Data Structures & Algorithms Mastery Roadmap
A comprehensive DSA learning path from fundamentals to advanced problem-solving covering arrays, trees, graphs, dynamic programming, and competitive programming.
Posts tagged with #data-structures
Arrays vs Linked Lists: Understanding the Trade-offs
Compare arrays and linked lists in terms of access time, insertion/deletion efficiency, memory usage, and cache performance.
Arrays: 1D, 2D, and Multi-dimensional Mastery
Master array operations, traversal, search, common patterns like two-pointer and sliding window, and when to use multi-dimensional arrays.
AVL Trees: Self-Balancing Binary Search Trees
Master AVL tree rotations, balance factors, and rebalancing logic. Learn when to use AVL vs Red-Black trees for your use case.
B-Trees and B+ Trees: Database Index Structures
Understand B-trees and B+ trees for disk-based storage, database indexing, and efficient range queries with O(log n) operations.
Bellman-Ford Algorithm: Shortest Paths with Negative Weights
Learn the Bellman-Ford algorithm for single-source shortest paths including negative edge weights and negative cycle detection.
Binary Trees and Binary Search Trees: Hierarchical Data
Master tree traversal (inorder, preorder, postorder), BST operations, and when trees outperform other data structures.
Dijkstra's Algorithm: Finding Shortest Paths in Weighted Graphs
Master Dijkstra's algorithm for single-source shortest path problems in weighted graphs with positive edges, including implementations and trade-offs.
Fenwick Trees: Binary Indexed Trees
Master Fenwick trees (binary indexed trees) for O(log n) prefix sum queries and point updates. Learn the elegant bit-trick implementation and when to choose BIT over segment trees.
Graphs: Adjacency List, Matrix, and Core Algorithms
Master graph representations (adjacency list and matrix), BFS, DFS, and when to use each representation.