This is my basic Computer Engineering Study Guide and where I did some experiments with algorithms

I am working on it to have some source of knowledge to share with everyone who wants to learn more about computer engineering

• Data Structures

1. Introduction to Data Structures:

   • Definition and importance of data structures.
   • Basic operations on data structures (insertion, deletion, searching).

2. Arrays:

   • Introduction.
   • One-dimensional and multi-dimensional arrays.
   • Accessing, inserting, and deleting elements in arrays.

3. Linked Lists:

   • Singly linked lists, doubly linked lists, and circular linked lists.
   • Insertion, deletion, and searching in linked lists.
   • Implementations and time complexity.

4. Stacks:

   • Definition and characteristics of stacks.
   • Basic operations (push, pop, peek).
   • Applications of stacks and implementations.

5. Queues:

   • Definition and characteristics of queues.
   • Basic operations (enqueue, dequeue, peek).
   • Priority queues and implementations.

6. Trees:

   • Binary trees, binary search trees.
   • Tree traversals (pre-order, post-order, in-order).
   • Basic operations and time complexity.

7. Graphs:

   • Definition of graphs, representations (adjacency matrix, adjacency list).
   • Graph traversals (DFS, BFS).
   • Common graph algorithms (Dijkstra's, Prim's, Kruskal's).

8. Hash Tables:

   • Concept of hash tables and hash functions.
   • Collision resolution (chaining, open addressing).
   • Implementations and time complexity.

9. Heaps:

   • Definition and characteristics of heaps.
   • Basic operations (insertion, deletion, heapify).
   • Heapsort and applications of heaps.

10. Balanced Trees:

    • AVL trees, red-black trees.
    • Tree balancing and operations.
    • Time complexity in balanced trees.

11. Directed and Undirected Graphs:

    • Cycles in graphs, topological sorting.
    • Minimum spanning trees (MST).
    • Bellman-Ford and Floyd-Warshall algorithms.

12. Advanced Data Structures:

    • B-trees, tries, segment trees.
    • Persistent data structures.
    • Data compression and compact data structures.

13. Time and Space Complexity:

    • Asymptotic analysis (O, Ω, Θ notations).
    • Understanding the complexity of algorithms and data structures.

14. Search and Sorting Algorithms:

    • Search algorithms (linear search, binary search).
    • Sorting algorithms (insertion sort, merge sort, quicksort).

15. Practical Applications:

    • Development and implementation of algorithms in real projects.
    • Solving programming problems online and in competitions.