R and Python, are excellent tools in their own right but more often than not are conceived as rivals. Performance wise R is not the fastest language and can be a memory glutton sometimes when dealing with large datasets. Capable of standalone analyses with built-in packages.īut there is a downside.Comes equipped with excellent visualization libraries like ggplot2.CRAN currently hosts more than 10k packages. Consists of packages for almost any statistical application one can think of.Some of the features that make R stand out among other languages are: It first appeared in the August of 1993 with its first stable release in 1995, and since then has been widely used by statisticians and data miners for statistical computing. R is essentially a software for statistical computing and graphics which is supported by the R foundation of statistical computing. However, when it comes to statistical computing, python lags behind and doesn't have specialized packages, unlike its counterpart R. Has efficient Packages like pandas, numpy and scikit-learn which make it an excellent choice for machine learning activities.Simple and easy to understand and learn.Some of the reasons for its vast popularity are: Python, today is among the fastest-growing programming languages in the world. Since its release, Python has been extremely popular in various fields including data science. ![]() It was created and released in 1991 by Guido Van Rossum. Python is an interpreted, high-level, general-purpose programming language. Let’s have a look at the various aspects of these languages and what’s good and not so good about them. Whereas Python is a general-purpose language used for a variety of applications, R is a programming language and environment for statistical computing and graphics. Both the languages have a wide variety of tools which provide an excellent array of functions, extremely suitable for the data science scenario. ![]() Out of all those languages, Python and R appear to be leading the race.īoth Python and R are being widely used in the data science world. ![]() A lot of tools in the form of programming languages are available in the market today. Therefore it has become imperative that we should be able to utilize this vast amount of data to generate actionable insights and work upon them. Right from Banking, to insurance to healthcare, humongous amount of data is being generated every second of the day. Data Science has become an integral part of every industry today.
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