R and Python, the “dynamic duo” of data science, are both free, open-source programming languages. That means that there’s no “vendor” in the sense that, say, Microsoft owns Excel. This can make getting started with these programs a little trickier: there are several ways to install them, often multi-step, confusing, and resource-intensive. 

It would be easy as a brand-new programmer to give up on tools that are so involved even to install — “If that’s hard, just imaging trying to use them!”

Fortunately, free cloud-based applications exist for you to experiment with these programs, no installation needed. This saves you disk space and headaches and allows you to dig into the code — and the possibilities — rather than the logistics. 

For R: RStudio Cloud

RStudio Cloud comes from RStudio, vendor of the predominant RStudio integrated development environment. (I use RStudio in teaching my R course.)

Simply create an RStudio account and get started. You can create a new project and run a session of RStudio from your browser. The code will execute on RStudio servers. 

Your initial workspace will look like the below. This is a “virtual” instance of the RStudio interface: 

If this is the first time you have worked in RStudio, check out my “Tour of RStudio” below.

To continue dabbling with R, check out my posts. Your R session will run just as it would on your computer, but this time RStudio takes care of the software. 

Ready to take the plunge into R? Get started with my course, R Explained for Excel Users.

For Python: Google Colaboratory

Google hosts the free Colaboratory service for running Python using a modified Jupyter notebook. The exact “look and feel” of Colab will not be the same as using a code editor like PyCharm (my favorite environment for working in Python) or even a “plain” Jupyter notebook, but the functionality is there, plus you don’t have to deal with maintaining the software and packages. 

To access Colab, log into your Google account and check out the Google Colab starter notebook, which includes the below video. 

Google Colab gives you direct access to Google’s supercomputers — you can do some pretty serious data on here, as the endorsement from TensorFlow suggests (that is a popular package for deep learning built by developers at Google). You can even execute on your Google Drive files entirely from the cloud. 

I don’t write as much about Python as I do R. If you would like an overview of the language, check out DataCamp’s free course, Introduction to Data Science in Python.

Conclusion: Get coding fast

Have you experimented with RStudio Cloud and Google Colab? Which seems more user-friendly to you? Are you more excited to learn R or Python?

Or, do you prefer a different free, online source for practicing R and/or Python? For example, I recently learned of Microsoft Azure Notebooks, which lets you practice both R and Python from Jupyter notebooks for free. 

Let’s talk in the comments.