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en/docs/chapter_preface/about_the_book.md
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# About This Book
# About this book
This open-source project aims to create a free, and beginner-friendly crash course on data structures and algorithms.
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- Run code with just one click, supporting Java, C++, Python, Go, JS, TS, C#, Swift, Rust, Dart, Zig and other languages.
- Readers are encouraged to engage with each other in the discussion area for each section, questions and comments are usually answered within two days.
## Target Audience
## Target audience
If you are new to algorithms with limited exposure, or you have accumulated some experience in algorithms, but you only have a vague understanding of data structures and algorithms, and you are constantly jumping between "yep" and "hmm", then this book is for you!
@@ -18,15 +18,15 @@ If you are an algorithm expert, we look forward to receiving your valuable sugge
You should know how to write and read simple code in at least one programming language.
## Content Structure
## Content structure
The main content of the book is shown in the following figure.
- **Complexity Analysis**: explores aspects and methods for evaluating data structures and algorithms. Covers methods of deriving time complexity and space complexity, along with common types and examples.
- **Data Structures**: focuses on fundamental data types, classification methods, definitions, pros and cons, common operations, types, applications, and implementation methods of data structures such as array, linked list, stack, queue, hash table, tree, heap, graph, etc.
- **Complexity analysis**: explores aspects and methods for evaluating data structures and algorithms. Covers methods of deriving time complexity and space complexity, along with common types and examples.
- **Data structures**: focuses on fundamental data types, classification methods, definitions, pros and cons, common operations, types, applications, and implementation methods of data structures such as array, linked list, stack, queue, hash table, tree, heap, graph, etc.
- **Algorithms**: defines algorithms, discusses their pros and cons, efficiency, application scenarios, problem-solving steps, and includes sample questions for various algorithms such as search, sorting, divide and conquer, backtracking, dynamic programming, greedy algorithms, and more.
![Main Content of the Book](about_the_book.assets/hello_algo_mindmap.png)
![Main content of the book](about_the_book.assets/hello_algo_mindmap.png)
## Acknowledgements

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en/docs/chapter_preface/suggestions.md
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# How to Read
# How to read
!!! tip
For the best reading experience, it is recommended that you read through this section.
## Writing Conventions
## Writing conventions
- Chapters marked with '*' after the title are optional and contain relatively challenging content. If you are short on time, it is advisable to skip them.
- Technical terms will be in boldface (in the print and PDF versions) or underlined (in the web version), for instance, <u>array</u>. It's advisable to familiarize yourself with these for better comprehension of technical texts.
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=== "Python"
```python title=""
"""Header comments for labeling functions, classes, test samples, etc""""
"""Header comments for labeling functions, classes, test samples, etc"""
# Comments for explaining details
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// comments
```
## Efficient Learning via Animated Illustrations
## Efficient learning via animated illustrations
Compared with text, videos and pictures have a higher density of information and are more structured, making them easier to understand. In this book, **key and difficult concepts are mainly presented through animations and illustrations**, with text serving as explanations and supplements.
When encountering content with animations or illustrations as shown in the figure below, **prioritize understanding the figure, with text as supplementary**, integrating both for a comprehensive understanding.
![Animated Illustration Example](../index.assets/animation.gif)
![Animated illustration example](../index.assets/animation.gif)
## Deepen Understanding through Coding Practice
## Deepen understanding through coding practice
The source code of this book is hosted on the [GitHub Repository](https://github.com/krahets/hello-algo). As shown in the figure below, **the source code comes with test examples and can be executed with just a single click**.
@@ -196,7 +196,7 @@ If time permits, **it's recommended to type out the code yourself**. If pressed
Compared to just reading code, writing code often yields more learning. **Learning by doing is the real way to learn.**
![Running Code Example](../index.assets/running_code.gif)
![Running code example](../index.assets/running_code.gif)
Setting up to run the code involves three main steps.
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Alternatively, you can also click the "Download ZIP" button at the location shown in the figure below to directly download the code as a compressed ZIP file. Then, you can simply extract it locally.
![Cloning Repository and Downloading Code](suggestions.assets/download_code.png)
![Cloning repository and downloading code](suggestions.assets/download_code.png)
**Step 3: Run the source code**. As shown in the figure below, for the code block labeled with the file name at the top, we can find the corresponding source code file in the `codes` folder of the repository. These files can be executed with a single click, which will help you save unnecessary debugging time and allow you to focus on learning.
![Code Block and Corresponding Source Code File](suggestions.assets/code_md_to_repo.png)
![Code block and corresponding source code file](suggestions.assets/code_md_to_repo.png)
## Learning Together in Discussion
## Learning together in discussion
While reading this book, please don't skip over the points that you didn't learn. **Feel free to post your questions in the comment section**. We will be happy to answer them and can usually respond within two days.
As illustrated in the figure below, each chapter features a comment section at the bottom. I encourage you to pay attention to these comments. They not only expose you to others' encountered problems, aiding in identifying knowledge gaps and sparking deeper contemplation, but also invite you to generously contribute by answering fellow readers' inquiries, sharing insights, and fostering mutual improvement.
![Comment Section Example](../index.assets/comment.gif)
![Comment section example](../index.assets/comment.gif)
## Algorithm Learning Path
## Algorithm learning path
Overall, the journey of mastering data structures and algorithms can be divided into three stages:
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As shown in the figure below, this book mainly covers “Stage 1,” aiming to help you more efficiently embark on Stages 2 and 3.
![Algorithm Learning Path](suggestions.assets/learning_route.png)
![Algorithm learning path](suggestions.assets/learning_route.png)