In this post you will:
- Install Julia [will take approx. 10 minutes]
- Instantiate the Julia kernel from within a Jupyter notebook (using Anaconda Navigator + Jupyter Lab) [~3 minutes]
- Run Ordinary Least Squares Regression [~5 minutes]
- Prerequisites: The following posts may be good to review before this one:
Julia is an exciting new(ish) computer language. I am most interested in its high speed nature. A few online resources report that it is orders of magnitude computationally faster than python. Also, it is a higher-level language making it easier to learn than something like C or C++. You can read about other valuable aspects here: Julia homepage.
The following video goes over the basic installation of Julia on a local (windows) machine. The packages downloaded and used by Julia can be very large (gigabytes!). I wanted to be able to change the “DEPOT_PATH” so that the packages get stored on my beefier “D:/” drive rather than my smaller, but faster, “C:/” drive. So, some of the video deals with that 🙂
First Operations with Julia
The following video shows how to start the Julia kernel from within a Jupyter notebook (using Anaconda Navigator) and do a few basic operations. We will run an ordinary least squares regression (more info: GLM).
Go back to Volume 2: Practice.