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Phyton v/s R for all computer geeks!

  • Python was originally developed as a programming language for software development (the data science tools were added later), so people with a computer science or software development background might feel more comfortable using it.
  • Accordingly, the transition from other popular programming languages like Java or C++ to Python is easier than the transition from those languages to R.
  • R has a set of packages known as the Tidyverse, which provides powerful yet easy-to-learn tools for importing, manipulating, visualizing, and reporting on data. Using these tools, people without any programming or data science experience (at least anecdotally) can become productive more quickly than in Python.
  • If you want to test this for yourself, try taking Introduction to the Tidyverse, which introduces R’s dplyr and ggplot2 packages. It will likely be easier to pick up on than Introduction to Data Science in Python, but why not see for yourself what you prefer?
  • Overall, if you or your employees don’t have a data science or programming background, R might make more sense.

Wrapping up, though it may be hard to know whether to use Python or R for data analysis, both are great options. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer. Finally, I’ll share the first bit of this handy infographic comparing the two languages. I don’t want to include it all as it’s very long and would require too much scrolling, but you can download the full image here.