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Data Structures & Algorithms
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Chapter 1: Algorithmic Thinking
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Why DSA Matters
4
Computational Thinking
4
Algorithms — Core Concepts
4
Measuring Algorithm Efficiency
3
Asymptotic Notation
4
Case Analysis & Cost Models
4
Problem Constraints & Trade-offs
5
Expressing Algorithms Clearly
5
Chapter 2: DSA Foundations & Classification
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Chapter 3: Primitive Data Structures
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Chapter 4: Strings
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Chapter 5: Arrays
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Chapter 6: Linked Lists
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Chapter 7: Stacks
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Chapter 8: Queues
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Chapter 9: Recursion
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Chapter 10: Hash Tables
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Chapter 11: Trees
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Chapter 12: Binary Search Trees
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Chapter 13: Balanced Search Trees
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Chapter 14: Heaps & Priority Queues
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Chapter 15: Tries
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Chapter 16: Graphs
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Chapter 17: Searching Algorithms
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Search Problem
4
Linear Search
4
Binary Search
4
Binary Search Variations
5
Search Space Reduction
4
Rotated Arrays
4
Binary Search on Answer
4
Searching in 2D
4
Interpolation Search
4
1.
Using Value Distribution for Better Guesses
2.
When Interpolation Beats Binary Search
3.
Worst-Case Degradation to O(n)
4.
Practical Applicability
Ternary Search
4
Chapter 18: Sorting Algorithms
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Chapter 19: Divide and Conquer
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Chapter 20: Greedy Algorithms
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Chapter 21: Dynamic Programming
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Chapter 22: Backtracking
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Chapter 23: Graph Algorithms
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Chapter 24: Advanced Graph Algorithms
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Chapter 25: String Algorithms
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Chapter 26: Bit Manipulation
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Chapter 27: Advanced Data Structures
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Chapter 28: Problem Solving Strategies
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