AI News, How Crucial is R for Mastering Data Science?

How Crucial is R for Mastering Data Science?

One of the most important aspects of data science is that its results are applicable to almost any sector like healthcare, e-commerce, travel and education among others.

A sound understanding of the implications of data science can help sectors identify and quantify their challenges and address them in an effective manner.

For instance, retail giant Target identifying pregnant women based on products they purchased, or the recommendation engines being used by Amazon, Netflix and the likes.

is not just a statistics package which allows you to use certain predefined functionalities, it is rather a language through which you can develop and execute functionalities according to your own need.

It’s a language for writing DSLs, which is something that’s altogether more powerful.” One of the goals of R is that the language should imitate the way that people think about real-world problems.

Being open source, one can easily obtain the source code of a piece of functionality and tweak it according to one’s own needs in no time.

From making a web application to generating mesmerizing harmonographs, you can implement a wide variety of concepts with R.R also allows incorporating C and C++ functionalities from within R to powerfully complement your R code.

large part of R’s success is due to the ecosystem of open-sourced packages that add functionality other than what the core installation of R (base R) offers.

Others sources include exploration and starter scripts on competition platforms where one can observe great deal of knowledge dissipation.

For instance, functions like mean, sd or a user defined function can be treated as data itself and be used in other function arguments.

R data types allow programmers to use the data in a natural form without having to put it into a particular predefined structure.

Choosing which tool to take up is dependent on a variety of factors, such as Choosing which tool to take up is dependent on a variety of factors, such as background in programming or statistics, or what you want as your output, or simply availability.

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