One of the reasons is because you can write a fast and scalable simulation directly in Julia. Something like Python will inhibit the speed of loops, scalarized

One of the reasons is because you can write a fast and scalable simulation directly in Julia. Something like Python will inhibit the speed of loops, scalarized nonlinear functions (like in ODEs), and multithreading. You could try to run an introduction to computational thinking (which is essentially introduction to computational science and introduction to programming at the same time) in C++ + Python, but then you spend half of the course teaching students how to handle C++ and how to link all of their compilation tools, and all of that is time not spent on the actual topic of the course. At one point I did this in a numerical analysis course, and it just detracts from the main message. But with Julia, the whole course is up and running on day one, and you can start with building bifurcation plots or agent-based models which then grow in complexity without having to rewrite for performance. Since these computational science courses always to try poke at studies of complexity, this is very essential.