Big O Notation - Full Course

Overview

This course teaches learners how to understand and apply the concepts of Big O Notation in Software Engineering. By the end of the course, students will be able to analyze algorithm efficiency and memory usage using Big O notation. The course covers various complexities such as O(n^2), O(n^3), O(log n), O(n log n), O(2^n), and O(n!), along with explanations and coding examples. The teaching method includes video lectures with explanations and coding demonstrations. This course is intended for individuals interested in improving their algorithm analysis skills and understanding the efficiency of algorithms in software development.

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.