Which Programming Language is Best for Data Science? R or Python

According to Stack Overflow’s annual developer 2019 survey, Python slithers up in being the most popular programming language. Python ranks to be the top-most wanted programming language reveals the survey.

Python and R are both open-source programming languages used by data scientists. Both these programming languages emerged as favorites of data science professionals. R and Python are similar yet differ in their ways which makes it difficult for developers and data scientists to choose between them.

While R is majorly used for statistical modeling and Python is used for web application development and data analysis as well. Most statisticians prefer R as it possesses an extensive catalog for graphical methods. Python performs pretty much the same work as R and is preferred by data scientists. Being a data science professional learningboth R and Python is an ideal solution.

R vs Python is often a topic of debate by most data scientists since both these languages are useful for data analysis. Nonetheless, let us move ahead and discuss more on R and Python and have a look at what best suits you.

  • Statistical accuracy

The winner is R

A data scientist who has chosen Python to work with machine learning often fail on understanding the statistical issues involved. Whereas R is basically written by statisticians and for statisticians.

  • Machine learning

The winner is Python

The rise of machine learning and artificial intelligence has triggered the growth of Python. Python libraries come with a set of fine-tuned libraries used for image recognition. The power comes from setting certain image-smoothing ops that can further be implemented in R’s Kera wrapper for which pure version of TensorFlow can easily be developed.

  • Learning curve

The winner is R

Data science professional working with Python need to learn NumPy, Pandas, matpotlib, matrix types and basic graphics that are built within R to have a better understanding of R programming. With the help of R, the professional might be able to do simple data analyses within minutes. Working with Python libraries can be a little tricky, even for tech professionals.

  • Elegance

The winner is Python

Although it is a subjective topic, Python reduces the usage of parentheses making it smoother when coding.

  • Unity of language

The winner is Python

Python no doubt has transitioned from 2.7X to 3X, however, this will not cause much disruption. And due to the impact of RStudio, R has now changed into two different dialects.

  • Linked data structures

The winner is Python

Data structures such as binary trees can easily be implemented in Python. Although it can be done in R, it is comparatively slower in process.

Python covers multiple areas such as data analysis, product deployment, data prediction, and data visualization while R is solely used for statistical modeling and data analytics. Besides this, Python emerged to be a programming language used by tech professionals and R is mainly used by R&D institutes and academicians. No doubt Python is beginner-friendly, no wonder why several data science professionals preferred learning Python.

There are several differences between both these programming languages. However, based on the professionals’ caliber with R and Python one should choose their choice of tools.

Data science is everywhere, however breaking into the data science industry is not a piece of cake.


In a nutshell, both R and Python programming are essential tools for data science.

However, the statistical gap between R and Python seem to get closer. Most work can be done by both these programming languages. However, as a data scientist, it is better to master both R and Python. In the end, it depends on the objectives of your project and the tool your company uses.

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