Data Science, Programming

Introduction to R Programming

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About the Course

During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.

The king of statistical languages for data analysis and visualization takes 6th place in the "The 2015 Top Ten Programming Languages" list. Ranked No 9. position in the year 2014 and language R emerging as a powerful tool in science statistical data.

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Why should you learn R?

Here are the reasons to learn R:

  • The big data and data science industry is rapidly growing, so is the need for data scientists and statisticians. By 2018, United States could face a shortage of 140,000 to 190,000 people with deep analytics skills and 1.5 million managers and analyst who would know the use of big data to make information based decision making (Source: McKinsey Global Institute).
  • R is increasingly becoming the required choice of tool in most of these jobs due to its advantages such as being open source, having external support of more than 5000 packages and gets easily integrated with big data technologies like Hadoop, MapReduce.
  • A flexible statistical analysis toolkit. All of the standard data analysis tools are built right into the R language: from accessing data in various formats, to data manipulation, to traditional and modern statistical models.

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