As someone who is still annoyed that their degree program (a non-CS engineering) had them use Matlab instead of Python, I find it hard to disagree with those points. I will say that I definitely enjoy the 1 based array indexing.
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.
Guess what, apparently the first hit is not always free. There are people posting in this thread who have to pay as much as 20k per year for a fully licensed Matlab.
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.
I can't stress enough that what you buy with MATLAB is license coverage. Octave and Julia hook into many of the exact same libraries, but they have not negotiated a commercial license for things like linear system solvers. That means that distributed binaries may be subject to things like the GPL. If that's what you want, great. I like the GPL for a certain kind of software delivered to a certain kind of clientele. However, it's a nonstarter for most of my clients, so they buy MATLAB.