Big O Notation - Full Course

Overview

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Dive into a comprehensive course on Big O Notation and its application in Software Engineering. Learn to understand and apply this crucial concept for describing algorithm efficiency in terms of time and memory usage. Explore various time complexities including O(n^2), O(n^3), O(log n), O(n log n), O(2^n), and O(n!). Gain practical insights through explanations of recursive and iterative approaches, binary search implementation, and merge sort coding. Delve into space complexity and avoid common mistakes in algorithm analysis. Master the skills to evaluate and optimize code performance, essential for any software engineer.

Syllabus

) Intro.
) What Is Big O?.
) O(n^2) Explanation.
) O(n^3) Explanation.
) O(log n) Explanation Recursive.
) O(log n) Explanation Iterative.
) O(log n) What Is Binary Search?.
) O(log n) Coding Binary Search.
) O(n log n) Explanation.
) O(n log n) Coding Merge Sort.
) O(n log n) Merge Sort Complexity Deep Dive.
) O(2^n) Explanation With Fibonacci.
) O(n!) Explanation.
) Space Complexity & Common Mistakes.
) End.