>> Calculus is certainly not important for CS majors. Compared to, say, linear algebra.

I dunno. I am trying hard to refrain from saying X is not important for C.S. because the nature of this site is that you are going to find a ton of C.S. people who point out that they would be crippled with the lack of X :-)

Having said that, I read Steve's article many many years ago and upon reflection, I have realized that Steve is way better in Math than he lets on. Some of the techniques there are designed not for you to "get" Math but to get some sort of cultural awareness of Math (which of course is a fun pursuit on its own). The reason why casual foraging of a ton of math has never worked for me when I started learning math is because one of the reasons Math is incredibly hard is that it is inherently a personal journey. Getting math sometimes requires working at it patiently for weeks before you finally understand how to wrap your brain (which really means making connections with how you think about something that you previously learned and this new math) around it. This requires hard work mostly in the form of rather mind numbing problems which at first glance can be boring and feel like you are being told to "wash on wash off" (to use a Karate Kid reference). However, the subconscious is a powerful tool and helps by developing weird connections. As I got more and more trained in math, I realized these connections became much much easier. I suspect this is why math researchers are able to look at paper and quickly grasp the intuition behind it.

Re: to get some sort of cultural awareness of Math, aka Liberal Arts degree in mathematics

The Princeton Companion to Mathematics is the best tool I know for this job. This is a bedtime reading, no problem solving needed. A bit like reading, say, Python Modules of the Week. You'd need to actually use that module in your code to "get" it, but just knowing that some module exists is very valuable. Same goes for math.

How the heck is linear algebra important for CS majors? Where in CS is linear algebra useful where calculus isn't?

Also graphics.

That said, it's absurd to make a dichotomy between the two. Both are essential. Maybe not at first glance at a simple CRUD app, but both get surprisingly handy once you want a bit more out of it.

Machine Learning, Graphics etc

On the other hand calculus is also used in machine learning...

Plus optimisation via calculus is sometimes a better choice then other cs specific methods.

And calculus is used in graphics.

Machine learning, for example.

Machine learning algorithms, at least the ones that I used, needs convex optimization to fully understand them which needs real analysis which is just calculus with more mathematical rigor.