Arrays ↓ Strings ↓ Hashing ↓ Two Pointers ↓ Sliding Window ↓ Stack & Queue ↓ Linked List ↓ Binary Search ↓ Trees ↓ Graphs ↓ Greedy ↓ Backtracking ↓ Dynamic Programming
This roadmap is designed to take you from the fundamentals of Data Structures and Algorithms (DSA) to advanced problem-solving. Follow the topics in order, because each concept builds the foundation for the next one.
Arrays ↓ Learn indexing, traversal, sorting, prefix sums, Kadane’s algorithm, and common array patterns. Arrays build the foundation for efficient problem-solving.
Strings ↓ Master string traversal, manipulation, frequency counting, palindromes, anagrams, and pattern-based problems.
Hashing ↓ Learn hash maps and hash sets to achieve fast lookups, frequency counting, duplicate detection, and efficient data organization.
Two Pointers ↓ Understand how two pointers can reduce unnecessary iterations and solve problems involving sorted arrays, pairs, subarrays, and strings efficiently.
Sliding Window ↓ Learn how to maintain a dynamic range while processing arrays and strings. This technique is essential for solving subarray and substring problems efficiently.
Stack & Queue ↓ Understand LIFO and FIFO data structures and learn important patterns such as monotonic stacks, next greater element, parentheses matching, BFS, and scheduling.
Linked List ↓ Master node manipulation, insertion, deletion, reversal, fast and slow pointers, cycle detection, and merging linked lists.
Binary Search ↓ Learn to search efficiently in sorted data and recognize binary-search-on-answer problems. Focus on reducing O(n) searches to O(log n) whenever possible.
Trees ↓ Build a strong understanding of binary trees, BSTs, DFS, BFS, recursion, tree height, diameter, lowest common ancestor, and tree-based problem-solving.
Graphs ↓ Learn graph representation, BFS, DFS, connected components, cycle detection, shortest paths, topological sorting, MST, and other essential graph algorithms.
Greedy ↓ Learn how to make locally optimal choices to construct globally optimal solutions. Practice activity selection, intervals, scheduling, and optimization problems.
Backtracking ↓ Master recursive exploration of possible solutions using techniques such as permutations, combinations, subsets, N-Queens, and constraint-based problems.
Dynamic Programming ↓ Learn to break complex problems into overlapping subproblems and store their results. Master memoization, tabulation, 1D/2D DP, knapsack, subsequences, grid DP, and advanced DP patterns.
The goal is not just to memorize algorithms. Understand the pattern behind each problem, analyze time and space complexity, and learn to recognize which technique should be applied.
Learn → Practice → Analyze → Optimize → Repeat 🔥