R is very important in data science because of its versatility in the field of statistics. R is usually used in the field of data science when the task requires special analysis of data for standalone or distributed computing.
R is also perfect for exploration. It can be used in any kind of analysis work, as it has many tools and is also very extensible. Additionally, it is a perfect fit for big data solutions.
Following are some of the highlights which show why R is important for data science:
Data analysis software: R is s data analysis software. It is used by data scientists for statistical analysis, predictive modeling and visualization.
Statistical analysis environment: R provides a complete environment for statistical analysis. It is easy to implement statistical methods in R. Most of the new research in statistical analysis and modeling is done using R. So, the new techniques are first available only in R.
Open source: R is open source technology, so it is very easy to integrate with other applications.
Community support: R has the community support of leading statisticians, data scientists from different parts of the world and is growing rapidly.
So, most of the development of R language is done by keeping data science and statistics in mind. As a result, R is become the default choice for data science applications and data science professionals.
R is also perfect for exploration. It can be used in any kind of analysis work, as it has many tools and is also very extensible. Additionally, it is a perfect fit for big data solutions.
Following are some of the highlights which show why R is important for data science:
Data analysis software: R is s data analysis software. It is used by data scientists for statistical analysis, predictive modeling and visualization.
Statistical analysis environment: R provides a complete environment for statistical analysis. It is easy to implement statistical methods in R. Most of the new research in statistical analysis and modeling is done using R. So, the new techniques are first available only in R.
Open source: R is open source technology, so it is very easy to integrate with other applications.
Community support: R has the community support of leading statisticians, data scientists from different parts of the world and is growing rapidly.
So, most of the development of R language is done by keeping data science and statistics in mind. As a result, R is become the default choice for data science applications and data science professionals.
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