CST 201 Data Structures Old Question Papers KTU

 

 

 

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Data Structures CST 201 KTU Third Semester Syllabus Notes and Solved Questions- Dr Binu V P 984739060

Dr BinuV P -About Me About The Course Syllabus Data Structures Lab CSL 201 ( Do the Programs to Understand the Concept Well) Model Question Paper CST 201 Data Structures University Question Papers CST 201 Data Structures Module-1 Basic Concepts of Data Structures 1.1 System Life Cycle 1.2 Algorithms , Performance Analysis  1.3 Space Complexity, Time Complexity  1.4 Asymptotic Analysis and Notations  1.5 Complexity Calculation of Simple Algorithms Module-2 Arrays and Searching  2.1 Polynomial representation using Arrays  2.2 Sparse matrix  2.3 Stacks  2.4 Queues 2.5 Circular Queues  2.6 Priority Queues 2.7 Double Ended Queues 2.8 Infix to  Postfix Conversion  2.9  Evaluation of Postfix Expression 2.10 Linear Search  2.11 Binary Search Module-3 Linked List and Memory Management 3.1 Linked List -Self Referential Structures  3.2 Dynamic Memory Allocation  3.3 Singly Linked List-Operations on Linked List 3.4 Doubly Linked List 3.5 Circular Linked List  3.6 Stack using Linked List  3.7 Queue
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Depth First Search DFS

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Depth-First Search (DFS) is a fundamental graph traversal algorithm that explores as far as possible along each branch before backtracking. It's often used to explore and analyze the connectivity of graphs, find paths, and solve problems related to connected components. Algorithm: The DFS algorithm operates on a graph, starting from an initial vertex. It maintains a stack (or uses recursion) to keep track of the vertices to visit. Initialization: Create a boolean array to mark visited vertices. Initialize all vertices as not visited. DFS Function: Choose a starting vertex (if the graph is disconnected, this process is repeated for unvisited vertices). Mark the chosen vertex as visited. Explore the chosen vertex's neighbours: If a neighbour is not visited, recursively call the DFS function on that neighbour. Repeat until all reachable vertices are visited. Here's a simplified recursive implementation in pseudocode: procedure DFS(vertex): mark vertex as visited for ea
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Binary Search Tree ( BST) and operations

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A binary search tree (BST) is a data structure that organizes elements in a hierarchical structure, allowing for efficient search, insertion, and deletion operations. Here are the key properties and operations associated with a binary search tree: Properties of a Binary Search Tree: Binary Tree Structure:      Each node in the tree has at most two children: a left child and a right child.      The left subtree of a node contains only nodes with values less than the node's value.      The right subtree of a node contains only nodes with values greater than the node's value. Ordering Property:      For any node n , all nodes in its left subtree have values less than n, and all nodes in its right subtree           have values greater than n . No Duplicates:     A binary search tree does not allow duplicate values. Example: Figure  shows a binary search tree. Notice that this tree is obtained by inserting the values 13, 3, 4, 12, 14, 10, 5, 1, 8, 2, 7, 9, 11, 6, 18 in that order,
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