Python vs R for Data Science – A Comparative Analysis

Python vs R for Data Science – A Comparative Analysis

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Rashmi
Rashmi Karan
Manager - Content
Updated on Oct 12, 2023 12:08 IST

With the massive growth in the importance of big data, machine learning, and data science across the industries, two languages emerged as the most favorable for developers, R and Python. It is hard to pick between these two flexible data science languages, as both are free and open source. Let’s talk about the basics of Python and R, and then move on to Python vs R.

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Python 

Python is a widely used object-oriented language, easy to learn and debug. Learning Python provides programmers with the skills necessary to work on the business, digital products, open-source projects, and various web applications outside of data science.

Must Read – What is Python?

R Programming 

R Programming is considered the best programming language for any statistician since it has a comprehensive catalog of statistical and graphical methods. R is a powerful and highly flexible scripting language with a vibrant community and resource bank.

Let’s explore more about Python vs R.
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Python vs R for Data Science

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Also Read – Most Popular Programming Languages for Data Science

 

Final Thoughts on Python vs R

Python is a powerful and versatile language that programmers can use for a variety of computing tasks. Learning Python will help you develop a universal data science toolkit, and it is a versatile programming language that you can learn quite quickly, even as a non-programmer.

On the other hand, R is a programming environment designed specifically for data analysis that is very popular in the data science community. You will need to understand R if you want to go far in your data science career.

R was built as a statistical language; it is much better suited to statistical learning, and anyone with a formal background in statistics can easily use R. Python is among the most popular programming languages in the world and helps to deploy and implement machine learning at a large-scale, especially when data analysis tasks need to be integrated with web applications.

Also explore – Statistical Methods Every Data Scientist Should Know

The reality is that learning both tools and using them to their respective strengths can only improve you as a data scientist. Versatility and flexibility are traits that any data scientist is at the top of their field. The Python vs R debate limits you to one programming language. You should look further and embrace both tools for their respective strengths. Using more tools will only make you better as a data scientist. 

 

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About the Author
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Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio

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