Data Structures & Algorithms Learning Path.

Time and space complexity bounds, asymptotic notation, and runtime trade-offs.

Contiguous memory bounds, opposite-end and same-direction pointer movements.

Fixed and variable length sliding window patterns and prefix sum arrays.

Singly/doubly linked lists, cycle detection (Floyd's algorithm), and node reversal.

LIFO vs FIFO mechanics, expression evaluation, and monotonic stack patterns.

Hash maps, hash sets, collision handling, and O(1) frequency counting.

Recursion tree decision space, base cases, state restoration, and power sets.

Binary search on sorted arrays, lower/upper bounds, and searching answer spaces.

Tree traversals (Pre, In, Post, Level-order), depth, path sums, and BST invariants.

Min-heap and max-heap properties, heapify, and Top-K element tracking.

Adjacency lists/matrices, BFS (queue), DFS (stack/recursion), and topological sort.

Greedy choice property, interval scheduling by end time, and local optimal choices.

Overlapping subproblems, optimal substructure, top-down memoization, and bottom-up DP.