Both Python and R are popular programming languages for Data Science. While R’s functionality is developed with statisticians in mind (think of R's strong data visualization capabilities!), Python is often praised for its easy-to-understand syntax. Ross Ihaka and Robert Gentleman created the open-source language R in 1995 as an implementation of the S programming language. The purpose was to develop a language that focused on delivering a better and more user-friendly way to do data analysis, statistics and graphical models. Python was created by Guido Van Rossem in 1991 and emphasizes productivity and code readability. Programmers that want to delve into data analysis or apply statistical techniques are some of the main users of Python for statistical purposes. As a data scientist it’s your job to pick the language that best fits the needs. Some questions that can help you:
What problems do you want to solve?
What are the net costs for learning a language?
What are the commonly used tools in your field?
What are the other available tools and how do these relate to the commonly used tools