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Learn widely used algorithms
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Module 1
Foundations of Algorithmic Analysis
1
Big-O Analysis of Iterative and Recursive Algorithms
Determine the time complexity of iterative and recursive algorithms using Big-O notation.
2
Comparing Asymptotic Notations: O, Θ, and Ω
Compare and contrast the asymptotic notations: Big-O, Big-Theta, and Big-Omega.
3
Solving Recurrence Relations: Master Theorem & Recursion Trees
Solve common recurrence relations using the Master Theorem and recursion tree method.
4
Understanding Space Complexity
Analyze the space complexity of an algorithm, distinguishing between auxiliary and total space.
5
P, NP, and NP-Complete: Understanding Computational Complexity
Define the complexity classes P, NP, and NP-complete and discuss their practical implications.
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Essential Data Structures Review