Best Programming Language for Data Science: Python vs R
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Best Programming Language for Data Science: Python vs R

Best Programming Language for Data Science: Python vs R
Photo by Mika Baumeister / Unsplash

There are a lot of programming languages available nowadays, but not all of them work for Data Science. As we know, Python has been the king of data science for the last couple of years, but as the field progresses we see more and more languages being created or repurposed for data science. In this article I will give you an outline of the two most popular data science programming languages, Python and R. This article does not say that one is better than the other, just simply listing the advantages of each of them. In the end, use whatever you find more comfortable and easier to use.

Advantages of Python

Python has gained a lot of popularity in recent years, because it is easy to learn, has a great community, and has a lot of libraries. Plus, it is used in Machine Learning and AI, which are areas that are getting more and more popular. It is a pretty old programming language, made in 1991, but has matured a lot over time. Numpy, Pandas, Scikit-learn, PyTorch all use Python. You can even make websites with Python frameworks like Flask and Django. There are no limits on what you can do with Python. It is very easy to read. Not using semicolons makes for faster development, although the language itself is pretty slow.

Advantages of R

R has recently risen as an alternative to Python, even though it was created in 1993. The language is geared more towards statistics and making statisticians' work easier, although it has proven itself very useful for data science. After all, data science involves a lot of statistics. R can perform a wide variety of functions,  including but not limited to data manipulation, statistical modeling, and graphics. The one really big advantage of R in my opinion is its extensibility. Anyone, with the right skills, can easily write their software and distribute it in the form of add-on packages.

As we can see, both languages excel at what they are geared towards. Python is more of a general-purpose language, and if you learn it you can dabble in more areas than you would with R. At the end of the day, use whatever you like. I use both, although I am still learning R. It comes down to preference and the task at hand.

If you'd like to learn Python, I have a playlist in the making on my YouTube channel.