The Ultimate Comparison of Python vs R for Data Science

  • keerthika ravichandran

Python and R are the most advanced data science-oriented programming languages. Of course, learning them both is the best course of action. Due to the enormous rise in the importance of big data in the software industry.

Python and R are the most advanced data science-oriented programming languages. Of course, learning them both is the best course of action. Due to the enormous rise in the importance of big data in the software industry, R and Python have become two of the most popular programming languages for data scientists and data analysts.
It is challenging for the developer to select just one of these because they are both similar to one another while also being distinct in their own right. For example, although R is more frequently used for statistical modelling and data analysis, Python is also used for developing web applications.
What is R programming?
Statistics and data scientists frequently use the statistical modeling language R. It offers support for various statistical packages, the most popular of which is used for data modeling and analysis. R can be challenging for new users and those who lack the necessary statistical knowledge. R is a tool for putting statistical learning into practice. It is a way of expressing how the users' statistical learning is defined. It might not, therefore, be the best programming tool for beginners.
What is Python?
Python is a well-known programming language that we employ for both the creation of web applications and the performance of data science operations. Python offers many libraries that are appealing to both programmers and data scientists. Python offers a variety of libraries, including matplotlib, seaborn, tensorflow, scikit-learn, and other crucial tools needed for processing data science. Additionally, it offers additional tools like Flask, SQLite support, and other features that can result in a comprehensive data product.
The difference between R and Python:
  • Python has a wide range of applications, including product deployment, data analysis, visualization, and data forecasting. The sole focus of R, on the other hand, is statistical modeling and analytics.
  • R is primarily used by academics and R&D institutions, whereas software engineers and businesses use Python.
  • Beginners who want to learn about programming and data science should use Python. However, statistics have a challenging learning curve and are not recommended for complete beginners.
  • Python uses PyPi, which contains all of the necessary Python packages. Contrarily, R makes use of the CRAN repository (Comprehensive R Archive Network). Users can use and contribute to the thousands of libraries and packages that are included in it.
  • While R has much more user-friendly and interactive visualization libraries like ggplot2, Python has its own visualization libraries like matplotlib and seaborn.
  • R data scientists make less money than Python-using data scientists. R-based data scientists typically make 90,000 dollars per year, while Python-based data scientists make around 100,000 dollars per year. However, the pay for data scientists who use both R and Python is significantly higher at $117,345.
  • R programmers were more prevalent up until 2016; however, as Python gained popularity in the data science community, the number of R programmers began to decline.
  • Python offers a variety of tools and support for debugging and code testing. R, however, lacks some of these robust programming features.
  • Python doesn't have as many libraries as R does. Every field that uses statistics, from medicine to astronomy, has packages available in R.
  • Last but not least, Python has a larger community base in terms of support. R does not enjoy the same level of popular backing.
We can conclude that Python and R are both widely used data science tools. However, the user's applications and preference for either Python or R will determine which language they use. The two programming languages differ significantly in several important ways. Additionally, before making a decision, a user should calibrate their experience with programming tools.
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