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# 使用指南
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本指南提供了使用《数据科学入门》课程的示例和常见工作流程。
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## 目录
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- [如何使用本课程](../..)
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- [学习课程内容](../..)
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- [使用 Jupyter Notebooks](../..)
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- [使用测验应用程序](../..)
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- [常见工作流程](../..)
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- [自学者提示](../..)
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- [教师提示](../..)
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## 如何使用本课程
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本课程设计灵活，可通过多种方式使用：
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- **自学**：根据自己的节奏独立完成课程
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- **课堂教学**：作为结构化课程进行指导教学
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- **学习小组**：与同伴协作学习
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- **工作坊形式**：短期强化学习
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## 学习课程内容
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每节课遵循一致的结构，以最大化学习效果：
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### 课程结构
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1. **课前测验**：测试现有知识
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2. **手绘笔记**（可选）：关键概念的视觉总结
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3. **视频**（可选）：补充视频内容
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4. **书面课程**：核心概念和解释
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5. **Jupyter Notebook**：动手编码练习
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6. **作业**：练习所学内容
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7. **课后测验**：巩固理解
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### 课程学习示例流程
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```bash
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# 1. Navigate to the lesson directory
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cd 1-Introduction/01-defining-data-science
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# 2. Read the README.md
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# Open README.md in your browser or editor
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# 3. Take the pre-lesson quiz
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# Click the quiz link in the README
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# 4. Open the Jupyter notebook (if available)
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jupyter notebook
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# 5. Complete the exercises in the notebook
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# 6. Work on the assignment
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# 7. Take the post-lesson quiz
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```
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## 使用 Jupyter Notebooks
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### 启动 Jupyter
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```bash
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# Activate your virtual environment
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source venv/bin/activate # On macOS/Linux
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# OR
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venv\Scripts\activate # On Windows
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# Start Jupyter from the repository root
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jupyter notebook
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```
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### 运行 Notebook 单元格
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1. **执行单元格**：按 `Shift + Enter` 或点击“运行”按钮
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2. **运行所有单元格**：从菜单中选择“Cell” → “Run All”
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3. **重启内核**：如果遇到问题，选择“Kernel” → “Restart”
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### 示例：在 Notebook 中处理数据
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```python
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# Import required libraries
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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# Load a dataset
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df = pd.read_csv('data/sample.csv')
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# Explore the data
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df.head()
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df.info()
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df.describe()
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# Create a visualization
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plt.figure(figsize=(10, 6))
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plt.plot(df['column_name'])
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plt.title('Sample Visualization')
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plt.xlabel('X-axis Label')
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plt.ylabel('Y-axis Label')
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plt.show()
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```
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### 保存您的工作
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- Jupyter 会定期自动保存
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- 手动保存：按 `Ctrl + S`（macOS 上为 `Cmd + S`）
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- 您的进度保存在 `.ipynb` 文件中
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## 使用测验应用程序
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### 本地运行测验应用程序
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```bash
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# Navigate to quiz app directory
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cd quiz-app
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# Start the development server
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npm run serve
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# Access at http://localhost:8080
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```
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### 参加测验
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1. 课前测验链接位于每节课顶部
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2. 课后测验链接位于每节课底部
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3. 每个测验包含 3 个问题
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4. 测验旨在巩固学习，而非全面测试
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### 测验编号
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- 测验编号为 0-39（共 40 个测验）
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- 每节课通常有课前和课后测验
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- 测验 URL 包含测验编号：`https://ff-quizzes.netlify.app/en/ds/quiz/0`
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## 常见工作流程
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### 工作流程 1：完全初学者路径
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```bash
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# 1. Set up your environment (see INSTALLATION.md)
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# 2. Start with Lesson 1
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cd 1-Introduction/01-defining-data-science
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# 3. For each lesson:
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# - Take pre-lesson quiz
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# - Read the lesson content
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# - Work through the notebook
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# - Complete the assignment
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# - Take post-lesson quiz
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# 4. Progress through all 20 lessons sequentially
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```
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### 工作流程 2：特定主题学习
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如果您对某个特定主题感兴趣：
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```bash
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# Example: Focus on Data Visualization
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cd 3-Data-Visualization
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# Explore lessons 9-13:
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# - Lesson 9: Visualizing Quantities
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# - Lesson 10: Visualizing Distributions
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# - Lesson 11: Visualizing Proportions
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# - Lesson 12: Visualizing Relationships
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# - Lesson 13: Meaningful Visualizations
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```
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### 工作流程 3：基于项目的学习
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```bash
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# 1. Review the Data Science Lifecycle lessons (14-16)
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cd 4-Data-Science-Lifecycle
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# 2. Work through a real-world example (Lesson 20)
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cd ../6-Data-Science-In-Wild/20-Real-World-Examples
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# 3. Apply concepts to your own project
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```
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### 工作流程 4：基于云的数据科学
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```bash
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# Learn about cloud data science (Lessons 17-19)
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cd 5-Data-Science-In-Cloud
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# 17: Introduction to Cloud Data Science
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# 18: Low-Code ML Tools
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# 19: Azure Machine Learning Studio
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```
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## 自学者提示
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### 保持条理
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```bash
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# Create a learning journal
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mkdir my-learning-journal
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# For each lesson, create notes
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echo "# Lesson 1 Notes" > my-learning-journal/lesson-01-notes.md
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```
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### 定期练习
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- 每天或每周安排固定时间学习
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- 每周至少完成一节课
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- 定期复习之前的课程
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### 参与社区
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- 加入 [Discord 社区](https://aka.ms/ds4beginners/discord)
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- 参与 Discord 中的 #Data-Science-for-Beginners 频道 [Discord Discussions](https://aka.ms/ds4beginners/discord)
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- 分享您的学习进度并提出问题
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### 创建自己的项目
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完成课程后，将概念应用到个人项目中：
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```python
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# Example: Analyze your own dataset
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import pandas as pd
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# Load your own data
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my_data = pd.read_csv('my-project/data.csv')
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# Apply techniques learned
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# - Data cleaning (Lesson 8)
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# - Exploratory data analysis (Lesson 7)
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# - Visualization (Lessons 9-13)
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# - Analysis (Lesson 15)
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```
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## 教师提示
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### 课堂设置
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1. 查看 [for-teachers.md](for-teachers.md) 获取详细指导
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2. 设置共享环境（GitHub Classroom 或 Codespaces）
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3. 建立沟通渠道（Discord、Slack 或 Teams）
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### 课程计划
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**建议的 10 周课程安排：**
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- **第 1-2 周**：介绍（第 1-4 课）
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- **第 3-4 周**：数据处理（第 5-8 课）
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- **第 5-6 周**：数据可视化（第 9-13 课）
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- **第 7-8 周**：数据科学生命周期（第 14-16 课）
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- **第 9 周**：云数据科学（第 17-19 课）
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- **第 10 周**：实际应用与最终项目（第 20 课）
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### 运行 Docsify 以离线访问
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```bash
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# Serve documentation locally for classroom use
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docsify serve
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# Students can access at localhost:3000
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# No internet required after initial setup
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```
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### 作业评分
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- 检查学生的 Notebook 是否完成练习
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- 通过测验分数检查理解情况
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- 使用数据科学生命周期原则评估最终项目
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### 创建作业
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```python
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# Example custom assignment template
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"""
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Assignment: [Topic]
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Objective: [Learning goal]
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Dataset: [Provide or have students find one]
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Tasks:
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1. Load and explore the dataset
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2. Clean and prepare the data
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3. Create at least 3 visualizations
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4. Perform analysis
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5. Communicate findings
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Deliverables:
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- Jupyter notebook with code and explanations
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- Written summary of findings
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"""
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```
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## 离线使用
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### 下载资源
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```bash
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# Clone the entire repository
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git clone https://github.com/microsoft/Data-Science-For-Beginners.git
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# Download datasets in advance
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# Most datasets are included in the repository
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```
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### 本地运行文档
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```bash
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# Serve with Docsify
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docsify serve
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# Access at localhost:3000
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```
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### 本地运行测验应用程序
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```bash
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cd quiz-app
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npm run serve
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```
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## 访问翻译内容
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翻译版本支持 40 多种语言：
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```bash
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# Access translated lessons
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cd translations/fr # French
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cd translations/es # Spanish
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cd translations/de # German
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# ... and many more
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```
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每种翻译版本的结构与英文版保持一致。
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## 其他资源
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### 继续学习
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- [Microsoft Learn](https://docs.microsoft.com/learn/) - 更多学习路径
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- [Student Hub](https://docs.microsoft.com/learn/student-hub) - 学生资源
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- [Azure AI Foundry](https://aka.ms/foundry/forum) - 社区论坛
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### 相关课程
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- [AI 入门](https://aka.ms/ai-beginners)
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- [ML 入门](https://aka.ms/ml-beginners)
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- [Web 开发入门](https://aka.ms/webdev-beginners)
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- [生成式 AI 入门](https://aka.ms/genai-beginners)
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## 获取帮助
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- 查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 了解常见问题
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- 搜索 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
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- 加入我们的 [Discord](https://aka.ms/ds4beginners/discord)
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- 查看 [CONTRIBUTING.md](CONTRIBUTING.md) 以报告问题或贡献内容
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---
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**免责声明**：
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本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。 |
