Grokking Algorithm Complexity and Big-O

Cracking Algorithms: Master Algorithm Analysis and Big-O
Level:
Beginner
Study Time:
5h
Lessons:
44
Playgrounds :
46
4.4
(1,885 ratings)
6,479 learners
Course Overview

Are you struggling to understand how algorithms work or why one solution is better than another? This course on algorithm analysis is designed for students, developers, and job seekers who need a clear path to mastering algorithm performance. Learn how to analyze time and space complexity, study recursive patterns, and apply your knowledge to real-world coding challenges.

By the end, you'll confidently choose the most efficient solutions, write optimized code, and excel in interviews and technical projects. Start now to build your expertise and set yourself apart in the world of software development and computer science. This course is for you!

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Course Content
8 Chapters • 44 Lessons
1.
Introduction
1 lesson
Course Overview
2.
Basics of Algorithm Analysis
3 lessons
Introduction to Algorithm Analysis
Measuring Efficiency
Functions and Their Growth Rates
Overview of Asymptotic Analysis
Big-O Notation (O-notation)
Big-Omega Notation (Ω-notation)
Big-Theta Notation (Θ-notation)
Little-o and Little-omega Notations
Comparing Asymptotic Notations
Understanding Time Complexity
Analyzing Control Structures
Analyzing Simple Algorithms
Best, Worst, and Average Cases
Constant Time: O(1)
Linear Time: O(n)
Quadratic Time: O(n²)
Quiz
Understanding Space Complexity
Analyzing Space Complexity of Algorithms
Constant Space: O(1)
Linear Space: O(n)
Quadratic Space: O(n²)
Quiz
Introduction to Recursion
Recursion Tree Method
Recurrence Relation Method
Master Theorem Method
Space Complexity Analysis of Recursive Algorithm
Logarithmic Time and Space: O(log n)
Linearithmic Time: O(n log n)
Exponential Time and Space: O(2ⁿ)
Quiz
Array
Linked List
Hash Table and Set
Stack and Queue
Binary Search Tree
Quiz
Sorting Algorithms
Graph Algorithms
Dynamic Programming Algorithms
Coding Interview Problems
Trade-offs in Algorithm Design
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