“Data Structures and Algorithms Roadmap”
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Arrays Overview: Basics of storing elements in contiguous memory. Difficulty: Easy Time: 1-2 weeks Concepts: Insertion, Deletion, Traversal, Searching, Sorting (basic algorithms like Bubble Sort, Selection Sort).
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Linked Lists Overview: A dynamic data structure where elements are linked using pointers. Difficulty: Easy to Moderate Time: 1-2 weeks Concepts: Single Linked List, Double Linked List, Circular Linked List, Operations (Insertion, Deletion).
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Stacks and Queues Overview: Linear data structures for LIFO (Stack) and FIFO (Queue) operations. Difficulty: Moderate Time: 1-2 weeks Concepts: Implementation using Arrays and Linked Lists, Applications (e.g., Recursion, Backtracking for Stacks, BFS for Queues).
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Hashing Overview: A technique to map data to a fixed-size table. Difficulty: Moderate Time: 1-2 weeks Concepts: Hash Tables, Hash Functions, Collision Resolution (Chaining, Open Addressing).
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Trees Overview: Hierarchical data structures with a root node and subtrees. Difficulty: Moderate Time: 2-3 weeks Concepts: Binary Trees, Binary Search Trees, Tree Traversals (Inorder, Preorder, Postorder), Height, Depth, Balanced Trees.
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Heaps Overview: A special tree-based structure that satisfies the heap property. Difficulty: Moderate to Hard Time: 1-2 weeks Concepts: Min Heap, Max Heap, Heap Operations (Insert, Delete), Heap Sort, Priority Queues.
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Graphs Overview: A set of nodes (vertices) connected by edges, useful for representing networks. Difficulty: Hard Time: 3-4 weeks Concepts: Graph Representation (Adjacency List, Matrix), Traversal (BFS, DFS), Shortest Path Algorithms (Dijkstra’s, Bellman-Ford), Minimum Spanning Tree (Kruskal, Prim).
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Advanced Trees Overview: Specialized trees for optimized operations. Difficulty: Hard Time: 2-3 weeks Concepts: AVL Trees, Red-Black Trees, B-Trees, Tries.
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Dynamic Programming Overview: An optimization technique to solve complex problems by breaking them down into simpler subproblems. Difficulty: Hard Time: 3-4 weeks Concepts: Memoization, Tabulation, Common Problems (Knapsack, Longest Common Subsequence, Fibonacci).
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Greedy Algorithms Overview: Algorithms that make locally optimal choices at each step. Difficulty: Hard Time: 2-3 weeks Concepts: Activity Selection, Huffman Encoding, Kruskal’s, Prim’s.
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Sorting and Searching (Advanced) Overview: Efficient algorithms for sorting and searching. Difficulty: Moderate to Hard Time: 2-3 weeks Concepts: Merge Sort, Quick Sort, Binary Search, Search Trees.
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Backtracking Overview: A technique for solving problems recursively by trying to build a solution incrementally. Difficulty: Hard Time: 2-3 weeks Concepts: N-Queens, Sudoku Solver, Hamiltonian Path.
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Divide and Conquer Overview: An algorithm design paradigm based on multi-branched recursion. Difficulty: Moderate to Hard Time: 1-2 weeks Concepts: Merge Sort, Quick Sort, Matrix Multiplication.
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Complexity Analysis Overview: Understanding the efficiency of algorithms. Difficulty: Moderate to Hard Time: 1-2 weeks Concepts: Big O, Big Theta, Big Omega, Space Complexity.
-------Total Estimated Time: 4-6 months-----