At Springboard, we pair mentors with learners in data science. We often get questions about whether to use Python or R – and we’ve come to a conclusion thanks to insight from our community of ...

Python and many of its popular data science and machine learning packages/libraries, such as NumPy and TensorFlow, are open source projects.

Most people aren’t writing Python scripts, to be clear. But Python has made it much easier for average people to do data science, which is one of the biggest reasons for its success in data science.

Python is a popular general-purpose language, but it's increasingly favored for statistics, data analysis, and data science. If you have a basic knowledge of statistics, how can you apply that to ...

That jibes with what RStudio has seen in the data science community. “We see both [R and Python] as powerful, both with unique strengths and options,” Bajuk says. “Both help drive data science ...

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: ...

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.

Because of its simplicity, Python is the language of choice for creating UDFs in the data science community. In fact, the Tuplex team cites a recent poll showing that 66% of data platform users ...