khuyentran1401 / production-ready-data-science-code

Transform messy data science notebooks into production-ready code. Examples covering testing, CI/CD, MLOps, and scalable deployment practices.

Production-Ready Data Science Code Examples

Code examples from the Production-Ready Data Science book by Khuyen Tran.

Enhance your data science workflow with scalable, production-ready practices through hands-on examples.

🔗 Get the Book

What You'll Gain

Transform your data science workflow with these production-ready skills:

• 📁 Organization: Transform messy notebooks into organized, maintainable code
• 🔄 Reproducibility: Create reproducible environments across teams and deployments
• 🧪 Quality: Write modular, reusable, and testable Python code
• 🔍 Testing: Implement automated testing to catch bugs early
• 📊 Version Control: Leverage version control for code and data integrity
• 🚀 Production: Deploy bulletproof systems that scale

Examples by Chapter

Chapter 1-3: Foundation

1. Version Control - Git workflows
2. Dependency Management - Environment setup
3. Modules & Packages - Project organization

Chapter 4-6: Code Quality

4. Variables - Clean code practices
5. Functions - Function design
6. Classes - Object-oriented programming

Chapter 7-9: Testing & Operations

7. Unit Testing - Automated testing
8. Configuration Management - Settings management
9. Logging - Monitoring and debugging

Chapter 10-11: Data

10. Data Validation - Input validation
11. Data Version Control - Dataset tracking

Chapter 12-14: Production

12. Continuous Integration - Automated deployment
13. Package Your Project - Package distribution
14. Notebooks in Production - Production notebooks

Getting Started

Fork and Clone

1. Click the "Fork" button at the top of this page
2. This creates your own copy at: github.com/YOUR_USERNAME/production-ready-data-science-code
3. Clone your fork:

git clone https://github.com/YOUR_USERNAME/production-ready-data-science-code.git
cd production-ready-data-science-code

Prerequisites

• Python 3.10.11 or higher
• uv - Fast Python package manager

Install Dependencies

Option A: Install Everything (Recommended)

uv sync --all-groups

Option B: Install Specific Chapters Only

uv sync --group chapter7   # Testing examples
uv sync --group chapter9   # Logging examples  
uv sync --group chapter10  # Data validation

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Ready to get started? Browse examples above or get the book

Author: Khuyen Tran | Website: https://codecut.ai/