Physics is experimental model building of phenomena which are not yet understood and are being explored.
Engineering is experimental model building of phenomena which are mostly understood, albeit sometimes with some quirks and unexpected edge cases.
Applied math is the toolset used in both physics and engineering.
Pure math is the abstract and philosophical exploration of symbolic relationships within all of math.
Academic CS - Wirth and Dijkstra-style - is the tiny subset of pure math used to explore theories of computing.
Practical CS is mostly just relatively trivial puzzle solving using a combination of cookbook academic CS with a bit of invention and innovation with influences from user psychology, marketing, and business design.
The most academic and mathematical parts of practical CS is ML and AI, which are genuinely exploratory. The second most academic part is probably processor architecture, where you may be applying statistical modelling to cache design and instruction pipeline outcomes.
Most of the rest is pretty basic compared to engineering modelling - never mind academic physics.
Physics is experimental model building of phenomena which are not yet understood and are being explored.
Engineering is experimental model building of phenomena which are mostly understood, albeit sometimes with some quirks and unexpected edge cases.
Applied math is the toolset used in both physics and engineering.
Pure math is the abstract and philosophical exploration of symbolic relationships within all of math.
Academic CS - Wirth and Dijkstra-style - is the tiny subset of pure math used to explore theories of computing.
Practical CS is mostly just relatively trivial puzzle solving using a combination of cookbook academic CS with a bit of invention and innovation with influences from user psychology, marketing, and business design.
The most academic and mathematical parts of practical CS is ML and AI, which are genuinely exploratory. The second most academic part is probably processor architecture, where you may be applying statistical modelling to cache design and instruction pipeline outcomes.
Most of the rest is pretty basic compared to engineering modelling - never mind academic physics.