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.
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
Chapter 1-3: Foundation
- Version Control - Git workflows
- Dependency Management - Environment setup
- Modules & Packages - Project organization
Chapter 4-6: Code Quality
Chapter 7-9: Testing & Operations
- Unit Testing - Automated testing
- Configuration Management - Settings management
- Logging - Monitoring and debugging
Chapter 10-11: Data
- Data Validation - Input validation
- Data Version Control - Dataset tracking
Chapter 12-14: Production
- Continuous Integration - Automated deployment
- Package Your Project - Package distribution
- Notebooks in Production - Production notebooks
- Click the "Fork" button at the top of this page
- This creates your own copy at:
github.com/YOUR_USERNAME/production-ready-data-science-code
- Clone your fork:
git clone https://github.com/YOUR_USERNAME/production-ready-data-science-code.git
cd production-ready-data-science-code
- Python 3.10.11 or higher
- uv - Fast Python package manager
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
Ready to get started? Browse examples above or get the book
Author: Khuyen Tran | Website: https://codecut.ai/