Why did we replace our R intro courses?

We’ve just revamped and relaunched our introductory R programming classes, which begs the question: what was wrong with the old courses?

The short answer is that they didn’t reflect the most modern and up-to-date approach to working with R for data science.

Introducing the Tidyverse

One of the biggest reasons for the change is that we wanted to introduce students to Tidyverse tools earlier. As R has grown in popularity in the field of data science, it has become increasingly clear that most data scientists favor using Tidyverse over base R. We also think introducing Tidyverse earlier is better for new learners. It allows students to get up and running with a powerful set of tools quite quickly—hands-on learning is important!—and it’s also more readable than base R for most beginners.

Given all of that, we felt it was important to give our R students an earlier and more thorough introduction to the Tidyverse than the old courses provided.

Production-ready R

The older courses also weren’t written by an R native, and they included a lot of content that, while technically correct, didn’t reflect the modern workflows and best practices associated with production-ready R today. For example, the old course exercises frequently asked students to save results as variables and print them in the console. That’s not a typical R workflow, both because having too many objects clutters up the workspace and because in R, simply typing an object name in the console prints its contents automatically anyway.

There were also a lot of smaller changes we wanted to make to the old courses, like improving some of the writing and visuals to make everything simpler and clearer.

At Dataquest, our goal is to help you learn the up-to-date data science programming skills and the workflows and best practices you need to get hired. We felt that the old courses simply didn’t meet that standard anymore, and that’s why we’ve replaced them with revamped courses that better reflect the real tools and workflows used by modern R data scientists.

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