Which programming language is best for data analysis?

The preference & usage of programming languages depend largely on the use-cases to which it is applied. While the financial services prefer SAS over other technologies, other businesses prefer Python, R over SAS. As per a survey conducted by Kaggle, Python, SQL & R are the leading programming languages preferred by data analyst. Research also says that 3 out of 4 data scientists prefer to learn Python. Therefore, with a certain level of subjectivity, Python can be considered as the best programming language for data analysis.

If you’re asking because you want to know what one you should learn, learn Python. It’ll open all of the same doors for you that R will, and then some. You’ll probably want to learn SQL too.

The more complicated answer is that it entirely depends on the types of data science problems that you are working on.

  • Python is more powerful than R in environments where models are integrated into production; however, there are Python packages for using R syntax and packages in Python if one was so inclined.
  • Python handles big data sets better than R, but that gap has been closing.
  • R has a much larger repository of analytics packages than Python does and thus you don’t have to “re-invent the wheel” nearly as much as you sometimes have to do in Python, although Python’s continues to increase in size.
  • Rstudio is leaps and bounds ahead of the functionality of Juypter, and you can generate very sophisticated reports and presentations using Rmarkdown
  • Both languages are supported by AWS EMR (Amazon Web Services Elastic Map Reduce) so either will allow you to implement models in a map reduce framework.