Exciting news! We know that R is one of the most important programming languages for anyone who wants to learn data analysis and data science. That’s why we’ve just launched four new R courses — a complete revamp of the first step in our Data Analyst in R career path!
These four new courses are designed to make it easier for you to start learning R from scratch and help you build a better foundation in R programming.
As with all of our courses, they’re also designed to keep you motivated by getting you working hands-on, writing real code and working with real data from day one.
The four new courses are:
The new courses also introduce new guided projects to help you synthesize your new R skills by building real data analysis projects as you learn.
We’ll help you get a local R environment set up on your machine, and then guide you through projects analyzing COVID-19 trend data and looking at books sales data to glean insights about marketing campaigns and the impact reviews have on sales.
In fact, we’re so excited about these new R programming courses and data analysis projects that we’re doing something we’ve never done before:
Learn R for FREE: July 20-27
For a full week, every course in our Data Analyst in R path will be free. This includes the four new courses and all of our existing R courses, including courses on SQL, statistics, and probability.
For one full week, the paywall is completely down, and there are no restrictions! You can complete as many courses as you like, and you’ll earn certificates for each course you complete, just as a subscriber would.
If you haven’t tried working with R before, or you haven’t tried Dataquest before, there’s no better time to try us out! Sign up now — the new courses are already free as a preview, so you don’t have to wait to get started!
I needed a resource for beginners; something to walk me through the basics with clear, detailed instructions. That is exactly what I got in Dataquest’s Introduction to R course.
Because of Dataquest, I started graduate school with a strong foundation in R, which I use every day while working with data.
Ryan Quinn – Doctoral Student at Boston University
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
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.
Charlie is a student of data science, and also a content marketer at Dataquest. In his free time, he’s learning to mountain bike and making videos about it.