Welcome to my repository housing a diverse collection of data structures, algorithms, and code challenges. This repository began its journey in 2020 during my studies at a coding bootcamp and has since evolved into a growing compendium.
This repository is a testament to my studies and practical implementations of data structures, algorithms, and code challenges. It's a curated collection that I've cultivated to encapsulate my understanding and work in these areas of computer science.
Here, you'll find a multitude of resources:
- Code Implementations: Written in various programming languages to offer a comprehensive view of the concepts.
- Notes: Detailed explanations and insights I've gathered during my studies on each topic.
- Tests: Rigorous test suites evaluating implementations under different scenarios—covering happy paths, worst-case scenarios, empty inputs, and edge cases.
- Whiteboard Visuals: Illustrative representations aiding in the visual comprehension of solution approaches.
The repository's diverse content spans multiple programming languages and is designed to foster a holistic understanding of computer science as a discipline.
Feel free to explore, contribute, and utilize these resources to enhance your understanding of data structures, algorithms, and coding challenges!
Data structures are a way of organizing and storing data to perform operations efficiently. They define how data is stored, accessed, and manipulated.
Data structures provide a way to manage and organize data to meet the specific requirements of different algorithms. Choosing the right data structure is important for optimizing the performance of algorithms.
Algorithms are step-by-step formulas for solving problems and performing tasks. They are a set of rules or instructions that specify the sequence of operations to be performed to solve a particular problem.
Algorithms provide a systematic approach to problem-solving. The efficiency of an algorithm is often measured in terms of time and space complexity known as Big O. Different algorithms can be applied to solve the same problem, and their efficiency may vary.
Code challenges are problems or puzzles that require a solution through programming. They are often used as a means to practice and improve coding skills, problem-solving abilities, and algorithmic thinking.
Code challenges serve as a practical application of data structures and algorithms. They help programmers enhance their problem-solving skills, understand how to choose appropriate data structures, and optimize algorithms for efficient solutions.
Testing is an integral part of each solution in this repository. Tests reside within the same folder as the respective solution implementation.
GitHub Actions will be used to ensure the reliability and comprehensiveness of the code. This will automatically execute the tests, validating the accuracy and assessing the coverage of each solution.
The test suites are designed to encompass a spectrum of scenarios, including:
- Best Path (Happy Path): Valid inputs that adhere to the expected flow of the solution.
- Worst-Case Scenario: Inputs that lead to the most inefficient or extreme behavior of the solution.
- Empty Path: Testing the solution's behavior when provided with empty or null inputs.
- Duplicated Values: Handling scenarios involving duplicate values within the input.
- Edge Cases: Evaluating the solution's behavior in boundary or exceptional cases that might challenge its performance or correctness.
At this time, I'm not accepting contributions in the form of Pull Requests. However, your engagement is highly encouraged, and you're welcome to challenge or discuss any aspect of the repository by submitting a formal Issue.
If you'd like to challenge or discuss any code, concept, or solution present in this repository, I encourage you to open a formal Issue. When submitting an Issue, please ensure it contains:
- A detailed description of the challenge or discussion point.
- Relevant context or reasoning supporting your challenge or question.
- If applicable, provide the link or reference to the specific code or content in question.
- Provide any additional links that further support the Issue you are raising.
I value your input and thoughtful engagement with the repository's content. Your contributions through meaningful discussions and challenges are highly appreciated!
Copyright (c) 2023 Heather "Hexx" King
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- Social media favicons from Icons8
- Emoji Gif from Tenor
- Emojis from Emojipedia
- Big O CheatSheet, Whiteboard Guide and Github Merge Checklist