
Explore the basics of a single linked list by creating nodes, performing insertion and deletion, and looking up elements, with each node containing data and a next address.
Demonstrate the insertion operation in a single linked list, including insert first and insert after, using a four-node example (5, 10, 20, 40).
Demonstrates deleting the first node and deleting after a given node in a single linked list. Shows updating the head and links and freeing the removed node.
Traverse a single linked list with a header pointer, printing each node’s data (5, 10, 20, 40) as you move to the next node until null.
Explore stack operations with push and pop, understand top, empty, and full conditions, and recognize overflow and underflow; see how stacks support recursion and infix to postfix conversions.
Learn that a binary tree is a subclass of trees consisting of nodes with a designated root, whose remaining nodes split into two disjoint subtrees: left and right.
Discover inorder traversal of a binary search tree, visiting left subtree, then the node, and finally the right subtree using a recursive algorithm.
This lecture explains bubble sort by repeatedly comparing adjacent elements, swapping out-of-order pairs, and moving the largest element to the end each pass, with early termination if no swaps occur.
Fundamentals of Data Structures: An Algorithmic Approach offers engineering students a comprehensive understanding of how data can be efficiently organized, stored, and processed to solve computational problems. The course begins by introducing the importance of selecting appropriate data structures and the role they play in designing effective algorithms. Students explore a wide range of linear and non-linear structures, including arrays, linked lists, stacks, queues, trees, heaps, and graphs. Each structure is examined in terms of its properties, operations, applications, and performance characteristics.
A major focus of the course is helping students understand how algorithms interact with these data structures. Through detailed explanations, visual demonstrations, and animated representations, students learn to trace the flow of algorithms such as searching, sorting, traversing, and manipulating data. Special emphasis is placed on analyzing time and space complexity using asymptotic notation, enabling learners to evaluate the efficiency of different algorithmic approaches.
The course is particularly supportive for students who may find coding difficult or struggle to visualize algorithmic steps. By using animations and simplified representations, the course builds intuition and confidence in algorithmic thinking. As students progress, they gradually develop strong problem-solving abilities and the capability to choose the most suitable data structure for a given task.
By the end of the course, learners are well-prepared to design robust data-handling mechanisms, optimize computational processes, and apply algorithmic reasoning to a variety of real-world engineering challenges. This foundational knowledge serves as a stepping stone for more advanced subjects in computer science and software development.