Data Structures and Algorithms-Deep Dive into Core Concepts

Delete an element from an array by removing the value and reorganizing the rest; end deletions are simple, while middle deletions require shifting elements left and updating the upper bound.

Identify the key trade-offs of arrays by weighing fixed size, direct index access, and contiguous memory advantages against inflexibility, costly middle insertions, and resizing challenges.

Explore linked lists as a dynamic alternative to arrays, where each node holds data and a link, enabling efficient insertions and deletions without shifting elements.

Explore singly linked lists where each node holds data and a next pointer, and learn traversal from the start to null, along with core operations like insertion, deletion, and searching.

Master insertion after and before a given node in a singly linked list by locating the target value, allocating a new node, and relinking next pointers.

Explore deletion in singly linked lists by removing the first or last node, updating the start pointer, and freeing memory; it also covers middle deletions and using a pre pointer.

Learn how to delete a middle node in a singly linked list by locating the node after a given value, using two pointers, and bypassing the target node.

Learn to insert at the beginning and at the end of a doubly linked list by creating a new node, updating the start reference, and enabling forward and backward links.

Explore singly, doubly, and circular linked lists, highlighting dynamic size, bidirectional traversal, and memory efficiency, with practical applications from memory management to round robin scheduling.

Implement a stack using an array with a top pointer and max capacity. Learn push, pop, and peek operations, including overflow, underflow checks, and array-based top handling.

Learn how a stack implemented with an array provides direct access and constant-time push and pop, highlighting advantages like simplicity and contiguous memory and applications in expression evaluation and recursion.

Learn to implement a stack with a linked list, where each node stores data and next address, and top points to the top, enabling constant time push, pop, and peek.

Learn infix to postfix conversion using a stack to handle operands, operators, and open parentheses, applying precedence to produce a final postfix expression.

Understand the queue data structure, its first-in-first-out principle, and two ends, front and rear, enabling enqueue, dequeue, and peek operations with O(1) time.

Explore dequeue operations in a circular queue, including underflow checks and front and rear updates, wraparound handling, and buffering and round robin scheduling applications.

Explore various tree types, including general trees, forests, binary trees, binary search trees, expression trees, AVL trees, and B trees, along with their operations and balance aspects.

Explore binary trees, their properties and types, and common drawbacks, then examine real-world applications from hierarchical data and database indexing to AI, compression, and memory representation.

Explore the three tree traversal methods—preorder, inorder, and postorder—and learn to visit each node exactly once in a non-linear tree by following root, left subtree, and right subtree steps.

Demonstrates postorder traversal by visiting the left subtree, then the right subtree, and finally the root, with a step-by-step example on a tree.

Explore graphs as non-linear data structures with vertices and edges, covering degree, paths, cycles, and directed, undirected, weighted, unweighted, cyclic, and acyclic types.

Learn Kruskal's algorithm for building a minimum spanning tree from a weighted undirected graph using a greedy approach, sorting edges in ascending order and using disjoint sets to avoid cycles.

Dijkstra's algorithm computes the single-source shortest paths from one node to all others in directed or undirected graphs with non-negative edge weights.

Explore quadratic probing as a collision resolution technique for open addressing, reducing primary clustering and guiding hash-table lookups with a quadratic probing sequence across programming languages, databases, and embedded systems.

Examine how binary search operates on sorted lists by repeatedly comparing the middle element, using low and high pointers, and dividing the problem into subproblems for efficiency.

Examine how quicksort uses a pivot and partitioning to create left and right subarrays. See step-by-step swapping to place the pivot in its final position.

Explore the quicksort algorithm, its pseudocode and partitioning steps, and analyze time complexity across best, worst, and average cases with pivot and subarrays.