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IT238 Data Structure and Algorithms

Data Structure and Algorithms notes

10 chapter notes, in syllabus order. Each starts with the key points.

Unit 1 · 6 hrs

Data Structures & Algorithms: Core Concepts, Definitions & ApplicationsUnit 1 of Data Structure and Algorithms introduces the foundational concepts of data structures (how data is organized in memory) and algorithms (step-by-step problem-solving methods), their classifications, and real-world relevance in software development and problem-solving.10 min read

Unit 2 · 6 hrs

Time Complexity: Analysis, Big-O, and Algorithm EfficiencyUnit 2 of Data Structure and Algorithms teaches how to measure and compare algorithm performance using time complexity (Big-O, Ω, Θ), asymptotic analysis, and real-world trade-offs between speed and memory. Covers worst-case, average-case, and best-case scenarios with examples from sorting, searching, and graph travers8 min read

Unit 3 · 6 hrs

Recursion and Backtracking: Techniques, Analysis, and ApplicationsUnit 3 of Data Structure and Algorithms explores recursion (self-referential function calls) and backtracking (systematic trial-and-error), covering definitions, mechanics, time/space complexity, and real-world applications in pathfinding, combinatorial problems, and divide-and-conquer algorithms.11 min read

Unit 4 · 8 hrs

Arrays, Linked Lists, Stacks: Operations, Analysis & Real-World UseUnit 4 of Data Structure and Algorithms covers arrays (static vs dynamic), linked lists (singly/doubly), stack operations (LIFO), their time/space complexity, and practical applications in Nepalese software (e.g., order queues in Daraz, undo mechanisms in eSewa). Includes visual traces of insertion/deletion, comparison12 min read

Unit 5 · 6 hrs

Queues, Priority Queues, and Real-World SchedulingUnit 5 of Data Structure and Algorithms covers queues (FIFO, circular, deque), priority queues (min-heap, max-heap), their operations (enqueue, dequeue, peek), time complexity, and applications in scheduling, task management, and resource allocation—with traces, code, and real-world ties to eSewa, Daraz, and Ncell.11 min read

Unit 6 · 6 hrs

Hashing, Hash Tables, Collision Handling & PerformanceUnit 6 of Data Structure and Algorithms covers hashing principles, hash table implementations (chaining and open addressing), collision resolution techniques, load factor analysis, and real-world applications in databases, compilers, and caching systems.7 min read

Unit 7 · 10 hrs

Binary Trees, BSTs, AVL Trees, and ApplicationsUnit 7 of Data Structure and Algorithms covers binary trees (structure, traversals, properties), binary search trees (BSTs: insertion, deletion, search), self-balancing AVL trees (rotations, balancing), and real-world applications in databases, file systems, and routing algorithms.19 min read

Unit 8 · 8 hrs

Sorting Algorithms: Bubble, Selection, Insertion, Merge, Quick, and RadixUnit 8 of Data Structure and Algorithms: This note explains fundamental sorting algorithms (Bubble, Selection, Insertion, Merge, Quick, and Radix), their time and space complexity, step-by-step operations, and real-world applications in databases, search engines, and financial systems.13 min read

Unit 9 · 8 hrs

Graph Algorithms: Paths, Trees, Shortest Paths & Network FlowsUnit 9 of Data Structure and Algorithms covers graph representations (adjacency matrix/list), traversal algorithms (BFS/DFS), minimum spanning trees (Prim/Kruskal), shortest path algorithms (Dijkstra/Floyd-Warshall), and network flow (Ford-Fulkerson). You’ll learn how to model real-world problems as graphs and solve th16 min read

Unit 10 · 6 hrs

Advanced Data Structures & Applications: Heaps, Tries, Disjoint Sets, and Graph AlgorithmsUnit 10 of Data Structure and Algorithms explores priority queues (heaps), string search (Tries), disjoint-set forests (Union-Find), and advanced graph algorithms (Kruskal’s, Prim’s, Dijkstra’s, and topological sorting). Learn their real-world uses, time complexities, and implementation details with step-by-step traces14 min read