Part A. Defining data, data types, data structure classification of data structures (linear and non-linear) understanding data structure and storage structure
Part B. Defining an algorithm, its properties growth of a function complexityasymptotic analysis asymptotic notations for complexity analysis comparing standard growth rates
Part C. Linear array: its representation in memory, traversal, and operations on it 2D array: its representation in memory, traversal, and operations on it Generalized representation of multi-dimensional array on memory.
Part D. Linked List: Node structure, traversal, insertion and deletion from a linked list circular linked list two way linked list.
Part E: Sparse matrix, representation using triplet array, chain representations (single chain, row chain, orthogonal list)
Part F: Stack (operations on stack, array and linked list representation, application in conversion of expression from infix to postfix, and in evaluation of postfix notation)
Part G: Queue (operations on queue, array and linked list representation, deque, priority queue)
Part H: Searching algorithms (linear search, binary search, Fibonacci search)
Part I: Sorting algorithms (bubble sort, selection sort, insertion sort, merge sort, quick sort)
Part J: Tree (Terminologies, representation, binary tree and its traversal)
Part K: Binary search tree and operations on it, properties of BST, binary expression tree
Part L: AVL tree and operations on it
Part M: m-way search tree and B-tree
Part N: Heap (min-heap and max-heap)
Part O: Graphs (terminologies, storage, Dijkstra's shortest path algorithm, traversal: DFS, BFS, generalized shortest path problems, Bellman-Ford algorithm, Floyd-Warshall Algorithm)
Part P: Spanning tree, Minimum spanning tree, Prim's and Kruskal's algorithms

To develop students’ knowledge in data structures and the associated algorithms.

To introduce the concepts and techniques of structuring and operating on abstract Data Types in problem solving

To discuss common sorting, searching algorithms. Also the complexity and comparisons among these various techniques

At the end of the course, students shall be equipped with the knowledge to:
CO1: follow the operations for maintaining common data structures and recognize the associated algorithms’ complexity.
CO2: design appropriate data structures for solving computing problems.
CO3: develop and analyse algorithms which can be implemented in programming languages.

M.A. Weiss, Data Structures and Algorithm Analysis in C, Pearson Education , ISBN:9798178081679

E. Horowitz, S. Sahni and S. Anderson-Freed, Fundamentals of Data Structures, Computer Science Press , ISBN: 978-0716780427

A.V. Aho, J.E. Hopcroft, and J.D. Ullman, Data Structures & Algorithms, Pearson Education India , ISBN: 9788178081021

T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, The MIT Press , ISBN: 978-0262033848