I imagine a language with decent support for macros would be much much more ergonomic for symbolic computation. Even ignoring all the other pros & cons, just this one reason would push one strongly towards Julia, Lisp(s), Mathematica, etc for symbolic computation.

Sympy is a poor tool to learn because it simply doesn’t scale to problems one most often encounters, even in schooling. Frankly, CAS are so general and unintelligent that problems with well known and elegant closed-form solutions, when presented to a system like sympy, result in an output which is often not even human readable - thousands of algebraic terms for instance to describe the equations of motion for a simple double pendulum.

Personally I have found that the best tools are 1) a firm grasp of elementary calculus (differential, series, and integral) and 2) exposure to simple numerical methods that apply to a broad range of problems.

Armed with this knowledge, in my opinion, Julia is a far superior language to python and its package ecosystem has a brighter future. Indeed, the language has been built with a focus on mathematical modeling and efficient numerical computation. It is a more natural starting point for those with an interest in mathematical modeling and engineering science, and will serve anyone who learns it better than python+numpy+sympy+scipy.

And learn C and C++ in the process, because they will need them for "Python" libraries.

Great. Thanks. Any suggestions for a book or course that uses sympy to teach calculus( svc)?