Talking about the data science, what big names come into mind regarding the programming languages? Surely these are Python and R. Both of these are the open-source programming languages excelling in their own ways. Python shares a more readable language syntax which is easier for deep data and machine learning. Whereas R has the advantage of an overwhelming number of libraries and data models which can be integrated for various statistical analysis and workarounds.

Talking about the very prospects of R and Python a general conclusion has been made that Python overtakes R regarding data science and machine learning. All the facts that point out to this particular narration can be found online with a general twist where Python is more acclimated as a dedicated programming language than R for data science and machine learning. But in this article, we will be using our own sense of working out with these programming languages and generating a final outcome of our own such as which one is the best.

In order to do so, take a look at the findings and general explanation of these given below;

Python

Python started its journey in the late 80s and early 90s. It plays a crucial role in powering the internal infrastructure of the tech giant Google. Python is the only programming language that uses the simplest syntax for coding that is highly readable by the developers and programmers all around the world. Python is a multi-reason programming language and adaptive too with whatever small changes you are willing to make along the line in overall build or designing of your code.

No wonder it is regarded as a great language for machine learning because while doing so the programming infrastructure needs to be adaptive and continually syncing with the big data which happens to be just the case with Python. Specifically, if you are working with AI then Python has to be your top pick in that. 

Extendable advantage of using Python

  • General-purpose language; if you are working on a project that requires more than the statistical approach to getting it done then you have to use Python, period. It is a multi-tiered language that has an extremely adaptive and easy to read syntax; perfect for everyday use.
  • Smooth learning curve; Python is extremely easy to learn language and its circle is pretty huge too which allows you to interact with professionals that are just top of their game
  • Faster integration; Integration is considered a vital activity especially if you are developing something that takes into account a multi-platforms spin-off approach. Working with Java and C++ is relatively easy but the difficulty arises when you have to integrate them with other platforms, the same problem arises with the R. But Python simply takes it off the bat pretty good, it is conveniently easy to integrate than any other language out there.
  • Tons of libraries to choose from; Python offers the users continuous access to a vast number of libraries. These can be used for a variety of purposes such as for parsing of code, gathering, controlling, and customizing data for keeping the integrity of the project intact. With the AI approach and prospects of machine learning the projects running on Python have a great chance of scoring more yield and profits with their endeavors than with R.

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R

R is known as the programming language that was built specifically for the statisticians and for the statisticians. The main purpose of the language is to ease down the process used by stats professionals for the interpretation of the statistical data and graphics work. This means that R fits right into multiple science-oriented projects and ease down the stats oriented problems; no questions asked. Built-in tooling of the language and the third-party libraries inclusion within the language is something that makes it a more vivid choice to be used for stats and analytical work.

Advantages of using R language

  • The language consists of many libraries and set of tools that are specialized for performing data operations. Such infrastructure allows you to modify the data structures so easily and then transform them into many efficient models for stats and analysis purposes.
  • Multiple packages and libraries have been included in this language that provides access to various data models too only to be used for different stats and data analysis related work. You don't have to start away with writing long algorithms in order, to begin with, your statistics project because these libraries can do that for you.
  • R language can work easily on whatever operating system that you use such as Windows, iOS, and Unix like platforms too.
  • Using internally laced systems this language can also be used for developing various dashboards from which the stats oriented work can be easily taken care of.

The Verdict

It is pretty much clear from all the information depicted above, isn’t it? Python comes out as a clear winner when it comes to data science and machine learning. But this doesn't mean that R is not cut out for it, it is just that the language is more focused on the data processing and statistics work. But with Python, we get customizability, faster integration, and a bunch of libraries making it more and more adaptive every passing second. That is why Python overtakes R for Data science and machine learning projects.

Using Python you can open for yourself a perfect gateway of opportunity and success in the job market. But before that, you are required to learn python for data science as it will truly open up the potential needed for exhilarating success.