Learn R for Data Analysis in our New Beta Path

With new courses coming soon, it’s time for us to reaffirm our commitment to helping students learn R by relaunching our Data Analyst in R path.

What’s in the Data Analyst in R Path, and What’s Coming

Right now, you can access the first four courses of Data Analyst in R for free:

• Introduction to R Programming
• Intermediate R Programming
• Data Visualization in R
• Data Cleaning in R

The Data Visualization and Data Cleaning courses will ultimately require a basic subscription, but in their beta form they’re accessible for free because we’re still working on improving the answer-checking experience in those courses.

The first two courses will remain completely free even after the beta period ends.

In the next few months, we’ll be launching three additional new R courses, which will be available to users with a Basic Dataquest subscription:

• SQL Fundamentals for R Users
• SQL Intermediate for R Users
• Statistics Fundamentals in R

We’ll continue launching R courses throughout the rest of the year, with courses on APIs and web scraping, advanced data cleaning, and several more advanced statistics courses all set to release within the year.

Reminder: This is a Beta

At Dataquest, “beta” isn’t a marketing term, it’s a genuine opportunity for us to iterate on our courses based on student feedback and completion data. We’ll keep you informed about the changes we’re making, but if you do enroll in a beta course, you should be aware that you may encounter bugs, and you may see changes in the course materials from week to week as we tweak each mission to make it clearer, more effective, and more fun!

How We Teach

Dataquest is different from other online education platforms you may have tried. One of the biggest differences you’ll notice is that we don’t teach with videos. We’ve written about some of the science behind this before, but here’s the short version: students who learn hands-on simply perform better than students who learn from video.

All of our courses, including our R courses, are presented like this: a text window on one side that introduces a new concept, and a coding window on the other side where you can immediately experiment and apply what you’ve learned. This short feedback loop of learning a little bit, applying it, adding a bit more, applying that, and so on, is a core part of our learning platform, and we believe this is the most effective way to teach and learn R.

We want to teach students real-world job skills, which is why we aim to teach the tools data analysts are actually using in the real world. In our R courses, that means getting students comfortable with RStudio , the industry standard tool for working with R.

We know that motivation is also important, which is why all of our courses will get you working with real-world data and doing real data science tasks almost immediately in our first course. Subsequent courses all make use of new and interesting data sets and ask you to solve real-world data analytics problems while you’re learning the programming skills.

When you reach the end of each course, you’ll be asked to synthesize what you’ve learned by undertaking one of our guided projects. These are data science projects designed to help you practice your new skills even as you start to build up your data science portfolio. And while our instructions will help point you in the right direction if you get lost, guided projects are designed to be open-ended, so you can make them completely your own, and take them as far as you’d like.

Why Learn R?

Although Python is a popular data science language, R is also increasingly popular . Either language is a great option for learning data science (here’s a head-to-head comparison of how they handle data science tasks), but learning R will open up a variety of data science positions to you whether or not you’ve already learned some Python.

Almost all of the top tech companies hire R users for data analytics and data science. And because R was originally designed with advanced statistics in mind, some basic data analytics and statistical operations are simpler in R than they are in Python. R also has a very welcoming and helpful online community (using #rstats on Twitter), and some really great open-source packages and libraries for data science (including tidyverse packages like ggplot2 and dplyr ).

Everyone who works in data can benefit from learning some R, and with our Data Analyst in R path, it’s now easier than ever to get started. Start learning one of the fastest-growing languages in data science right now, and in five minutes you’ll have written your first R code and be on your way to learning R.