From this point on, the course switches gears a little bit, and will begin to discuss algorithms, efficiency, and complexity. First lets talk a little bit about efficiency and orders of growth.
Although it may sound intuitive, but when first thinking about efficiency, one might ask "why is efficiency important?"
The answer is that efficiency determines how long our programs takes to run, which is called run-time.
This is not to be confused with the time in seconds an algorithm takes to run. For instance, if one algorithm took 2 seconds to run, and another algorithm took 0.5 seconds to run, can you say that the second is faster than the first? No, because I did not specify many necessary factors that needs to be taken in account. For one, we need to think about the speed of the processor, as well as the implementation of Python. It also depends on the amount of data input, and in terms of extremely large data sets, some algorithms might take a millennium to run!
More importantly, efficiency is about algorithms, not about coding structure or clever coding.
So the way we normalize across these factors, is to talk about efficiency in terms of the steps an algorithm takes to produce a result based on an input. A step is both a physical thing and an abstract idea, in that we assume each step takes constant time.
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