• Arrays: Linear data structure with a fixed size, allowing random access to elements.

    • Best for: Static datasets with frequent element access.
    • Complexity:
      • Access: O(1)
      • Search: O(n)
      • Insertion/Deletion: O(n) Array

  • Linked Lists: Linear structure of nodes, where each node points to the next.

    • Types: Singly, Doubly, Circular.
    • Best for: Dynamic datasets with frequent insertions/deletions.
    • Complexity:
      • Access/Search: O(n)
      • Insertion/Deletion: O(1) (at head/tail with pointer)

  • Hash Maps (or Hash Tables): Key-value pair storage with a hash function.

    • Best for: Quick lookup and insertion.
    • Complexity:
      • Average: O(1)
      • Worst-case: O(n)(due to hash collisions)

  • Stacks: LIFO (Last In, First Out) structure.

    • Operations: Push, Pop, Peek.
    • Best for: Recursive problems, expression evaluation.
    • Complexity:
      • Push/Pop/Peek: O(1)

  • Queues: FIFO (First In, First Out) structure.

    • Variants: Circular Queue, Deque, Priority Queue.
    • Best for: Scheduling, buffering.
    • Complexity:
      • Enqueue/Dequeue: O(1)

  • Trees: Hierarchical structure with nodes (root, parent, children).

    • Types: Binary, Binary Search Tree (BST), AVL, Red-Black, etc.
    • Best for: Hierarchical data, searching/sorting.
    • Complexity:
      • Search/Insert/Delete: O(h), where h is the tree height.
      • Balanced trees maintain h=O(logn).

  • Graphs: Collection of nodes (vertices) connected by edges.

    • Types: Directed, Undirected, Weighted, Unweighted.
    • Representation: Adjacency Matrix/List.
    • Best for: Network problems, pathfinding.
    • Algorithms: DFS, BFS, Dijkstra’s, Prim’s, Kruskal’s.