R and Python are both open source programming languages with a relatively large community. Since their debut tooth of these languages has been continuously used in the data science statistics related projects. Both excel in their own way where continuous addition of libraries and tools is done on a constant basis to increase the user experience for both these programming languages. The main purpose or use of the R is for the statistical analysis as the programs it has for this approach are more sophisticated and advanced just as needed to perform the statistical analysis.
As for Python, it uses a more direct approach with data science and its varied prospects. But given their infrastructure and the use of open libraries for working, both of these programming languages can be a hit when it comes to data science. Python is a general language that has a more appreciative and readable syntax whereas R is more bent on performing the statistical operations and is good in that particular sector.
What is R? And all you need to know about it
Academics and statisticians have taken their time when developing the R and it almost took them a period of two decades to get there. Now, R has become the most dedicated and professionally appreciated programming language for performing stats and solving complicated problems. There are over 12000 different packages that are available for you.
This means that you can certainly shift from this library to that, sort of jumping in between until you find the absolutely required one which can help you with a specific project you are working on. Those professionals who have to work on special statistical and analytical projects would absolutely love to go with R simply because of having such a detailed number of libraries to help them out. One of the most alluring benefits of using R is the ease of producing output. While other competitors don’t make it easy to get output from the results that were run but R makes it pliantly easy for the users.
Python; what’s all this hype about?
Python can be used as the tool using which machine learning can be deployed and implemented at a larger scale. It uses simple language syntax that is easily readable than the other syntaxes used by other programming languages. Like R Python can also help with a variety of systems such as data wrangling, engineering, running statistics, and its dynamic use in web scrapping too.
Years ago python was not that fit with the data analysis and machine learning-oriented tasks due to a lack of overall libraries. But this has been taken care of as now with Python you can get access to a variety of user libraries that makes it a little easier to perform the analytics related to the data science. It is one of the reasons that Python is more effective with Data science than it is with the statistical analysis.
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Python vs. R; A tough battle
Which excels where?
Python is more intuitive and helpful while taking into account the realm of the deep learning, with its tools such as Keras and PyTorch the realms of deep learning and machine learning can be better explored and observed, so this is an area where Python clearly excels.
In the statistical research, however, there is a bitter aspect of using one too many data models which can be better accomplished with R given the number of statistical models this programming language has within it.
Python taking it to the next level
As you have already observed that Python has a lot of tools and software systems that simply make it a clear winner regarding the customizability of the programming related software systems and for data science and deep learning. Python can also help with deploying models into other pieces of software. Given the fact that Python is a general-purpose programming language, you can simply work on or build your entire application within Python and then readily deploy it wherever you want. This is something Python has an edge on.
R not coming slow to help the non-technical users
One of the most dedicated benefits or say a trick up R's sleeves is the creation of intuitive dashboards using Shiny which is a tool embedded into R. This approach can massively help the non-technical users to create a simulated approach for using R regarding statistical and analytical work. These dashboards can also be shared among colleagues working on the same project. This literally removes the confusion of not being able to use the programming language and all the tools that it has to offer for help.
If you want to pursue a career for yourself in data science then data analytics certification might be the way to go as it will help you broaden your overall horizon and start right up with things that matter the most in building your career.