What do you want to become :

Perfect Data Analyst or,
Full Stack Data Scientist ?
First, understand the difference between the jobs here. If you just want to become a Data Analyst with few concepts of machine learning as well, then you can just go with R Programming without any second thought. But if you want to become a full stack data scientist with deep knowledge of machine learning and deep learning and then how to deploy them or integrate your models into some web or desktop applications then go for Python programming.

In this blog we will learn :

R Programming Definition
Python Definition
Usability
Popularity Comparison
Jobs and Salary Comparison
Features Comparison
Libraries and Packages
Syntax Comparison
R Programming
R is one of the most popular languages in data science. But we need to understand the usage and limitations of it. R programming is designed in such a way that even a non-programmer can easily understand it. R is a programming language as well as it is considered as a tool for data analysis. R is mainly designed to fulfill your statistical needs. It is primarily used in academics and research and it is the best tool/programming to perform EDA (Exploratory Data Analysis). It could be used in finance, marketing, media, etc.

Python Programming
Python has become one of the most popular languages in data science as well as in other domains like web development or security. Python is considered as a general-purpose language, it means you can use Python for multiple tasks like software development, web development, networking or security, and data science. Using Python programming you can explore a whole new level of data science. Machine Learning and Deep learning could be easily done using python.

Read Full Article Here – https://brain-mentors.com/r-programming-vs-python-programming/

Author's Bio: 

BRAIN MENTORS Pvt. Ltd. started with a mission to link the IT industry and educational institutions. We aim to transform our every student into an IT professional who is ready to be employed in the industry.