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https://github.com/zakircodearchitect/data-structures

This course is designed to provide a comprehensive understanding of fundamental data structures and their applications. From arrays and linked lists to trees and graphs, each topic is covered in detail with explanations, examples, and exercises to reinforce learning.
https://github.com/zakircodearchitect/data-structures

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This course is designed to provide a comprehensive understanding of fundamental data structures and their applications. From arrays and linked lists to trees and graphs, each topic is covered in detail with explanations, examples, and exercises to reinforce learning.

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# Data-Structures

This course is designed to provide a comprehensive understanding of fundamental data structures and their applications. From arrays and linked lists to trees and graphs, each topic is covered in detail with explanations, examples, and exercises to reinforce learning.



Welcome to the Data Structure Course repository! This repository contains materials and resources for learning about various data structures commonly used in computer science.

# About

This course is designed to provide a comprehensive understanding of fundamental data structures and their applications. From arrays and linked lists to trees and graphs, each topic is covered in detail with explanations, examples, and exercises to reinforce learning.

# Table of Contents

Introduction to Data Structures

Arrays

Linked Lists

Stacks and Queues

Trees

Graphs

Sorting and Searching Algorithms

Advanced Data Structures

Additional Resources



# Introduction to Data Structures

Overview of data structures and their importance in computer science.

Basic terminology: elements, data types, operations.

Types of data structures: linear (arrays, linked lists) and nonlinear (trees, graphs).



# Arrays

Definition and properties of arrays.

Operations: accessing, inserting, deleting elements.

Multi-dimensional arrays and their applications.

Time and space complexity analysis.



# Linked Lists

Singly linked lists, doubly linked lists, and circular linked lists.

Implementation of basic operations: insertion, deletion, traversal.

Comparison with arrays and their advantages/disadvantages.

Applications in practice.



# Stacks and Queues

Introduction to stacks and queues: definitions, characteristics.

Implementations using arrays and linked lists.

Basic operations: push, pop (for stacks); enqueue, dequeue (for queues).

Applications: expression evaluation, reversing strings, etc.



# Trees

Introduction to trees: terminology, definitions.

Binary trees, binary search trees (BST).

Traversal algorithms: in-order, pre-order, post-order.

Operations: insertion, deletion, searching.

Balanced trees: AVL trees, red-black trees.



# Graphs

Basics of graph theory: definitions, types of graphs.

Representation of graphs: adjacency matrix, adjacency list.

Graph traversal algorithms: depth-first search (DFS), breadth-first search (BFS).

Applications: shortest path algorithms (Dijkstra's, Bellman-Ford), minimum spanning tree (Prim's, Kruskal's).



# Sorting and Searching Algorithms

Overview of sorting algorithms: bubble sort, selection sort, insertion sort, merge sort, quick sort.

Comparison of sorting algorithms based on time and space complexity.

Overview of searching algorithms: linear search, binary search.



# Advanced Data Structures

Hash tables and hash functions.

Heaps and priority queues.

Trie data structure.

Disjoint-set data structure (Union-Find).



# Additional Resources

Links to additional readings, online courses, and tutorials for further exploration.

Getting Started

To get started with the course, simply clone this repository and navigate through the topics listed in the table of contents. Each topic folder contains lecture notes, code examples, and exercises to help you understand and practice the concepts.



# Prerequisites

Basic understanding of programming concepts (e.g., variables, loops, functions).

Familiarity with at least one programming language (e.g., Python, Java, C++).



# How to Contribute

If you find any errors, typos, or have suggestions for improvements, feel free to open an issue or submit a pull request. Contributions from the community are highly appreciated!



# License

This course is licensed under the MIT License.