In the war of Data Science tools, both R and Python have their own sets of pros and cons. Selecting one over the other should be done on the basis of certain criteria or attributes: Availability/Cost: ...

They found that, contrary to the rumors of R’s demise, that R remains an integral part of the data science processes for its customers and prospects. In fact, Qubole concluded that organizations would ...

But data science is a specific field, so while Python is emerging as the most popular language in the world, R still has its place and has advantages for those doing data analysis.

Data science and machine learning professionals have driven adoption of the Python programming language, but data science and machine learning are still lacking key tools in business and has room ...

In contrast, Python follows a multiprogramming paradigm, which makes it easy for developers to write concise code using syntactic sugar. Python was not built specifically for data science workloads, ...