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Data Structures & Algorithms
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Chapter 1: Algorithmic Thinking
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Why Data Structures and Algorithms Matter
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Computational Thinking & Problem-Solving
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Algorithms — Core Concepts
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Measuring Algorithm Efficiency
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Asymptotic Notation — Formalizing Algorithm Efficiency
4
Case Analysis & Cost Models
4
Problem Constraints & Algorithmic Trade-offs
5
Expressing Algorithms Clearly
5
Chapter 2: Data Structures Foundations
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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 11: Trees Hierarchical Data Structures
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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 And Priority Queues
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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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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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Graph Traversal Overview
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Depth-First Search (DFS) — Algorithm & Applications
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Breadth-First Search (BFS) — Algorithm & Applications
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Connected Components — Graph Decomposition
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Cycle Detection
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Topological Sorting
5
Shortest Path — Unweighted Graphs (BFS)
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Shortest Path — Dijkstra's Algorithm
5
Shortest Path — Bellman-Ford Algorithm
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All-Pairs Shortest Path — Floyd-Warshall (Conceptual)
4
Choosing the Right Shortest Path Algorithm
5
Region Color Propagation
Region Color Propagation
Task Dependency Validator
Task Execution Order
Signal Propagation Time
Implement downstream impact across a drainage network
Track visited drainage assets while following downstream links
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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Course overview & roadmap
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