![]() ![]() Python is yet another high-level object-oriented programming language widely used in scientific and numeric computing. The feature rich library of R is what makes it the most preferred choice for statistical analysis. Technically, it is both a language in statistics as well as computer science and analytics software with significant usefulness in data analysis. ![]() Since then, it has been used in every conceivable discipline from science to engineering. It began as a research project by Ross Ihaka and Robert Gentleman in the early 1990’s and by 1995, the program had become open-sourced meaning anyone could modify or alter the code absolutely free of cost. R is more than just a computer program it is a statistical programming environment and language for statistical computing and graphics. R is a powerful open-source programming language with aspects of both functional and object-oriented (OO) programming languages. But which of these languages is easy to use and best to learn? ![]() Python is well known for being great with big datasets and flexibility but still catching up to the number of good statistical libraries available in R. R is not particularly a fast programming language and the poorly written code can be fairly slow. However, the technology is not without its fair share of downsides. R is a powerful programming language which is rapidly becoming the de facto standard among professionals and has been used in every conceivable discipline from science and medicine to engineering and business. R is the latest cutting edge technology widely used among data miners and statisticians for developing statistical software and data analysis. Both R and Python are the two most popular open-source programming languages oriented towards data science. ![]()
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