LPU - Lovely Professional University
What you'll learn

The course is designed to build a strong foundation in Data Structures and Algorithms through a balanced approach of theory, coding, and problem-solving and side by side building their professional profile. Students will learn core concepts such as arrays, linked lists, stacks, queues, trees, graphs, and hashing, along with fundamental algorithmic techniques like searching, sorting, and recursion. Emphasis is placed on developing logical thinking and analytical skills by solving real-world and interview-oriented problems, including curated LeetCode exercises. By the end of the course, learners will be able to design efficient solutions, analyze algorithm performance, and apply data structures effectively in programming tasks thus making them placement ready.

  • Basics of Data Structures, Classification of Data Structures (Linear and Non-linear), Abstract Data Types , Introduction to Algorithms, Time Complexity, Space Complexity, Asymptotic Notations (Big-O, Theta, Omega), Algorithmic Trade-offs
  • Array Representation in Memory, Array Traversal, Insertion and Deletion in Arrays, Linear Search, Binary Search (Iterative and Recursive), Bubble Sort, Selection Sort, Insertion Sort, Comparison of Sorting Algorithms, Problem Solving using Arrays
  • Introduction to Linked Lists, Singly Linked List, Doubly Linked List, Circular Linked List, Header Linked List, Insertion in Linked List, Deletion in Linked List, Traversal of Linked List, Applications of Linked Lists
  • Stack Concept and Operations (Push, Pop, Peek), Stack Implementation using Array and Linked List, Applications of Stack (Expression Evaluation, Infix to Postfix), Queue Concept and Operations, Simple Queue, Circular Queue, Priority Queue, Deque (Double Ended Queue), Applications of Queue
  • Recursion Basics, Recursive Problem Solving, Tower of Hanoi, Tree Terminology, Types of Trees, Binary Tree Representation, Tree Traversals (Inorder, Preorder, Postorder), Binary Search Tree (Insertion, Deletion, Searching), AVL Trees and Rotations
  • Heaps (Min Heap and Max Heap), Heap Sort, Graph Representation (Adjacency Matrix and List), Breadth First Search (BFS), Depth First Search (DFS), Shortest Path Algorithms (Dijkstra, Floyd-Warshall), Hashing, Hash Functions, Collision Resolution Techniques

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Amit Sharma
Assistant Professor

He brings over 15 years of rich experience in teaching and mentoring, consistently guiding students toward academic excellence and practical competence. With deep expertise across multiple core domains of computer science, he emphasizes the importance of building strong conceptual foundations complemented by hands-on projects and interactive learning approaches. His teaching philosophy focuses on making complex concepts accessible, engaging, and industry-relevant. Beyond the classroom, he actively contributes to strengthening industry–academia collaboration, ensuring that students are well-prepared to meet real-world challenges and evolving technological demands. He is also certified under the NASSCOM FutureSkills initiative in Blockchain Technology, having earned a Silver Badge from NIELIT Kolkata. This recognition reflects his commitment to staying aligned with emerging technologies and continuously enhancing his professional capabilities.