The first question a lot of aspiring data science enthusiasts ask is whether they should go with Python or R-programming as their choice of programming language. Hear Merishna, a data strategy consultant, shed some light on the topic.
Well, it depends on what you want to achieve.
If you’re looking to become a data analyst then it doesn’t matter if you choose to use either Python or R to perform your analysis. Since, both of these programming languages offer a variety of tools and libraries that (are quite similar and) help you to perform transformations and manipulations on data.
Similarly, you can also create informative visualizations using any of these programming languages to put across insights gained from your analysis. However, when you step into the shoes of a data scientist, your job is not only to analyze and model the data but also to deploy as well as monitor your model’s performance in production.
Python is more oriented towards programmers and developers with a linear learning trajectory in comparison to R, which is more oriented towards statisticians and researchers.
Also, working in Python gives you a range of frameworks wherein you can quickly deploy a model which gives it a much greater advantage over R when it comes to scalability and reproducibility.
Therefore, it is fairly easy to maintain small as well as large-scale data science products using Python and integrate them with other applications. The same kind of flexibility is hard to achieve using R.
So, the answer again lies on what you want to achieve. Python is the way to go if you’re interested towards building a product or a scalable service using data science. On the contrary, if you’re focused (mostly) on analytical processes and visualizations then R is certainly a good choice of a programming language.
If you have a question of your own, feel free to comment it down.