It's hard to know whether to use Python or R for data analysis. And that’s especially true if you're a newbie data analyst looking for the right language to start with.

But it is possible to figure out the strengths and weaknesses of both languages. One language isn’t better than the other—it all depends on your use case and the questions you’re trying to answer: What should I use for machine learning? I need a fast solution, so should I use Python or R? Python vs. R for Data Analysis At DataCamp, we often get emails from learners asking whether they should use Python or R when performing their day-to-day data analysis tasks. Both Python and R are among the most popular languages for data analysis, and each has its supporters and opponents. While Python is often praised for being a general-purpose language with an easy-to-understand syntax, R's functionality was developed with statisticians in mind, thereby giving it field-specific advantages such as great features for data visualization.

Our infographic "When Should I Use Python vs. R?" is for anyone interested in how these two programming languages compare to each other from a data science and analytics perspective, including their unique strengths and weaknesses. Click the image below to download the infographic and access the embedded links.

For additional insight into these languages, read our blog post on Python vs. R for Data Science: What’s the Difference? To explore these languages for yourself, try our Introduction to Python and Introduction to R courses.