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Introduction

In the domain of data science and machine learning, the first questions to crop up are often about the degree of support for two languages that have become popular in the data science world: R and Python. This research note explains the rise of R and Python and Ovum's view on the latter's future.

Highlights

  • The strength of R is that it has a well-established ecosystem of packages and libraries. Python became popular because the science and engineering communities embraced it as an alternative to Java: Java was not designed for numerical programming, whereas Python was a good language on which to build linear algebra and numerical analysis libraries.

Features and Benefits

  • Explains the history and popularity of the R programming language.
  • Offers Ovum's view on the future of Python.

Key questions answered

  • Which programming languages are suitable for data science and machine learning?
  • Are there other programming languages suited to data science and machine learning?

Table of contents

Ovum view

  • Summary
  • The rise of R
  • The rise of Python
  • The future of Python

Appendix

  • Author

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