Training your staff in data science? Here’s how to pick the right programming language

Kaggle’s survey revealed that while Python was more popular overall, R was more popular among statisticians.

R can be a good choice because it has been purpose-built for statistical analysis.

This means that many common statistical tools are built into R (whereas Python relies more on external packages).

Traditionally, R has also had a bigger range of statistical and specialist visualisation packages.

However, as Python has become more popular, it has started to catch up.

These days, pretty much anything you can do in R can be easily replicated in Python.


OK, I think I’ve made my decision.

What if it turns out I’ve made a terrible mistake?Don’t panic.

As you might have noticed, the Kaggle survey revealed that Python was used by 76.

3% respondents and R by 59.

2% — so there’s a huge overlap among the community.

Most data scientists are comfortable using both tools, even if they have an opinionated preference for one.

Learning either language will make picking up the other far simpler.

Don’t forget, there’s a lot more to learn beyond the programming itself— from maths and statistical concepts, to data pipelines and visualisation.

Either language makes an excellent base to explore those topics, and begin your journey as a data scientist.

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