Why would you waste your time re-inventing a wheel.
A good data scientist isn't good because he/she can ace shitty trivia, he/she is good because they know the right question to ask.
In those situations math isn't "shitty trivia," but instead a tool to be leveraged against those hard questions.
You can consider the derivation of SVD to be shitty trivia while throwing np.linalg.svd around while engineering features. That's fine! Good luck visualizing that data in a meaningful way, or dealing with non-linear data, if you're ignoring that "shitty trivia."
What is non-linear data?
That is to say problems that can't be expressed by linear functions.
I.e. Y= mx + B is a linear function.
Y= ax^2 + bx + C is a polynomial (non linear) function.
Linear Programming (LP) involves solving a series of linear equations (something like Excel's Solver can do this).
When you are dealing with non linear functions you need to use a method such as Sequential Quadratic Programming (SQP).
— Stanislaw Ulam