Discreteness introduces discontinuities and errors, and it's usually possible to describe the errors analytically. But there are situations where discrete systems become numerically unstable and blow up while the smooth analytic equivalent has no problems.
I did not intend to imply that the world is discrete in the strictest sense. Just that, except for AI/ML, discrete math will prove much more helpful to understanding the concepts and material presented in a graduate CS curriculum.
The benefit studying continuous maths provides in the context of CS is the rigor and modeling skills one gains.
All of my thesis is rooted in Programming Languages, Compilers, and Type Theory. Continuous math is utterly useless in this context. It's all SAT/SMT, set theory, and graphs -- all of which are topics in discrete math.
When I started my undergrad CS program in 1989 it required 4 semesters (2 full years) of calculus. This was in addition to 4 semesters of physics.
Maybe I'm just not up to date on what "Computer Science" is today.
Erwin Schrodinger would like a word with you.
Now, I will say, save machine learning/AI, calculus isn't really necessary; the world is completely discrete.
That being said, that doesn't mean that knowing calculus wouldn't _enhance_ your ability to understand and digest some of the more difficult reductions and proofs in, say, a theory of computation course.
I relied on "The Calculus Tutoring Handbook"[0]. I wanted a book that had answers to _all_ the exercises for confidence building purposes. The book goes slow and provides a great amount of detail -- the authors are pretty good at not hand-waving.
I also found \r\learnmath useful as a "I have a problem and can't ask anyone" site. They are really friendly.
[0] https://www.amazon.com/Calculus-Tutoring-Book-Carol-Ash/dp/0...