The Whole Machine at Once
The payoff of studying a computer part by part is being able to reason about the whole system for a real task — to follow information as it flows through encoding, the CPU, the memory hierarchy, and I/O, and to see where the true bottleneck lies.
Consider a simple job: read a number from the keyboard, add one to it, and store the result. Trace it: the keypress arrives as an I/O event (an interrupt); the character is encoded as bits and placed in memory; the CPU fetches the add instruction (checking the cache first, paying a slow main-memory access on a miss), the ALU performs the addition in the data path, possibly touching two's-complement rules if signs are involved; the result is stored back to memory. Every layer of the course appears in one small task.
The unifying lesson is that a system's performance is governed by its bottleneck — the slowest stage that the work must pass through. Speeding up a stage that is not the bottleneck yields little; this is why measuring before optimizing matters. Because the layers differ in speed by orders of magnitude (a register access versus a disk access can differ by a factor of millions), the bottleneck is very often I/O or memory, not raw CPU arithmetic.
This is also why the abstractions matter: each layer (bits, ISA, memory hierarchy, I/O) hides its internal complexity behind a clean interface, so a programmer can reason at one level without tracking every wire — while knowing that the layer below is there when performance demands attention.
Common pitfall: optimizing the wrong layer. Making the CPU arithmetic faster does nothing if the task spends almost all its time waiting on I/O or a cache miss. Always identify the actual bottleneck — the slowest stage on the critical path — before trying to speed a system up.