As a data science enthusiast you have definitely come across the names of Python and R. Yes, both of them are open source programming languages suitable for data science and advanced analytics work. Both of them have been in the scene for quite a long time, R being the older of the two. It goes without saying that these two tools have been rivals in terms of popularity and application. The general purpose coders and developers can easily choose Python over R for a number of reasons that we will look into later. But when it comes to the data science crowd, there can be a serious fix among the enthusiasts.
Between the two giants
We can comfortably call these platforms giants in the field of data science not only because of how popular they are among the practitioners but also because of the organizations that use these tools extensively. While Google, Instagram, Spotify, Netflix, to name a few, have considerable chunks written on Python, R is extensively used by Facebook, Twitter and Microsoft among others. In this section they really fight toe to toe. However Python users among application developers out number R users by a huge margin.
The most desirable qualities
Let us go with Python, first. It is almost famous for its simplicity and smooth learning curve. But if you are planning to run some serious data science models on Python you may just have to get into the complexities. Nevertheless the text based syntax does make things easier.
The libraries Python has built for analytics and machine learning are phenomenal. Tensorflow and Keras are probably the most used libraries to work on Python. Apart from that PyTorch, Scikit-learn, among others are quite famous among the data science professionals and the machine learning developers.
R on the other hand is more of a niche language for data science professionals. It is extremely well suited for statistical computing. The R libraries like MatplotLib are great for data wrangling and visualization.
R has made great developments towards being accessible from a lot of different platforms while accessing data from different sources in different formats.
The visualization features on R combined with the statistical prowess makes it a very formidable tool for data science.
The way forward
Both these languages have extensive applications and dedicated communities working towards making them more accessible and useful. If you are aiming for a long and colourful career in data science, the best way to go is to learn more. While a Python course gives you more options, a data science R course adds depth to your skillset. Learning a little more than the current necessity can never be harmful.