📚 Data Structures and Algorithms (2025–2026 Edition) is a complete syllabus book designed for BSCS, BSIT, Software Engineering students, competitive programmers, software developers, and self-learners who want to master the art of coding, problem-solving, and optimization. This edition includes MCQs, and quizzes to provide both an academic and practical approach to understanding data structures and algorithms.

The book covers both theory and implementation, helping students explore how data is organized, stored, and manipulated efficiently. It bridges arrays, stacks, queues, linked lists, trees, graphs, hashing, recursion, searching, sorting, and algorithm design techniques to strengthen analytical and programming skills. Learners will also gain insights into algorithm complexity, optimization strategies, and real-world applications of DSA.

📂 Chapters & Topics

🔹 Chapter 1: Introduction to Data Structures

– What are Data Structures?
– Need and Importance of Data Structures
– Abstract Data Types (ADT)
– Types of Data Structures: Linear vs Non-Linear
– Real-life Applications

🔹 Chapter 2: Arrays

– Definition and Representation
– Operations: Traversal, Insertion, Deletion, Searching
– Multi-dimensional Arrays
– Applications of Arrays

🔹 Chapter 3: Stacks

– Definition and Concepts
– Stack Operations (Push, Pop, Peek)
– Implementation using Arrays and Linked Lists
– Applications: Expression Evaluation, Function Calls

🔹 Chapter 4: Queues

– Concept and Basic Operations
– Types of Queues: Simple Queue, Circular Queue, Deque
– Implementation using Arrays and Linked Lists
– Applications

🔹 Chapter 5: Priority Queues

– Concept of Priority
– Implementation Methods
– Applications

🔹 Chapter 6: Linked Lists

– Singly Linked List
– Doubly Linked List
– Circular Linked List
– Applications

🔹 Chapter 7: Trees

– Basic Terminology (Nodes, Root, Height, Degree)
– Binary Trees
– Binary Search Trees (BST)
– Tree Traversals (Inorder, Preorder, Postorder)
– Advanced Trees: AVL Trees, B-Trees

🔹 Chapter 8: Graphs

– Graph Terminologies (Vertices, Edges, Degree, Paths)
– Graph Representation: Adjacency Matrix & List
– Graph Traversals: BFS, DFS
– Applications of Graphs

🔹 Chapter 9: Recursion

– Concept of Recursion
– Direct and Indirect Recursion
– Recursive Algorithms (Factorial, Fibonacci, Towers of Hanoi)
– Applications

🔹 Chapter 10: Searching Algorithms

– Linear Search
– Binary Search
– Advanced Searching Techniques

🔹 Chapter 11: Sorting Algorithms

– Bubble Sort, Selection Sort, Insertion Sort
– Merge Sort, Quick Sort, Heap Sort
– Efficiency Comparison

🔹 Chapter 12: Hashing

– Concept of Hashing
– Hash Functions
– Collision and Collision Resolution Techniques
– Applications

🔹 Chapter 13: Storage and Retrieval Techniques

– File Storage Concepts
– Indexed Storage
– Memory Management Basics

🔹 Chapter 14: Algorithm Complexity

– Time Complexity (Best, Worst, Average Case)
– Space Complexity
– Big O, Big Ω, Big Θ Notations

🔹 Chapter 15: Polynomial and Intractable Algorithms

– Polynomial Time Algorithms
– NP-Complete and NP-Hard Problems
– Examples

🔹 Chapter 16: Classes of Efficient Algorithms

– Characteristics of Efficient Algorithms
– Case Studies

🔹 Chapter 17: Algorithm Design Techniques

– Divide and Conquer
– Dynamic Programming
– Greedy Algorithms

🌟 Why Choose this Book?

✅ Covers complete DSA syllabus for BSCS, BSIT, and Software Engineering
✅ Includes MCQs, quizzes, and applications
✅ Strengthens exam prep, project work, and competitive programming
✅ Builds a strong foundation in theory, coding, and problem-solving
✅ Perfect for students, developers, and interview preparation

✍ This book is inspired by authors:
Thomas H. Cormen (CLRS), Donald Knuth, Robert Lafore, Mark Allen Weiss

📥 Download Now!
Master Data Structures and Algorithms with the 2025–2026 Edition and level up your programming, optimization, and problem-solving skills.