Data Structures: Active Learning with C++ and Python

This introductory module covers fundamental concepts in computer science including computational complexity analysis, C++ programming basics, random number generation, binary operations, terminal usage, and version control with Git. The module establishes essential foundations needed for understanding data structures.

This module explores basic data structures including array lists, linked lists, skip lists, and abstract data types. It covers implementation and operations of fundamental structures like deques, queues, and stacks, along with iteration concepts.

This module delves into various tree data structures including heaps, binary search trees, AVL trees, red-black trees, and B-trees. It covers tree operations, balancing techniques, and time complexity analysis of tree operations.

This module covers graph theory fundamentals, different graph representations, and essential graph algorithms including breadth-first search, depth-first search, Dijkstra's algorithm, and minimum spanning trees using Prim's and Kruskal's algorithms.

This module explores hash functions, hash tables, and collision resolution strategies including open addressing, closed addressing, and cuckoo hashing. It covers probability analysis of collisions and implementation of hash maps.

This module focuses on practical implementation of a lexicon using various data structures including linked lists, arrays, binary search trees, hash tables, multiway tries, and ternary search trees. It compares different implementation approaches.

This module covers information theory, entropy, and file compression techniques. It includes implementation of coding trees and bitwise I/O operations for data compression applications.

This module provides comprehensive summaries of all data structures covered in the course, reviewing their characteristics, use cases, and trade-offs.