Prerequisites:

• Programming Proficiency: A strong foundation in the C programming language is essential. Students should be comfortable with:

  • Variables, data types, and operators.

  • Control structures (if-else, switch, for, while).

  • Functions and parameter passing.

  • Pointers, pointer arithmetic, and dynamic memory allocation (malloc, calloc, free).

  • Structures (struct) and Unions (union).

  • File handling.

• Mathematical Skills: Basic understanding of algebra and logical reasoning. Familiarity with concepts like logarithms and summation notation will be beneficial for algorithm analysis.

• Problem-Solving: A logical and analytical approach to problem-solving.

This course provides a comprehensive exploration of fundamental data structures and their implementation using the C programming language. It is designed to equip students with the essential skills to organize, manage, and store data efficiently. The course covers the theory and practical application of linear and non-linear data structures, including arrays, stacks, queues, linked lists, trees, and graphs. A significant emphasis is placed on algorithm design and analysis to evaluate the efficiency and performance of these data structures. By the end of this course, students will be able to choose and implement appropriate data structures to solve complex computational problems.

Upon successful completion of this course, students will be able to:

• CO1: Understand Fundamentals: Comprehend the concepts of abstract data types (ADTs) and the fundamental principles of data structures like arrays, linked lists, stacks, queues, trees, and graphs.

• CO2: Implement in C: Gain proficiency in implementing various data structures from scratch using C, with a strong focus on pointers, dynamic memory allocation, and structures.

• CO3: Analyze and Select: Develop the ability to analyze computational problems and select the most appropriate data structure and algorithm for an efficient solution.

• CO4: Enhance Problem-Solving Skills: Apply knowledge of data structures and algorithms to solve a variety of real-world problems and algorithmic challenges.

• CO5: Evaluate Efficiency: Analyze the time and space complexity of algorithms and understand the trade-offs between different data structures.

  
  

  Module 1: Introduction and C Language Revision (5 Hours)

  • Introduction to Data Structures: Basic terminology, classification of data structures.

  • Abstract Data Types (ADTs).

  • Introduction to Algorithm Analysis: Time and Space Complexity, Asymptotic Notations (Big O, Big Omega, Big Theta).

  • Revision of C: Pointers, Structures, Self-referential Structures, Dynamic Memory Allocation.

  Module 2: Arrays and Stacks (7 Hours)

  • Arrays: One-dimensional, two-dimensional, and multi-dimensional arrays.

  • Polynomial representation using arrays.

  • Sparse Matrices and their representation.

  • Stacks: The Stack ADT, implementation using arrays.

  • Applications of Stacks: Infix to Postfix/Prefix conversion, evaluation of postfix expressions.

  Module 3: Queues (6 Hours)

  • Queues: The Queue ADT, implementation using arrays.

  • Circular Queues: Implementation and advantages.

  • Deques (Double-Ended Queues) and Priority Queues.

  Module 4: Linked Lists (8 Hours)

  • Singly Linked Lists: Representation, traversal, insertion, deletion, searching.

  • Circular Linked Lists.

  • Doubly Linked Lists.

  • Implementation of Stacks and Queues using linked lists.

  • Applications of Linked Lists: Polynomial representation and addition.

  Module 5: Recursion (5 Hours)

  • Principles of Recursion.

  • Difference between iteration and recursion.

  • Examples: Factorial, Fibonacci sequence, Tower of Hanoi.

  • Recursive Binary Search.

  Module 6: Trees (8 Hours)

  • Trees: Basic terminology (root, node, leaf, height, depth).

  • Binary Trees: Representation, traversal algorithms (Pre-order, In-order, Post-order).

  • Binary Search Trees (BST): Searching, insertion, deletion.

  • Height-Balanced Trees: Introduction to AVL trees (insertion, rotation, deletion).

  • Threaded Binary Trees.

  Module 7: Sorting and Searching (8 Hours)

  • Searching: Linear Search, Binary Search.

  • Sorting:

    • Bubble Sort

    • Selection Sort

    • Insertion Sort

    • Merge Sort

    • Quick Sort

    • Shell Sort

  • Comparative analysis of sorting and searching algorithms.

  Module 8: Hashing (5 Hours)

  • Introduction to Hashing and Hash Tables.

  • Hash Functions.

  • Collision Resolution Techniques: Separate Chaining and Open Addressing (Linear Probing, Quadratic Probing).