Modify a data structure : Big O Notation and Calculating the Runtime of a Function

The video introduces Big O notation, a method to classify algorithm scalability and runtime efficiency. It covers different time complexities: constant (O(1)), linear (O(n)), exponential (O(n^2)), and logarithmic (O(log n)), using examples to illustrate each. The video emphasizes the importance of understanding these concepts for efficient programming.

What are the potential drawbacks of using algorithms with exponential time complexity?

How does binary search utilize logarithmic runtime to improve efficiency?

In what scenarios would you prefer to use a logarithmic algorithm over a linear one?