For these reasons, Python is often cited as a go-to programming language for newcomers with no coding experience. In fact, high readability and interpretability are at the heart of the design of Python. Python is an easy language to read and write due to its high similarity with human language.

Python is backed by a vast community of users and developers who ensure the smooth growth and improvement of the language, as well as the continuous release of new libraries designed for all kinds of purposes. One of the reasons for the worldwide popularity of Python is its community of users. Launched in 1991, Python is one of the most popular programming languages in the world, occupying the top position in several programming language popularity indices, such as the TIOBE Index and the PYPL Index. Python is a general-purpose, open-source programming language used in various software domains, including data science, web development, and gaming. Now that we've established that Python and R are both good, popular choices, there are a few factors that may sway your decision one way or the other. What makes R and Python the perfect candidates for data science? In this article will cover what Python and R are used for, the key differences between R and Python, and provide some factors to consider to choose the right language for your needs. Rather than seeing the two languages as mutually exclusive, you should see them as complementary tools that you can use together depending on your specific use case. While this may be true for newcomers to the discipline, in the long run, you’ll likely need to learn both. The Python vs R debate may suggest that you have to choose either Python or R.

Both languages are well suited for any data science tasks you may think of. Python and R are the two most popular programming languages for data science. If you are in this situation, don’t panic: most data professionals were in your situation once. Plus, you may have already heard about the Python vs R debate, and you may need help deciding which one to learn. You will probably know by now that learning to code is a critical milestone for every aspiring data professional.

If you are reading this article, you are likely just at the beginning of your data science journey.

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