Production‑Ready Data Science

Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you’ve been seeking.

This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions.

Why This Book?

Complex concepts explained in simple, accessible terms. Each concept includes clear diagrams and code examples, all coming from actual data science workflows.

Skills You Will Gain

Through practical examples and clear explanations, you’ll master techniques for:

• Transform messy notebooks into organized, maintainable code
• Create reproducible environments across teams and deployments
• Write modular, reusable, and testable Python code
• Implement robust data validation and error handling
• Leverage version control for code and data integrity
• Implement automated testing to catch bugs early
• And much more!

About the Author

Khuyen Tran transforms how data scientists learn and work. She has written over 180 articles as a top writer on Towards Data Science, helping data professionals bridge the gap between prototyping and production. As founder of CodeCut, she publishes daily Python tips in her newsletter that reach over 10,000 views per month and has built a community of 110,000 LinkedIn followers. Previously an MLOps Engineer and Senior Data Engineer at Accenture, she built enterprise data solutions for clients worldwide.

Where to buy this book:

• Book's website
• Book's page