Mastering data structures and algorithms is essential for technical interviews. Arrays and strings form the foundation with O(1) random access and O(n) insertion/deletion. Linked lists provide dynamic memory allocation with O(1) insertion at known positions. Stacks follow LIFO for undo operations and expression evaluation. Queues follow FIFO for task scheduling and breadth-first search. Hash tables offer O(1) average-case lookup for dictionary operations. Trees enable hierarchical data representation: binary trees, BSTs, heaps. Graphs model relationships with adjacency lists or matrices. Sorting algorithms include quicksort, mergesort, and heapsort. Search algorithms range from binary search to depth-first and breadth-first search. Dynamic programming optimizes recursive solutions with . Greedy algorithms make locally optimal choices. Understand time and space complexity analysis using Big O notation. Practice pattern recognition: sliding window, two pointers, recursion. Use arrays for contiguous data, linked lists for frequent insertions/deletions. Trees excel at hierarchical data, graphs for networks. Regular practice on platforms like LeetCode builds confidence. Understanding trade-offs between different data structures is key.