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Efficient data processing requires mastering sorting and searching algorithms. This module covers widely used sorting techniques, such as bubble sort, quicksort, and merge sort, emphasizing their efficiency and real-world applications. Learners will implement these algorithms in .NET Core, analyze their time and space complexity, and explore searching techniques like linear search and binary search. By applying binary search within sorted data sets, participants will enhance back-end system performance and evaluate trade-offs between different algorithmic approaches.

Hierarchical and interconnected data structures are essential for many back-end applications. This module introduces tree structures, including binary trees and balanced trees (e.g., AVL trees), along with traversal techniques such as preorder, inorder, and postorder traversal. Learners will also explore graph theory concepts, implementing traversal algorithms like Depth-First Search (DFS) and Breadth-First Search (BFS) to solve practical back-end challenges. Analyzing the time and space complexity of these structures will help developers optimize system performance.

Advanced problem-solving techniques, such as dynamic programming and greedy algorithms, play a crucial role in optimizing back-end systems. This module covers the implementation of dynamic programming solutions (e.g., Fibonacci sequence, longest common subsequence) and explores how hashing and hash tables improve search operations. Learners will apply these advanced algorithms to real-world back-end tasks, such as database query processing and authentication systems, while evaluating trade-offs between different algorithmic approaches.

AI-assisted development tools like Microsoft Copilot can streamline the implementation and optimization of complex algorithms. This module explores how Copilot enhances back-end efficiency by assisting in writing and optimizing data structures and algorithms in .NET Core. Learners will use Copilot to implement advanced algorithms like Dijkstra's shortest path and A* search, analyze AI-generated code for performance improvements, and complete a comprehensive optimization project. By leveraging Copilot, developers can refine their approach to algorithmic design and scalability.