Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming. Rob Pike
This repository is a comprehensive collection of data structures and algorithms implemented in various programming languages, including TypeScript, Go and Rust. The purpose of this repository is to provide a comprehensive learning resource for anyone interested in mastering algorithms and data structures, with a focus on ensuring that you never forget what you learn.
The code in this repository is designed to help you learn algorithms in a way that sticks. Each algorithm is implemented in a clear and concise manner, with detailed explanations and comments provided throughout the code. Additionally, each problem includes a set of test cases to help you verify that your solution is correct and to reinforce your understanding of the underlying algorithm.
Whether you're a beginner or an experienced programmer, this repository is a great resource for learning and improving your skills in data structures and algorithms. By studying and practicing the code in this repository, you'll gain a deeper understanding of how algorithms work, and be able to apply them to a wide range of programming problems.
So, if you're looking to improve your understanding of data structures and algorithms in different programming languages and want to ensure that you truly understand what you learn, this repository is the perfect place to start.
Here are some tips to help you develop the mindset and confidence you need to solve algorithm problems:
The more you practice solving algorithm problems, the more comfortable and confident you will become. Set aside time each day or each week to work on algorithm problems, and try to challenge yourself with increasingly difficult problems over time.
When faced with a complex algorithm problem, it can be helpful to break it down into smaller, more manageable sub-problems. This can make the problem easier to understand and solve, and can also help you identify and address any issues more quickly.
Before diving into a solution, it can be helpful to work through some examples and test cases to ensure you fully understand the problem and what is being asked. This can also help you identify any edge cases or special situations that you may need to handle in your solution.
If you're struggling with a particular problem or concept, don't be afraid to ask for help. Reach out to peers, mentors, or online communities for guidance and support, and use resources like textbooks, online tutorials, and coding forums to supplement your learning.
Algorithm problems can be challenging and require a lot of persistence and perseverance to solve. Don't give up if you encounter setbacks or obstacles, and keep working through the problem until you find a solution. Remember that every problem you solve is an opportunity to learn and grow as a programmer.
By following these tips and approaches, you can develop the mindset and confidence you need to tackle algorithm problems with ease and success.
learn more about problem-solving strategies
finally, take is easy and have fun with solving problems
| DS & Algos | Solutions | References | |
|---|---|---|---|
| 1 | Arrays | TS, Go | Wikipedia, What is An Array? - Great Learning |
| 2 | String | TS, Go | String Algorithms - The University of Western Australia, String Algorithms, Jaehyun Park - CS 97SI - Stanford University |
| 3 | Binary Search | TS Go | |
| 4 | Linked List | TS, Go | |
| 5 | Doubly Linked List | TS, Go | |
| 6 | Stack, Queue | TS, Go | |
| 7 | Sort | TS, Go | |
| 8 | Binary Tree | TS, Go | CLRS |
| 9 | Heaps | TS, Go | |
| 10 | Priority Queues | TS, Go | |
| 11 | Matrix | TS, Go | |
| 12 | Graphs | TS, Go | |
| 13 | N-ary Tree | TS, Go |