Analytics 2.0 problems are fundamentally different. Specifically, they have three characteristics that have problems Analytics 1.0: rely heavily on statistical methods, which work in large data and require seamless integration with web techniques.
R is designed for statistical computing, with thousands of packages containing almost all statistical application applications available, which meet the first feature perfectly. In fact, it is perhaps the only means to make such a comprehensive manner. Think of packing R as LEGO pieces. When a problem requires a different method, simply connect a different R set. It's that simple. In addition, R can handle more data points than Excel by default. You can even write R code in parallel low-cost computers and use in the cloud to analyze large data sets, cases of Amazon Web Services (AWS). Finally, you can easily integrate web technologies with R for web analytics applications.
R training lets you learn R programming language that is deployed for varied purposes like graphics representation, statistical analysis and reporting. With this online R Programming & data analysis training you will be able to get a clear understanding of the core concepts, import data in various formats for statistical computing, data manipulation, business analytics, machine learning algorithms and data visualization. You will learn about the various functions, data structures, variables and flow of control. Learn how to go about doing R integration with Hadoop through practical R exercises.
R programming is a statistical language for the specialization of data science, which has higher adoption rates at present due to its extended nature. It can be widely deployed to various applications and can be easily increased. Training R will help you learn R in getting all the jobs created in large companies that provide very good salary scales.