Well, if you would have asked which language is best for Data Science, a few years ago, the answer would have been R. But lately, things are changing, Python has become one of the biggest challenges for R programming. So, it all comes down to which language suits you the best and which type of problems are you trying to solve, and the kind of result you require. R is developed solely for Data Science and is made for statisticians to help them ease their work of analyzing huge amounts of data. R for Data Science has huge libraries and packages to meet all kinds of needs to get your job done. R also has a very large dedicated community that has solutions for every problem that you might encounter.
The biggest reason why Python is finding it hard to beat R is that R is specifically made for Data Analysis and is not a general-purpose language like Python. Python also has a big community like R, I should say bigger than that of R, but specifically for Data Science is less when relatively compared. But Python has several Data Science packages and frameworks that will do more and produce above par results and sometimes more than what R can produce. And the trend suggests that there is gaining popularity for Python in the Data Science atmosphere. So, both languages are good for working with Data Science, it's you who should decide what to choose, based on the requirements that you have. The best piece of advice: try both and become skilled in both in this ever-changing world, it’s a boon if you are trained in both.
And for that, I would recommend you to enroll in a Data Science with Python or R certification course from Intellipaat, whose courses are designed to inculcate practical skills through guided projects and exercises. Also, watch our YouTube video on Data Science
Back