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Understand and apply algorithms
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Module 4
Analyzing Algorithm Efficiency
1
Efficiency Matters: Why Large Inputs Demand Algorithm Analysis
Explain why measuring algorithm efficiency is critical for large inputs.
2
Counting Loop Operations
Count the number of basic operations in a simple loop or nested loop.
3
Understanding Big O Notation for Time Complexity
Define Big O notation and use it to express an algorithm's time complexity.
4
Big O Notation: Common Time Complexities
Identify the time complexity of common patterns: O(1), O(log n), O(n), O(n²).
5
Comparing Algorithm Efficiency with Big O
Compare two algorithms for the same problem using Big O analysis.
6
Understanding Algorithmic Complexity: Best, Worst, and Average Cases
Explain the difference between best-case, worst-case, and average-case complexity.
Previous module
Introduction to Object-Oriented Programming
Next module
Recursion