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Top Comments
Big O notation is Used to tell how long the code runs for large amount of data. The bigger the power of N , longer is the time taken to run and it become less efficient.
One for loop is better than two, and nested for loops are the devil. Binary search trees are king.
Other Comments
Big O notation illustrates how an algorithm's performance or runtime grows relative to the size of the input data.
The video showed how different Big O notations affect performance as data scales, making algorithm efficiency much clearer.
I learned that Big O notation measures an algorithm’s efficiency based on time and space as input size grows.
I learned how critical it is to structure your code efficiently, especially when dealing with large data.
Big O notation shows how an algorithm's performance changes as the amount of data increases.
Importance of efficient efficient on algorithm, measured by its time complexity, is crucial when dealing large datasets, algorithms with high time complexities like O(n^2) or O(n^3) can become impractically slow as the dataset grows.
This video made me understand better how Big O Notation works and how it justifies the runtime of programs.
I found the visual breakdown of time complexities super helpful, especially how O(N), O(log N), and O(N^2) compare.