DataStructures and Algorithms:Zero to Hero

Explore the queue as a linear data structure that uses first-in, first-out, with rear insertions and front deletions, handling overflow and underflow and real-world applications like printers, buffers, and playlists.

Explore tree structures, including root, parent and child relationships, left subtrees and right subtrees, siblings, and expression trees with operands at leaves and operators at internal nodes, using a stack.

Explore the binary tree data structure, with root, left and right children, and leaves, and learn traversal orders: preorder, inorder, postorder, and level by level, complete, full, and extended trees.

Explore binary search trees, a tree with left values smaller and right values larger, enabling efficient search, insertion, and deletion with cases for no children, one child, or two children.

Explains AVL trees, self-balancing binary search trees, balance factors of -1, 0, 1, and how single and double rotations (left, right, left-right, right-left) rebalance insertions.

Learn how a binary heap, a complete binary tree with min-heap and max-heap properties, supports insertion and deletion while maintaining the heap property.

Explore directed and undirected graphs, weighted edges, complete graphs, loops, and strongly or weakly connected graphs. Master depth-first search traversal to build spanning trees and identify graph components.