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Data structures and algorithms deep dive
·
Module 1
Foundations: Complexity & Recursion
1
Algorithm Efficiency: Time and Space Complexity
Explain the purpose of time and space complexity analysis in evaluating algorithms.
2
Big-O of Iterative Code: Loops and Nested Loops
Compute the Big-O time complexity of iterative code with loops and nested loops.
3
Understanding Common Complexity Classes
Compare common complexity classes (O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ)).
4
Understanding Recursion: Base Cases and Recursive Steps
Formulate base cases and recursive steps to solve simple problems recursively (e.g., factorial).
5
Time-Space Tradeoffs in Problem Solving
Analyze the time-space tradeoff in a given problem solution.
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