Data Science & Machine Learning Extension Pack

A comprehensive collection of essential VS Code extensions for Data Science and Machine Learning development.

🚀 What's included in this pack?

This extension pack automatically installs all the necessary tools for a complete Data Science and Machine Learning development environment:

🐍 Python & Development

• Python - Complete Python support
• Pylance - Advanced Python language server
• Python Debugger (debugpy) - Advanced debugging capabilities
• Python Environments - Virtual environment management
• isort - Automatic import sorting
• Black Formatter - Automatic Python code formatting

📊 Jupyter & Data Analysis

• Jupyter - Complete notebook support
• Jupyter Keymap - Familiar keyboard shortcuts
• Jupyter PowerToys - Additional notebook tools
• Jupyter Renderers - Enhanced output visualization
• Jupyter Cell Tags - Cell organization
• Jupyter Slideshow - Presentations from notebooks
• Data Wrangler - Visual data manipulation

🧪 Testing & Quality

• Test Explorer - Unified testing interface
• Python Test Adapter - Python test integration
• Test Adapter Converter - Multi-framework compatibility

📈 Data Visualization & CSV

• Rainbow CSV - Enhanced CSV file visualization
• Better Comments - More expressive comments

🤖 AI & Assistance

• Claude Code - Anthropic's AI assistant

📝 Documentation & Markdown

• Markdown Preview Enhanced - Advanced Markdown preview
• Markdown Lint - Markdown file linting

🔧 Development Tools

• Git Graph - Git history visualization
• Even Better TOML - TOML file support
• Remote Containers - Container development
• Docker - Docker integration

🎨 Themes & Icons

• Bearded Theme - Modern visual theme
• Material Icon Theme - Professional file icons

🛠️ Installation

1. Open VS Code
2. Go to Extensions tab (Ctrl+Shift+X)
3. Search for "Data Science & Machine Learning Pack"
4. Click "Install"
5. All extensions will be installed automatically!

💡 Recommended Usage

This pack is optimized for:

• Data analysis with pandas, numpy, matplotlib
• Machine Learning with scikit-learn, tensorflow, pytorch
• Jupyter notebook development
• Data visualization
• AI experimentation
• Research projects

🔄 Typical Workflow

1. Setup: Python extensions will automatically detect your virtual environments
2. Development: Use Jupyter notebooks for rapid experimentation
3. Coding: Develop Python scripts with intelligent autocompletion
4. Testing: Run tests directly from Test Explorer
5. Visualization: Use Rainbow CSV to inspect datasets
6. AI Assistance: Utilize Copilot to accelerate development

📋 Requirements

• VS Code 1.105.0 or higher
• Python 3.7+ installed on your system
• Git (recommended)

🤝 Contributing

Missing an important extension? Open an issue in the repository!

📄 License

MIT License