Data Structures and Concurrency

This repository contains a collection of notes, examples, and exercises on various data structures and concurrency concepts. The topics covered include:

Data Structure

• Collections: A high-level overview of collections, including sets, lists, and maps. Implementation details and trade-offs are discussed.
• ArrayLists: A dynamic array implementation, including insertion, deletion, and searching operations.
• Linked Lists: A node-based implementation, including insertion, deletion, and searching operations.
• Stacks and Queues: Last-In-First-Out (LIFO) and First-In-First-Out (FIFO) data structures, including implementation and usage examples.
• Trees and Binary Search Trees: A hierarchical data structure, including traversal algorithms (inorder, preorder, postorder) and search operations.

Concurrency

• Recursion: A fundamental concept in programming, including recursive function calls, memoization, and dynamic programming.
• Efficiency of Algorithms: Analysis of algorithmic complexity, including Big O notation, time and space complexity, and trade-offs.
• Profiling: Techniques for measuring and optimizing the performance of algorithms and data structures.

What I Learned

Through this repository, I gained a deeper understanding of the following concepts:

1. The importance of choosing the right data structure for a problem, considering factors such as time and space complexity.
2. The trade-offs between different data structures, including arrays, linked lists, stacks, and queues.
3. The power of recursion in solving complex problems, and the importance of memoization and dynamic programming.
4. The need to consider concurrency and parallelism in modern programming, including the use of threads and locks.
5. The importance of profiling and optimizing algorithms for performance