R vs Python: Which is best for Data Science?

The world is estimated to generate 463 exabytes of data daily by 2025. As technology advances and global data increases exponentially, the demand for data scientists and data analysts trained in utilising these data resources explodes across all industries!


Data science is an extremely exciting field to work in. It combines advanced statistical, quantitative and Artificial Intelligence (AI) skills to drive data-driven business decisions and increase efficiency. Being proficient in a programming language for data analysis is also essential for jobs of the future.


The two most popular programming tools for data science right now are R and Python. They are arguably some of the most flexible programming languages, with R being used specifically for statistical analysis and Python originally used for general-purpose programming work with data science tools added later. They are absolutely essential for anyone that will work with large datasets, machine learning and data visualisation. Now let us get into the pros and cons of each language to find out which is the best for data science!


R

R is probably one of the most widely used languages for statistical computing and graphical visualisation. It provides a wide variety of statistical and graphical techniques such as time series, clustering, classification, and linear and non-linear modelling. In other words, the key difference between R and the other programming languages for statistical analysis is the fantastic analytical and visualisation tools in R that make it easy to present findings.


Python

Python was originally created as an object-oriented programming language with a focus on code readability and efficiency. Years ago, Python did not have many data analysis and machine learning libraries. As it gained popularity, it expanded rapidly and now provides a great API for machine learning and AI. Compared to R, Python makes replicability and accessibility a lot easier.

Learning Curve

R

The difficulty of learning usually depends on the individual, but can be estimated. According to an online poll from Visualising Data, out of about 2000 votes, 59% consider R to be more accessible to beginners without any programming background.


In other words, it may be easier to learn R if you do not have prior coding experience. This is because of the simplicity of creating statistical models, high functionality, and e