R vs Python | Best Programming Language for Data Science and Analysis | Edureka Rewind

Key Takeaways

with the massive growth in the importance of Big Data machine learning and data science in the software industry or software service companies two languages have emerged as the most favorable ones for the developers R and python have become two most popular and favorite languages for the data scientists and data analysts both these are similar yet different in their own ways which makes it difficult for the developers to pick one out of the two this is shanthony from Eddie Rea and in today's session I will help you find out which one wins the battle and how so let's begin with a brief introduction about both the languages R is considered to be the best programming language for any statistician as it possesses an extensive catalog of statistical and graphical methods python on the other hand can do pretty much the same work as are but it is preferred by the data scientists or data analysts because of its Simplicity and high performance now both the programming languages are free and open source and were developed in the early 90s R is a powerful scripting language and highly flexible with a Vibrant Community and Resource Bank whereas python is a widely used object-oriented language which is easy to learn and debug so let's have a look at the comparison parameters for the two and find out which one is better if we look at the ease of learning R has a steep learning curve and people with less or no experience in programming finds it difficult in the beginning but once you get a grip of the language it is not that hard to understand python on the other hand emphasizes on productivity and code readability which makes it one of the simplest programming languages it is a preferable language for the beginners as well as the experien developers due to its ease of learning and understandability now if if we compare the speed R is a low-level programming language due to which it requires longer codes for simple procedures this is one reason for the reduced speed on the other hand python is a highlevel programming language and it has been the choice for building critical yet fast applications in case of data handling capabilities R is convenient for analysis due to the huge number of packages readily usable tests and the advantage of using formulas but it can also be used used for basic data analysis without the installation of any package and only the big data sets require packages like data. table and deeper now in the initial stages the python packages for data analysis were in issue but this has improved with the recent versions numai and pandas are used for data analysis in Python and both these languages are suitable for parallel computation now if we consider graphics and visualization a picture is worth a thousand words visualized data is understood efficiently and more effectively than raw values R consists of numerous packages that provide Advanced graphical capabilities like the GG plot 2 is used for customized graphs now visualizations are important while choosing data analysis software and python has some amazing visualization libraries such as caborn Boke and pigal it has more number of libraries when compared to R but they are more complex and also gives a output with a rise in popularity in deep learning two new packages have been added to the r Community caras R and R Studio Scaris now both the packages provide an R interface to the python deep learning package it's a highlevel neural network API which is written in Python and capable of running on top of either tensorflow or Microsoft cognitive toolkit now getting started with Caris is one of the easiest ways to get familiar with deep learning in Python and that also explains why the kis R and kis packages provide an interface for this fantastic package for the r users now if we compare the flexibility of both the languages it is easy to use complex formulas in R and also the statistical tests and models are readily available and easily used on the other hand python is a flexible language when it comes to working on something new or building from the scratch it is also used for scripting a website or or other applications now if we look at the code repository and libraries comprehensive R archive Network that is CN is a huge repository of the r packages to which users can easily contribute the packages consist of R functions data and compiled code which can be installed using just one line it also has a long list of popular packages such as deer plier data. table and many more on the other hand python consist of pp I package index which is a repository of python software and libraries although users can contribute to pii it is a complicated process the dependencies and installation of python libraries can be a tiring task at times some popular libraries of python are pandas numai and matplot lib now if we look at the popularity of both the languages they started from the same level a decade ago but python witnessed a huge growth in popularity and was ranked first in 2016 as compared to our that ranked sixth in the list also the python users are more loyal to the language when compared to the users of the other as the percentage of people switching from R to python is twice as large as python to R now when we consider the job scenario the software companies have been more inclined towards Technologies such as machine learning artificial intelligence and big data which explains the growth in the demand for python developers although both the the languages can be used for statistics and Analysis python has a slight Edge over the other due to its Simplicity and ranks higher on the job Trends in case of community and customer support usually commercial softwares offer paid customer service but R in Python do not have customer service support which means you are on your own if you face any trouble but both the languages have online communities for help and python has a greater Community Support when compared to R so now that we are done with all all the parameters of comparison we can say that it was a tough fight between the two but python emerges to be the winner due to its immense popularity and simplicity when compared to our so what do you think do let us know about your opinion in the comment section below till then thank you and happy learning