The lectures, instructions, and projects for this course are designed for the Linux operating system (Ubuntu 20.04 LTS Linux, to be precise -- there are many flavors/versions!).
Rather than replace Windows or Mac OS X on your computer, it will be easier to rent a virtual machine (VM) in the cloud, which you will then connect to remotely. Knowing how to create virtual machines is an important data-science skill because it makes your analysis more reproducible -- if your code works in your virtual machine, and other people know how to reconstruct a similar virtual machine for themselves (with the same operating system and programs installed), they're more likely to be able to reproduce the same results by running your code.
At the low-end, renting a VM costs about $10-20/month. Fortunately, the major cloud providers often provide free credit for students and new users, so you'll likely pay little or (hopefully) nothing this semester.
We provide directions for two major cloud providers: GCP (Google's cloud) and Azure (Microsoft's cloud). If you want to find another way/place to use Ubuntu 20.04 LTS and install Jupyter, that's fine too, and you can skip this lab (though we will only support the first two options during office hours).
Choose one of the following for the remainder of this lab:
-
GCP Directions. Google gave me educational credits ($50/student) to use for CS 320, which should cover you. This is the preferred option.
-
Azure Directions. At the time these directions were written, Microsoft offers students $100/year of credit per year. You can use this if there is an issue with option 1 (or you somehow run out of the $50 of credits before the end of the year).