Arrays - Structures - Pointers - Data structures and its types - Abstract Data Type - Algorithms - Asymptotic notations - Time complexity analysis. Infix to Postfix conversion - Infix to Prefix conversion - Evaluation of Postfix expression - Evaluation of Prefix expression. Stack and queues, Linked list, Circular linked List, Doubly linked list.

Basic sorting algorithms: : Bubble sort, Insertion sort, Selection sort. Trees, Binary tree, Binary Search Tree, Tree traversals, Height balanced tree, Red-black tree, B-tree. Heap as data structure.

Greedy algorithms: Coin change problem, activity selection, Minimum Spanning Tree, Single source shortest path, Dijkstra Algorithm, Knapsack problem. Divide and Conquer technique. Merge sort, quick sort. Solving Recurrence relations. Dynamic programming: matrix chain multiplication, all pair shortest path algorithm.

Graphs: Definition, Basic terminology, Graph algorithms: Warshall algorithm, Depth First Search, Breadth First Search. NP completeness.

To understand and apply suitable data structures in all possible applications.

To develop and design algorithms using the data structures concept.

To analyse the efficiency of algorithms developed.

4. To develop skills to apply appropriate data structures in problem solving.

Understand the basic concepts of data structures and algorithms.

Derive the efficiency of algorithms.

Choose appropriate linear and non-linear data structures to develop any application.

Apply the suitable sorting and searching algorithms in real world applications.

Create effective solution for challenging real-world problems.

Thomas H. Cormen. Charles E. Leiserson. Ronald L. Rivest. Clifford Stein, , Introduction to Algorithms, Cambridge, MA: The MIT Press 2001.

Debasis Samanta, Classical Data Structures, Paperback, 2nd edition.

Seymour Lipschutz, Data Structures with C, Tata McGraw Hill 2nd Edition

Narasimha Karumanchi, , Data Structures and Algorithms Made Easy, Fifth Edition, Career Monk, 2017

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