Data Structures
Understand arrays, trees, graphs, heaps, hash tables, and choosing the right structure.
Choosing the wrong structure is a sharp signal in coding rounds and design discussions: it leads to wrong complexity, brittle code, or production slowness that's hard to fix later. At senior levels, interviewers listen for whether you reason from constraints to structure, not the other way around.
These topics build a precise mental model of arrays, trees, heaps, hashes, and when each fits—so you can choose and justify your choices under real constraints, reason about trade-offs, and explain your design clearly. You'll signal that you think in data shapes, not just APIs.
Topics in this category
Arrays: Data Structure Fundamentals & Complexity
Read →Master arrays: the most fundamental data structure. Learn operations, time complexity, dynamic arrays, and when to use arrays vs other structures.
B-Tree Explained for Database Indexes (B+ Tree Included)
Read →A clear guide to B-trees for DB indexes: how inserts/splits work, why high fanout matters, and how B+ trees enable fast range queries.
Bloom Filters: Fundamentals, Operations & Complexity
Read →Master Bloom filters: space-efficient probabilistic data structures for membership testing. Learn false positives, hash functions, and real-world applications.
Disjoint Set (Union-Find)
Read →Master the Union-Find data structure for efficiently managing disjoint sets. Learn path compression and union by rank optimizations.
Graphs: Fundamentals, Operations & Complexity
Read →Master graph data structures: representations, traversals (BFS, DFS), and graph algorithms. Understand when to use graphs for modeling relationships.
Hash Tables: Fundamentals, Operations & Complexity
Read →Master hash tables: hashing, collision resolution, load factor, and hash table operations. Understand when to use hash tables for O(1) average operations.
Heaps: Fundamentals, Operations & Complexity
Read →Master heap data structures: min-heap, max-heap, heap operations, and priority queues. Learn heapify algorithms and heap applications.
Linked Lists: Fundamentals, Operations & Complexity
Read →Master linked lists: singly, doubly, and circular variants. Understand when to use linked lists vs arrays and common operations.
Memory Management & Pointers
Read →Master memory management: stack vs heap, pointers, references, dynamic allocation, and how data structures use memory. Essential for understanding linked struct
Recursion & Backtracking
Read →Master recursion and backtracking: essential patterns for tree traversal, graph algorithms, and solving problems with data structures. Learn how recursive calls
Segment Trees & Fenwick Trees
Read →Master advanced tree structures for range queries: segment trees and Fenwick trees (Binary Indexed Trees). Learn when to use them for range sum, min, max querie
Self-Balancing Trees: Fundamentals, Operations & Complexity
Read →Master AVL and Red-Black trees: self-balancing binary search trees that maintain O(log n) operations. Learn rotations, balancing strategies, and when to use eac
Stacks and Queues
Read →Master stacks (LIFO) and queues (FIFO) data structures. Learn implementations, use cases, and when to choose each structure.
Big-O, Time & Space Complexity
Read →Master complexity analysis: Big-O notation, time complexity, space complexity, and how to analyze data structure operations. Essential for algorithmic thinking.
Trees: Fundamentals, Operations & Complexity
Read →Master tree data structures: binary trees, binary search trees, tree traversals, and tree algorithms. Understand when to use trees and common operations.
Tries (Prefix Trees)
Read →Master tries: fast string search, prefix matching, and autocomplete. Learn when to use tries for text-based problems and dictionary operations.