who This Book Is For

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.

What You Will Learn

Develop, execute, and modify R scripts
Find, install, and use third-party R packages
Organize your data to get the best results
Produce graphical displays of your results, including 3D visualizations
Perform statistical analyses that you can use all the time
Understand the trade-offs between different approaches to problems
Be comfortable with trying features to fine-tune your results
Adopt and learn data science with R in a practical tutorial format
Explore concepts such as data mining, data analysis, data visualization, and machine learning using R

In Detail

R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands.

The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available R functions and packages. In many cases, the resultant data can be displayed in a graphical form that is more intuitively understood. You will also learn about the often needed and frequently used analysis techniques in the industry.

By the end of the book, you will know how to go about adopting a range of data science techniques with R.

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