Welcome to the Coding Interview Practice Repository! This repository is designed to help you prepare for coding interviews by providing a curated collection of questions and solutions.
In today's tech industry, coding interviews are an essential part of the hiring process. Whether you're applying for a software engineering position at a large tech company or a startup, chances are you'll encounter technical interviews that assess your problem-solving skills, algorithmic thinking, and coding proficiency.
This repository aims to assist you in your interview preparation journey by offering a diverse set of coding problems commonly asked during technical interviews. These problems cover various topics ranging from data structures and algorithms to system design and software engineering concepts.
- ✅ Curated Collection
- ✅ Diverse Topics
- ✅ Organized Structure
- ✅ Getting Started Guide
- ✅ Contributions Welcomed
- ✅ Responsive Community
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Clone this repository to get started.
git clone https://github.com/wesleybertipaglia/interview
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Navigate to the repository directory:
cd interview
The repository is organized into folders based on different topics and difficulty levels. Each folder contains one or more coding problems along with their solutions.
Here's a breakdown of the directory structure:
- Data Structures
- Arrays
- Linked Lists
- Stacks and Queues
- Trees and Graphs
- ...
- Algorithms
- Sorting and Searching
- Dynamic Programming
- Greedy Algorithms
- Backtracking
- ...
- System Design
- Object-Oriented Design
- Concurrency
- Software Engineering
- ...
We welcome contributions to this repository! Please follow the contributing guidelines to submit your work. Your contributions will help make this repository a valuable resource for the community.
This repository is licensed under the [MIT]. See the LICENSE file for details.
A big thank you to the open-source community for inspiring and supporting this project.
The solutions provided in this repository are meant for learning purposes and may not always represent the most optimal solution. It's essential to understand the underlying concepts and reasoning behind each solution rather than focusing solely on the code itself.
