Introduction to Merge Sort and Time Complexity
Explains merge sort as a divide-and-conquer algorithm: recursively split an array into halves, sort each half, and merge the results in order. Includes a complete Python implementation with step-by-step commentary. Introduces Big-O notation to reason about algorithmic efficiency and explains why merge sort achieves O(n log n) in all cases by combining log n levels of recursion with O(n) merging work per level. Compares merge sort against bubble, selection, insertion, and quick sort across best, average, and worst-case scenarios.