Python Vs R: A Strong Comparison For Data Science Languages

Python Vs R

A Strong Comparison For Data Science Languages

1. Comparison Of Two Strong Data Science Programming Languages

Two strong programming languages are often pitting against each other to find out which is best among both for data science and analysis of data. Both are well-known; however, python is widely used while learning the programming language. In fact, data science is a particular domain, that’s why python is going to be most prevalent language across the world, R has also a great significance and has its own advantages. 

Davis, professor of computer science from University of California has published a detailed relation among the key metrics that also includes elegance, library ecosystems as well as level of difficulty to learn the language.

2. Python Vs R Comparison By Editor In Chief

Matloff is writer of four book regarding R, he is also editor in chief of the R Journal, therefore, he is biased towards R instead of Python. He also claims that the analysis conducted by him will be “fair and helpful”. Therefore, he argues against Python that if a person learns to implement Python for data science and analysis it needs extra effort. There is need to make your hands dirty by all the relevant libraries of python that deals with data science like Numpy, Panfdas, Scipy, Seaborn Matplotlib, and ScienceKit libraries that makes the Python strong. Whereas, R is only built for data analysis and statistical computing and all the features are built in the language. So, a novice user can perform data analysis in few minutes.

3. Python Vs R Package Comparison

Currently Python Package Index has more then 183,000projects, R packages are also available at Comprehensive R Archive Network where about 14385 packages are online. 

From the above discussion it is much easy to understand that R is really much obliging language for statistical analysis. However, Python a general-purpose language has much readable syntax and convenient for data science approaches. Both the languages are state-of-the-art and oriented towards data science.