viva.pdfjgthjghngnfgnfgnhfnhvndrcfbdrfgd
- 1. A TRAINING REPORT Submitted to LOVELY PROFESSIONAL UNIVERSITY PHAGWARA, PUNJAB From 06/05/24 to 07/18/24 SUBMITTED BY Mansi Sharma 1220777
- 3. Dr. Sudha Sankar I am deeply grateful to Dr. Sudha Sankar for his invaluable guidance and support throughout the internship. His insightful feedback and expert advice were instrumental in shaping my understanding of complex algorithms and data structures. His enthusiasm for teaching and deep knowledge in the field greatly inspired me and enhanced my learning experience. 3
- 4. UNDERSTANDING DATA STRUCTURES DEFINITION: DATA STRUCTURES ARE ESSENTIAL COMPONENTS IN COMPUTER SCIENCE THAT ENABLE THE EFFICIENT STORAGE, RETRIEVAL, AND MANIPULATION OF DATA. THEY PROVIDE A WAY TO ORGANIZE AND MANAGE DATA SO THAT IT CAN BE ACCESSED AND MODIFIED WITH OPTIMAL PERFORMANCE. TYPES OF DATA STRUCTURES 1. LINEAR DATA STRUCTURES: ARRAY: AN ARRAY IS A COLLECTION OF ELEMENTS STORED IN CONTIGUOUS MEMORY LOCATIONS. EACH ELEMENT IN THE ARRAY CAN BE ACCESSED DIRECTLY USING AN INDEX, MAKING IT EFFICIENT FOR READING AND WRITING DATA. .
- 5. STACK: A STACK OPERATES ON THE LAST IN, FIRST OUT (LIFO) PRINCIPLE, WHERE THE LAST ELEMENT ADDED IS THE FIRST TO BE REMOVED. THIS STRUCTURE IS PARTICULARLY USEFUL IN SCENARIOS LIKE FUNCTION CALL MANAGEMENT IN PROGRAMMING LANGUAGES, WHERE THE MOST RECENT FUNCTION CALL IS THE FIRST TO BE RESOLVED. QUEUE: A QUEUE FOLLOWS THE FIRST IN, FIRST OUT (FIFO) PRINCIPLE, WHERE THE FIRST ELEMENT ADDED IS THE FIRST TO BE REMOVED. THIS MAKES QUEUES IDEAL FOR MANAGING TASKS IN ORDER, SUCH AS IN JOB SCHEDULING SYSTEMS WHERE THE FIRST TASK SUBMITTED IS THE FIRST TO BE EXECUTED.
- 6. . Linked List: Unlike arrays, linked lists store elements in nodes, each of which contains a reference (or link) to the next node in the sequence. This allows for dynamic memory allocation, where the list can grow and shrink as needed. 2. Non-Linear Data Structures: Tree: A tree is a hierarchical data structure that consists of nodes connected by edges. Each node in a tree has a parent node (except the root node) and zero or more child nodes. Trees are widely used to represent hierarchical relationships, such as file systems, where directories contain subdirectories and files. A special type of tree, the Binary Search Tree (BST), allows for efficient searching, insertion, and deletion operations.
- 7. ARRAYS AND LINKED LISTS ARRAYS: ARRAYS ARE ONE OF THE MOST BASIC AND WIDELY USED DATA STRUCTURES. THEY PROVIDE FAST ACCESS TO ELEMENTS THROUGH INDEXING, WHICH IS A SIGNIFICANT ADVANTAGE IN SCENARIOS WHERE QUICK DATA RETRIEVAL IS NECESSARY, SUCH AS IN SEARCHING ALGORITHMS AND DYNAMIC PROGRAMMING. LINKED LISTS: LINKED LISTS ARE MORE FLEXIBLE THAN ARRAYS, ALLOWING FOR DYNAMIC MEMORY ALLOCATION. THIS MAKES THEM IDEAL FOR APPLICATIONS WHERE THE SIZE OF THE DATA STRUCTURE IS NOT KNOWN IN ADVANCE, OR WHERE FREQUENT INSERTIONS AND DELETIONS ARE REQUIRED. Memory Usage: Linear data structures like arrays and linked lists have different memory footprints. Arrays require a contiguous block of memory, which can be inefficient if the size of the array is overestimated. Linked lists, on the other hand, use memory more flexibly but require additional space for storing pointers.
- 8. QUEUES: QUEUES OPERATE ON THE PRINCIPLE OF FIRST IN, FIRST OUT (FIFO), MEANING THE FIRST ELEMENT ADDED TO THE QUEUE IS THE FIRST TO BE REMOVED. THIS STRUCTURE IS WIDELY USED IN SCENARIOS WHERE ORDER NEEDS TO BE PRESERVED, SUCH AS IN JOB SCHEDULING SYSTEMS, WHERE TASKS NEED TO BE PROCESSED IN THE ORDER THEY ARRIVE. . TREES AND GRAPHS TREES: TREES ARE HIERARCHICAL DATA STRUCTURES THAT CONSIST OF NODES CONNECTED BY EDGES. EACH TREE HAS A ROOT NODE, AND EVERY NODE (EXCEPT THE ROOT) HAS EXACTLY ONE PARENT. THE STRUCTURE OF A TREE RESEMBLES A BRANCHING STRUCTURE, MAKING IT IDEAL FOR REPRESENTING HIERARCHICAL RELATIONSHIPS. GRAPHS: GRAPHS ARE VERSATILE DATA STRUCTURES USED TO MODEL RELATIONSHIPS BETWEEN ENTITIES. A GRAPH CONSISTS OF A SET OF VERTICES (NODES) CONNECTED BY EDGES. Stacks and Queues Stacks: Stacks: Stacks are a fundamental data structure used in various computing applications. They operate on the principle of Last In, First Out (LIFO), meaning the last element added to the stack is the first to be removed.
- 9. HASH TABLES: A HASH TABLE IS A DATA STRUCTURE THAT IMPLEMENTS AN ASSOCIATIVE ARRAY, MAPPING KEYS TO VALUES. HASH TABLES ARE HIGHLY EFFICIENT, WITH AN AVERAGE TIME COMPLEXITY OF O(1) FOR BOTH INSERTIONS AND LOOKUPS, MAKING THEM IDEAL FOR APPLICATIONS LIKE DATABASES, CACHING, AND IMPLEMENTING DYNAMIC DICTIONARIES. SORTING ALGORITHMS BUBBLE SORT BUBBLE SORT IS A SIMPLE SORTING ALGORITHM THAT REPEATEDLY STEPS THROUGH THE LIST, COMPARES ADJACENT ELEMENTS, AND SWAPS THEM IF THEY ARE IN THE WRONG ORDER. THIS PROCESS CONTINUES UNTIL THE LIST IS SORTED. Hashing: Hashing is a technique used to map data of arbitrary size to fixed size values, known as hash codes. These hash codes are then used as indices in a hash table, where the corresponding data is stored.
- 10. Quick Sort Quick Sort is a divide-and-conquer algorithm that selects a 'pivot' element and partitions the array into elements less than and greater than the pivot. It then recursively sorts the sub-arrays. Searching Algorithms Linear Search Linear Search scans each element in the array sequentially until the desired element is found. Binary Search Binary Search works on sorted arrays by repeatedly dividing the search interval in half.
- 11. ALGORITHM COMPLEXITY AND BIG O NOTATION • Big O Notation describes the upper bound of the time complexity of an algorithm. It provides an asymptotic analysis of the algorithm's performance. • O(1): Constant time complexity. • O(n): Linear time complexity. • O(n^2): Quadratic time complexity. • O(log n): Logarithmic time complexity. 11
- 12. THANK YOU!!