And the Best Programming Language for Data Science goes to…

Questions you should ask while picking the best programming language for your Data Science project

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Most commonly used programming languages for Data Science

Python and R are the most widely used languages among others( for example, Java, Scala, Matlab) for statistical analysis or machine learning-centric projects.


Python Logo


R programming language logo

4 Questions to learn about the BEST suited language for your project!

So, how does one make the right choice for their work at hand?

1. Which language/framework is preferred in your organisation/industry?

Depending on the industry you are working in and the most commonly used language by your peers and competitors, you might want to speak the same language. Here is an analysis carried out by David Robinson(Data Scientist), it’s a reflection of the popularity of R in an industry and you can see that R is outstandingly being used in Academia and Healthcare.


2. What is the scope of your project?

This is an important question because before you pick up a language, you must have an agenda for your project, the extent to which you want to work over it.

3. How experienced are you in the field of data science?

For a beginner in data science who has limited familiarity with statistics and mathematical concepts, Python might turn out to be a better choice because it lets you code the fragments of an algorithm with ease.

4. How much time do you have at hand/cost of learning?

The amount of time you can invest makes another case for your choice. Depending on your experience with programming and the delivery time of your project, you might choose one language over another to get started in the field.


In a nutshell, the gap between the capabilities of R and Python is getting narrower. Most of the jobs can be done by both languages. And both have rich ecosystems to support you.

  • The domain of the project at hand and the extent of statistical or scientific processing required.
  • The future scope of your project.
  • The language/framework that is most widely supported in your teams, organisation, and industry.

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  • Explained Mathematics and derivations of why we do what we do in ML and Deep Learning.
  • Podcasts with Data Scientists and Engineers at Google, Microsoft, Amazon, etc, and CEOs of big data-driven companies.
  • Projects and instructions to implement the topics learned so far.

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