I am an undergrad, and I am considering taking an optional course on Optimization Engineering next semester. In my engineering faculty, MatLab is the default programming language, and I have heard that the subject relies heavily on MatLab. I then assume that it must the Optimization Toolbox that will be used a lot. On the Mathworks page for the Optimization Toolbox, the list of features includes

The toolbox includes solvers for linear programming (LP), mixed-integer linear programming (MILP), quadratic programming (QP), second-order cone programming (SOCP), nonlinear programming (NLP), constrained linear least squares, nonlinear least squares, and nonlinear equations.

The Julia alternative seems to be JuMP.jl, with a very similar feature-list:

JuMP makes it easy to formulate and solve linear programming, semidefinite programming, integer programming, convex optimization, constrained nonlinear optimization, and related classes of optimization problems.

The lists seem very similar, but are clearly not identical. If you have experience with both the julia package JuMP.jl AND the Optimization Toolbox in MatLab, could you contrast them? Do you think that following the course using Julia will work fine?

For context, I have followed MatLab-based coursed through the Julia-equivalent packages in the domains of Control Sytems, and Digital Signal Processing, and found it to be no real obstacle.