Which Is Better For Data Science: Python or R?
If you are reading this, you are probably just starting your data science journey. You are undoubtedly well aware that learning to code is an essential step for every aspiring data professional. Also, you might already know Python vs. R controversy and need guidance in selecting which one to study. Don’t panic if you find yourself in this predicament; most data professionals have been there.
Programming For Data Science
The two most used programming languages for data science are Python and R. Any data science task you can imagine can be accomplished using either language. The Python vs. R debate could lead you to believe that you must select Python or R.
Although this might be the case for those who are new to the field, you’ll probably need to learn both in the long run. Instead of considering the two languages mutually exclusive, consider them complementing tools that you may combine based on your unique use case.
Although this might be the case for those who are new to the field, you’ll probably need to learn both in the long run. Instead of considering the two languages mutually exclusive, consider them complementing tools that you may combine based on your unique use case.
Why are R and Python the ideal programming languages for data science? This article will discuss the main similarities and differences between R and Python and some important considerations for selecting the best language for your needs. Before that, do have a look at the top Data Science certification course in Delhi, if you are tech-savvy and want to learn programming for data science.
Now that we’ve established that Python and R are both respectable, popular options, there are a few things to consider that might impact your decision.
Python for Data Science
Data science, web development, and gaming are just a few software industries that employ the open-source, general-purpose language Python.
Python, which was first released in 1991, currently holds the top position in several popularity indices for programming languages, including the TIOBE Index and the PYPL Index.
The Python user base is one of the factors contributing to the language’s widespread ubiquity. Python is supported by a sizable user and developer community that ensures the language’s seamless development and refinement and the ongoing release of new libraries made for various uses.
Python is a simple language to read and write because it resembles human language. In actuality, Python was created with excellent readability and interpretability in mind. For these reasons, Python is frequently mentioned as a programming language that beginners with no prior coding experience should start with.
Due to its versatility and the hundreds of specialized libraries and packages that enable all data science tasks, including data visualization, machine learning, and deep learning, Python has been steadily gaining prominence in the field of data science throughout time.
R for Data science
The open-source programming language R was developed primarily for statistical analysis and graphics.
R has been frequently used in academic settings and scientific research since its initial release in 1992. It continues to be one of the most often used analytics tools in both conventional data analytics and the quickly developing domains of business analytics today.
With R, created with statisticians in mind, you only need a few lines of code to use sophisticated functions. Several publicly available and simple-to-use statistical tests and models exist, including classifications, clustering, non-linear modeling, and linear modeling.
R’s large community is largely responsible for its wide range of possibilities. It has built one of the most comprehensive sets of data science-related programmes. They are all accessible through the Comprehensive R Archive Network (CRAN).
The capability of R to produce high-quality reports with support for data visualization and its available frameworks to develop interactive web apps are two further features that set it apart from other statistical software. R is usually regarded as the best tool for creating stunning graphs and visualizations in this sense.
Popularity of Python and R for Data Science
Python and R continue to reign supreme in data science, despite the recent growth of other programming languages like Julia.
The discrepancies are startling in terms of popularity, which is a tricky topic in general. R has continuously fallen short of Python, especially in recent years. Several popularity indices for programming languages place Python at the top. Python is widely used in many software disciplines, including data science, which explains why. R, on the other hand, is mostly used in academics, some industries, and data science. The choice is yours. If you are a beginner and want to learn both languages, check out theData Science course in Delhi, which offers special programming assistance for non-programmers.