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You have completed Algorithms: Sorting and Searching!
You have completed Algorithms: Sorting and Searching!
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The algorithms we've discussed in this stage are very well-known, and some job interviewers are going to expect you to know their Big O runtimes. So let's look at them!
Quicksort Run Time (Worst Case)
O(n²)
Quicksort Run Time (Average Case)
O(n log n)
Merge Sort Run Time
O(n log n)
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an algorithm for each and
every problem they need to solve.
0:00
And they often need to discuss their
decisions with other developers.
0:02
Can you imagine needing to describe all
the algorithms in this same level of
0:05
detail all the time?
0:10
You'd spend all your time in
meetings rather than programming.
0:11
That's why Big O Notation was created,
as a way of quickly describing how
0:15
an algorithm performs as the data set
its working on increases in size.
0:19
Big O Notation lets you quickly compare
several algorithms to choose the best one
0:24
for your problem.
0:28
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