The statements that correctly describe the time complexity of data structures with N data are:
- The average time complexity of the data lookup in a hash table is O(N).
- The average time complexity of inserting data into a heap is O(logN)
<h3>What is time complexity of data structures?</h3>
Time Complexity of an algorithm is known to be the depiction of the amount of time needed by the algorithm to carry out to completion.
Note that The statements that correctly describe the time complexity of data structures with N data are:
- The average time complexity of the data lookup in a hash table is O(N).
- The average time complexity of inserting data into a heap is O(logN)
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A speaker sometimes is not able to capture the intended details since it is affected by homophones.
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Explanation:
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Answer:
While loops are typically used when you don’t know how many times the loop needs to repeat. The body of the loop will repeat while the condition is true. The logical expression will be evaluated just before the body of the loop is repeated.
Let’s say that we want to find the square root of a number. For some square roots, you’re never going to be exact. Let’s say that we want to find a square root that, when multiplied by itself, is within 0.01 of the square we want. How do we do it? There’s a really old process that we can apply here.
Start by guessing 2.
Compute the guess squared.
Is the guess squared close to the target number? If it’s within 0.01, we’re done. We’ll take the absolute value of the difference, in case we overshoot. (In Python, abs is the absolute value function.)
If it’s not close enough, we divide the target number by our guess, then average that value with our guess.
That’s our new guess. Square it, and go back to Step #3.
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Answer:
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