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Big O Notation - Full Course

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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.

Taught by

freeCodeCamp.org

Reviews

5.0 rating, based on 1 Class Central review

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  • Anonymous
    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.
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