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136 lines
4.4 KiB
136 lines
4.4 KiB
# Beginner-Friendly Data Science Examples
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Welcome to the examples directory! This collection of simple, well-commented examples is designed to help you get started with data science, even if you're a complete beginner.
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## 📚 What You'll Find Here
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Each example is self-contained and includes:
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- **Clear comments** explaining every step
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- **Simple, readable code** that demonstrates one concept at a time
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- **Real-world context** to help you understand when and why to use these techniques
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- **Expected output** so you know what to look for
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## 🚀 Getting Started
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### Prerequisites
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Before running these examples, make sure you have:
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- Python 3.7 or higher installed
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- Basic understanding of how to run Python scripts
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### Installing Required Libraries
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```bash
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pip install pandas numpy matplotlib
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```
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## 📖 Examples Overview
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### 1. Hello World - Data Science Style
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**File:** `01_hello_world_data_science.py`
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Your first data science program! Learn how to:
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- Load a simple dataset
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- Display basic information about your data
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- Print your first data science output
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Perfect for absolute beginners who want to see their first data science program in action.
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---
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### 2. Loading and Exploring Data
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**File:** `02_loading_data.py`
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Learn the fundamentals of working with data:
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- Read data from CSV files
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- View the first few rows of your dataset
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- Get basic statistics about your data
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- Understand data types
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This is often the first step in any data science project!
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---
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### 3. Simple Data Analysis
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**File:** `03_simple_analysis.py`
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Perform your first data analysis:
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- Calculate basic statistics (mean, median, mode)
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- Find maximum and minimum values
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- Count occurrences of values
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- Filter data based on conditions
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See how to answer simple questions about your data.
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---
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### 4. Data Visualization Basics
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**File:** `04_basic_visualization.py`
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Create your first visualizations:
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- Make a simple bar chart
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- Create a line plot
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- Generate a pie chart
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- Save your visualizations as images
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Learn to communicate your findings visually!
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---
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### 5. Working with Real Data
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**File:** `05_real_world_example.py`
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Put it all together with a complete example:
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- Load real data from the repository
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- Clean and prepare the data
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- Perform analysis
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- Create meaningful visualizations
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- Draw conclusions
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This example shows you a complete workflow from start to finish.
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---
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## 🎯 How to Use These Examples
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1. **Start from the beginning**: The examples are numbered in order of difficulty. Begin with `01_hello_world_data_science.py` and work your way through.
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2. **Read the comments**: Each file has detailed comments explaining what the code does and why. Read them carefully!
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3. **Experiment**: Try modifying the code. What happens if you change a value? Break things and fix them - that's how you learn!
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4. **Run the code**: Execute each example and observe the output. Compare it with what you expected.
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5. **Build on it**: Once you understand an example, try extending it with your own ideas.
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## 💡 Tips for Beginners
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- **Don't rush**: Take time to understand each example before moving to the next one
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- **Type the code yourself**: Don't just copy-paste. Typing helps you learn and remember
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- **Look up unfamiliar concepts**: If you see something you don't understand, search for it online or in the main lessons
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- **Ask questions**: Join the [discussion forum](https://github.com/microsoft/Data-Science-For-Beginners/discussions) if you need help
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- **Practice regularly**: Try to code a little bit every day rather than long sessions once a week
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## 🔗 Next Steps
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After completing these examples, you're ready to:
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- Work through the main curriculum lessons
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- Try the assignments in each lesson folder
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- Explore the Jupyter notebooks for more in-depth learning
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- Create your own data science projects
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## 📚 Additional Resources
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- [Main Curriculum](../README.md) - The complete 20-lesson course
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- [For Teachers](../for-teachers.md) - Using this curriculum in your classroom
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- [Microsoft Learn](https://docs.microsoft.com/learn/) - Free online learning resources
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- [Python Documentation](https://docs.python.org/3/) - Official Python reference
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## 🤝 Contributing
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Found a bug or have an idea for a new example? We welcome contributions! Please see our [Contributing Guide](../CONTRIBUTING.md).
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---
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**Happy Learning! 🎉**
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Remember: Every expert was once a beginner. Take it one step at a time, and don't be afraid to make mistakes - they're part of the learning process!
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