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This introductory course explains Big O notation for analyzing algorithm time and space complexity. It uses binary search, merge sort, recursion, Fibonacci, and common complexity classes to illustrate the concepts.
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.
Taught by
freeCodeCamp.org
Reviews
5.0 rating, based on 1 Class Central review
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This course from freeCodeCamp is truly excellent! The concept of Big O Notation is one of the most fundamental concepts in computer science and is often complex, but this explanation makes it simple and easy to understand. The instructor explains the concepts clearly and sequentially, with practical examples that greatly helped me grasp how to analyze algorithm efficiency. I benefited immensely and now feel more confident in writing better and more efficient code. I highly recommend this course to anyone who wants to solidify their understanding of data structures and algorithms.