GNU Octave: A high-level interactive language for numerical computations [pdf]

Overall I'm excited for Julia, but have a lot to learn. As far as Octave/Scilab type applications go, I find them useful for small systems where I need to make sure the math of what I'm doing is correct before writing it in something like Julia or Python + Numpy. Excel is also surprisingly good for the really simple stuff (Ex: inverting a small matrix and doing some matrix multiplications).

Anything involving matrix computation is great to do in python because of existing libraries (numpy). I think most people who are actually writing code (i.e. grad students) don't mind the syntax because they learned it in school starting in undergrad.

Octave's main appeal to me (and most people I work with) is being able to run existing MATLAB code. However, making it work for nontrivial MATLAB projects, is, well, nontrivial. It's also very slow.

Julia is the language I'm most excited about. It's fast [1] even though it has a slow startup time, and that's improving quickly. It's got a nice grammar-of-graphics-based plotting library [2] that's quickly catching up to R's ggplot2. I think it has potential to be the language of choice for non-neural-network stats/ML (that's python for the foreseeable future). I like the syntax a lot, too. The data types take some getting used to, not unlike R. I also think if Julia moves away from JIT compilation, it has potential to be the language of choice for deployed NN models (right now, that's C++).

As for plotting, R's ggplot2 beats MATLAB any day of the week for me. To each his own.

[1] https://julialang.org/benchmarks/

[2] https://github.com/GiovineItalia/Gadfly.jl

Should a university use something like Matlab/Octave or Python to teach numerical methods and related classes?

Matlab is usually only ~$20 for an engineering student and the language itself is mostly imperative with matrices as the default data type. Also, plotting and variable inspection are available as part of the default install.

Python is Object Oriented. While that is certainly better (well ...most would argue so) from a software engineering perspective, it is a bit of a hurdle for an engineering student with a little bit of C background. I can still remember most of my numerical methods homework assignments. Build this matrix, invert it, implement Simpson's method...blah blah. This is definitely possible in Python, but I would've had to figure out which libraries to import, which Python distribution to use (if you don't use Anaconda or WinPython, getting certain libraries to work is difficult), and a myriad of other problems.

In short, even though I like Python better, Matlab might have been a better choice by my teacher at the time which allowed us to focus on numerical methods and control systems rather than getting lost in the rich Python ecosystem which has a higher learning curve.

Does that mean Python couldn't have worked at all? No I don't buy that either, but think it requires a different approach. If I was the engineering Dean I would require a course called intro to scientific Python which is all about getting a good Anaconda distribution setup with the Spyder IDE and Jupyter Notebooks with some basic Python coding. Then, when it is time to take your numerical methods, digital signal processing, control systems...etc, the teacher can host Jupyter Notebooks that interactively explore the subject with LaTex, code-snippets, and graphs. There is a university somewhere where the professor has a full DSP book done via Jupyter Notebooks and it seems glorious.

2.) I've posted several comments on here including one showing that the cost can be up to $20k, so I'm well aware of that fact, but it is irrelevant to the above comment as the $20k is for working professionals and not poor engineering students where the cost is indeed ~$20 as I've paid it. Note that $20k is insane by some standards, but engineering software frequently gets much much higher than that. Yearly licenses in the 0.5 million range are common in my industry, so $20k is not considered unreasonable based on cost alone. It is unreasonable to me when they have free alternatives.

3.) I have said multiple times on here that I prefer Python and Julia as they are deeper and don't cost an arm and a leg. However, my above point still stands that Matlab isn't a bad solution for a student. There is some lock-in of course, but it isn't too bad to migrate away after you graduate and can no longer get the student rate.