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okay so today we're going to be covering a fairly famous algorithm called heapsort and before we get into this video I first off want to say if you're uncomfortable with logarithms it's very important that you become very very very comfortable with them I have a video on logarithms in computer science I also have a fairly long video on mergesort it's very very in-depth and it goes into how logarithms factor into calculating time complexities and doing analysis and I also have a video on binary heaps already doing insertion and deletion from a binary heap today we're gonna analyze heapsort which is the application of a heap to sort data and a structure we'll explain but I want to make sure that you're comfortable with these things before we get into analysis because inherently these things are not hard they're not complex but if your fundamentals are weak using logarithms understanding how many nodes are in a certain level of a tree it's these fundamental things that make algorithm classes difficult algorithm classes don't need to be difficult besides the kind of hard computations that happen where you need to do some weird extraction of a quantity using some famous derivation of sorts but in general everything is straightforward and it's the weakness in the fundamentals that cause confusion going forward through each intellectual leap that we take so I just want to say that in in advance and I don't think this is going to be too hard but we're going to walk through it I'm going to try to do my job to make everything as clear as possible as always and yeah so let's let's get into it right now okay so heapsort is one of our algorithms that runs in Big O of n log n time in the best in the worst and in the average case of the data and this is because of the nature how we do of how we do things just as merge sort doesn't care about how the input comes in to us heapsort is not going to care either what we're going to do is we're going to do a two-phase process we're going to be doing a phase where we build a max-heap you'll see why it's a max-heap you could do a min heap but we'll see why it's a max heap where we build a map and we do an extraction after we build a heap we extract elements one by one by one and we place them going from the back to the front of the array until our whole array is sorted and what we do is our array when we build our heap is going to store the mappings and the way that we know what our heap looks like because we're doing a binary heap the way we know our heap looks like although we're doing an array is we have a certain mapping system and we're going...