Focus on data science. Python is a general-purpose language, and there is less focus on data analysis packages then in R. Nevertheless, there are very cool options for Python such as Pandas, ...

Huge win for R. In my book, The Art of R Programmming, I wrote "R is written by statisticians, for statisticians," a line I've been pleased to see quoted by others. One could update that to read "R is ...

Huge win for R. In my book, The Art of R Programmming, I wrote "R is written by statisticians, for statisticians," a line I've been pleased to see quoted by others. One could update that to read "R is ...

Python and R stand out as leading programming languages in the realm of data science. Both languages have their strengths and weaknesses, and choosing between them can be a difficult task. In this ...

Therefore, data science researchers should view R and Python as partners who may collaborate to provide the greatest outcomes rather than as competitors. The language that you feel most competent and ...

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. Hoping to ...

Python was not built specifically for data science workloads, but it does include many features that make it easy to code against data science workloads such as read-eval-print loops, notebooks and ...

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job. Among the many use cases Python covers, data analytics has become ...