Self-Balancing AVL Trees in Python

When working with standard binary search trees, unbalanced data can severely degrade search performance. Understanding self-balancing structures is essential for writing efficient, high-performance algorithms. This text-only course guides you from foundational tree structures to implementing fully self-balancing AVL trees. You will learn how to maintain optimal search times by restructuring nodes dynamically. What you will learn: Understand the core concepts of binary search trees and node balance factors; Learn the mechanics of single and double tree rotations; Apply balance restoration logic during element insertion; Practice calculating node heights and identifying balance violations; Write clean, modern Python code using type hints for node and tree classes. The course begins with foundational definitions of tree data structures, node properties, and balance factors before moving into step-by-step written walkthroughs of rotation logic and balance restoration. This course is designed for beginner computer science students and programmers who want to strengthen their data structure fundamentals; no prior experience with tree algorithms is required. Start building more efficient algorithms today.