We know you've got a lot of options when it comes to programming languages, but I just want to make sure you're 100% aware of your options when it comes to machine learning. So, in the spirit of healthy competition, we present you with Python vs R for machine learning programming!

Python and R are two programming languages commonly used by data scientists. They have a lot in common, but their differences help data scientists solve different types of problems. Let's see how they stack up against each other.

Some companies are choosing R, a programming language that has been around since 1993. Other companies are choosing Python, a programming language that was created in 1989 and is still going strong today. Though both languages have their own sets of strengths and weaknesses, both have similar sets of strengths and weaknesses, so users of either language can get the job done well.


If you're looking for a language that will get you to a working model as quickly as possible, then R is for you. It was designed for statistical computing and graphics using S language as a starting point. It's great if your goal is to just prototype something fast and move on. Batch processing is another strength of R, meaning that it can run multiple processes at once without any real-time constraints. Its biggest weakness? It's not really equipped for big data sets.


Python isn't quite as big-data-friendly as R, but its biggest strength is that it's so adaptable and flexible—you can adapt it to fit pretty much any purpose or problem set. Python is easy to read and it's a good choice for beginners thanks to its large online community and extensive set of available libraries. Still, it isn't commonly used for high-traffic websites or apps because of its slow performance at runtime. Its biggest weakness? It's not always super-efficient in terms of speed and execution time (though this is changing!)

Best case scenario: You use both! You'll be really well-equipped to tackle anything from simple tasks to big data sets—and you'll get the best of both worlds when it comes to speed and efficiency.

Feb. 23, 2022
Machine Learning

More from 

Machine Learning


View All