Julia is a rather new programming language for technical or scientific computing. You will find out a lot more about it on the Julia homepage. Unfortunately, there is not a lot of tutorials on Julia out there and if you do find them, most are by computer scientist for computer scientists. Perhaps rightly so, as Julia is a fantastic tool, capable of some pretty impressive things when it comes to scientific computing. It prides itself on being as simple and easy to use as Python, with speeds approaching that of C or Fortran. It is indeed much speedier than other mathematical languages such as Matlab® and Mathematica®.
On top of this, I believe that it makes for an excellent language for a novice starting off, learning how to code. This is especially true for those who plan to go into the fields of science and technology. Even if you move on to other languages, Julia will stand you in good stead. It might spoil you, though, which means you’ll come running straight back to it.
I do stick to IPython for my medical statistics, but Julia works perfectly here too. I’ve made a lecture on the topic, which you can view here.
Go on, give Julia a spin. There is just something about it that speaks to me. A certain elegance and power. Well done to the brilliant minds that came up with it and to all those who are continuing its development.
You can write Julia code in the cloud using JuliaBox, so no need to install anything at all. At this time, I am having tremendous problems getting it (IJulia) to run in Jupyter, so much so that I am using the very nice Juno development environment. In upcoming lessons I will look at installing Julia, Jupyter, and Juno, but for now, you can follow along without any downloads or installs. Just use JuliaBox and your Google® account to sign in. The notebook files that I use are in a zip file on this page.