Wednesday, November 16, 2022

R vs Python - Which One is Dominant for Machine Learning and Data Science?

R vs Python- Which One is Dominant for Machine Learning and Data Science?

Machine Learning and Data Science are in high demand. Both these domains are pretty hard and involve extensive usage of mathematics, statistics, programming, and domain knowledge. While all these skills can be acquired with practice, you should know a programming language to be a data scientist or a Machine Learning engineer.  

There is a lack of skilled professionals in this area, and if you are someone who is looking to jump into the domain, you need the proper tools and technologies.

Two widely used programming languages in the Data Science and Machine Learning domain are Python and R. If you are looking to upskill and break into these areas, you need to learn any of these languages.

Beginners are often confused about what to choose, so in this article, we will learn everything about both languages, and at the end, we will have a comparison based on real-world usage of the languages.

Let’s start off with Python.


Who hasn't heard of Python and its power in the Data Science and Machine Learning domain? It is an open-source and high-level programming language. The main aim of Python's development was to have a language that was easy to learn and provide excellent code readability. Both these aims have been taken care of very well.

Today Python has changed a lot since its initial release. It has a large community, and even companies like Google and Facebook use Python in their tech stack.

Python found its shot to fame with the extensive demand for Machine Learning and Data Science among companies and developers. Since it has become popular, it has held the top spot in Data Science languages, but it constantly competes with R, which is equally remarkable.

Having known about the background of Python, let’s have a look at the rival R.


R is a mathematical language developed with the sole aim of making mathematical and statistical computing easier. Being an interpreted language, R is slower and more prone to errors as there is no compilation and no pre checks that happen.

R is the language for people moving to Data Science and Machine Learning from mathematical and statistical backgrounds. Unlike Python, which can be written and run anywhere, you might need to install an IDE to use R to its fullest. It is open-source, and you can easily integrate it with other languages. RStudio is the favorite IDE among R developers, so you may give it a try.


After knowing a brief history of these languages, it is time that we discuss the intricacies of these languages, and find out which one is dominant for Machine Learning and Data Science

Which is Better For Machine Learning & Data Science?


For people coming from a programming background with significant knowledge of object-oriented programming and programming in general, Python is the language to go.

But, if you are from a maths/stats background and you are starting to learn Data Science and Machine Learning, R is the language you should pick up first.

You will feel comfortable with the syntax, and as your needs change, you can pick up newer languages too. I had a good command over compiled languages like C and Java, which helped me transfer my learnings and understand the Python language better.

Community Support

As a data analyst who is just starting out, for me, community support was a significant factor, and that is where I chose Python. With an established community and tons of solutions on websites like Stack Overflow, learning and implementing Python can be pretty straightforward.

If you like to dig deeper into documentation and you are just learning Data Science and Machine Learning casually, you might choose R.

Python has an active community on programming platforms, and if you ask something, you’ll surely get answers pretty soon as you post. With the wide applications of the language, you’ll often get different solutions for your problems, so that you can implement solutions better.


By learning Python, I have opened a lot of doors for myself. I can pick up any domain in software development or Data Science, and I can easily accomplish it with my Python skills. 

Python has enabled me to develop web applications, backend services, CI/CD processes, and even data pipelines for large enterprise-scale data warehouses. It is also a reason why businesses look to hire python programmers as they give the same level of output in a cost effective way

While Python allows you to be a generalist and pick up new skills as and when required, R will enable you to be a specialist.

If you are determined to make big in Data Science and Machine Learning, then you should choose R as when the data becomes too much to handle, R has speed benefits and better libraries.

Third-Party Libraries

When it comes to Data Science and Machine Learning, there is a lot to code, but you can save time by using third-party libraries.

Third-party libraries are open-source packages that can be installed and used instantly to support your code. Such libraries help you to stop reinventing the wheel and provide you with functionalities that are ready-to-use and widely tested.

Both Python and R have an awesome community and excellent third-party libraries. While Python is more popular, it has significant options to choose from.

You can easily get multiple libraries that perform the same tasks, and it is up to you to choose the best.

Python’s libraries are managed by pip, and installing them is pretty easy. If you are looking to get started with development quickly, pip and Python will help you integrate all major third-party libraries.

With Python, you get instant support on libraries, and newer versions are released regularly that contain feature updates.

On the other hand, R’s third-party libraries are not as par to Python’s. R offers maximum in-built functionalities, and if you need to use third-party libraries, you can get them too but it's different from Python.

Today, R’s community has converted all major Data Science and Machine Learning libraries available in Python to R, but still, there is a long way to go.



In the end, both these languages are just the tools to create incredible Machine Learning models or help businesses to make better decisions. It is your personal choice on which you learn first, but in the end, it's a tech career, and in the real world, you will often come across situations where you might have to pick up another language and master it to get the work done.

Both Python and R are just replacements for one another, and if you have your Data Science concepts clear, you can implement them in any language.

While you learn Data Science, there will be some areas where Python will excel, and there will be some areas where R excels.

For me, both languages look pretty similar in functionality, and it all boils down to a personal preference. If you love to be a generalist, you should learn Python as it opens lots of doors for you. On the other hand, if you want to master everything in Data Science, and Machine Learning, and dominate at work, then R is your friend. 

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