Data Structures & Algorithms
From Fundamentals to Advanced Problem Solving
Master data structures and algorithms from fundamentals to advanced topics. Interactive visualizations, multi-language implementations (C, Python, C++, TypeScript), real-world applications, and comprehensive interview preparation.
Foundations— Algorithmic thinking and complexity analysis.
Introduction to Algorithmic Thinking
Develop the mindset for solving computational problems systematically
Complexity Analysis
The language of efficiency - measuring algorithm performance
Mathematical Foundations
Essential mathematics for algorithm analysis
Recursion Mastery
Master the recursive paradigm and call stack mechanics
Linear Structures— Arrays, linked lists, stacks, queues, hash tables.
Arrays
The foundation of all data structures - contiguous memory storage
Linked Lists
Pointers, nodes, and flexible memory allocation
Stacks
LIFO data structure and its powerful applications
Queues
FIFO data structure for fair ordering and BFS
Hash Tables
O(1) average-case operations through hashing
Trees— BST, AVL, Red-Black, heaps, and advanced trees.
Tree Fundamentals
Hierarchical data structures and tree traversals
Binary Search Trees
Ordered trees for efficient searching
AVL Trees
Self-balancing BSTs with height balance
Red-Black Trees
Balanced BSTs with color-based invariants
Heaps and Priority Queues
Complete binary trees for priority-based operations
Advanced Trees
B-Trees, Tries, Segment Trees, and more
Graphs— Traversals, shortest paths, MST, and advanced algorithms.
Graph Fundamentals
Vertices, edges, and graph representations
Graph Traversals
BFS and DFS - the foundations of graph algorithms
Shortest Paths
Finding optimal paths in weighted graphs
Minimum Spanning Trees
Connecting all vertices with minimum total weight
Advanced Graph Algorithms
Topological sort, SCCs, and network flow
Advanced DS— Union-Find, skip lists, spatial and probabilistic structures.
Union-Find (Disjoint Set)
Efficient set operations with near-constant time
Skip Lists
Probabilistic alternative to balanced trees
Self-Organizing Data Structures
Adaptive structures that optimize for access patterns
Spatial Data Structures
Organizing multi-dimensional data efficiently
Probabilistic Data Structures
Trading accuracy for space and speed
Design Paradigms— Divide & conquer, greedy, DP, backtracking.
Divide and Conquer
Breaking problems into smaller subproblems
Greedy Algorithms
Making locally optimal choices for global solutions
Dynamic Programming Foundations
Optimal substructure and overlapping subproblems
Classic DP Problems
Essential dynamic programming problems every developer should know
Advanced DP Techniques
Interval DP, Tree DP, Bitmask DP, and optimization
Backtracking
Systematic exploration of solution spaces
Branch and Bound
Intelligent pruning for optimization problems
Specialized— Strings, sorting, searching, geometry, number theory.
String Algorithms
Pattern matching and string processing
Sorting Algorithms Deep Dive
Comprehensive analysis of sorting techniques
Searching and Selection
Finding elements and order statistics efficiently
Computational Geometry
Algorithms for geometric problems
Number Theory Algorithms
Mathematical algorithms for integers
Randomized Algorithms
Leveraging randomness for efficiency
Real-World— Databases, OS, distributed systems, and interview patterns.
Data Structures in Databases
How databases use data structures for storage and indexing
Data Structures in Operating Systems
OS internals powered by data structures
Data Structures in Distributed Systems
Distributed data structures for scalable systems
Interview Patterns and Problem Solving
Master patterns for technical interviews
Where the book lands in practice.
Data Structures in Databases
How databases use data structures for storage and indexing
Open chapterData Structures in Operating Systems
OS internals powered by data structures
Open chapterData Structures in Distributed Systems
Distributed data structures for scalable systems
Open chapterInterview Patterns and Problem Solving
Master patterns for technical interviews
Open chapter261 sections. Begin with one.
Chapter 1 — Introduction to Algorithmic Thinking — is where every reader starts.