PRODUCTION READY DATA SCIENCE
From Prototyping to Production with Python
Ebook – $36.24
Paperback coming soon
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 examples come from actual data science workflows.
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.
Skills you WILL GAIN
Through practical examples and clear explanations, you’ll master techniques for:
- Manage dependencies and environments for reproducible code
- Write modular, reusable, and testable Python code
- Implement robust data validation and error handling
- Leverage version control for code and data integrity
- Automate repetitive tasks with build tools like Make
- Establish continuous integration pipelines for quality assurance
- And much more!

Book TestimonialS
Don’t just take our word for it. – See what actual readers say about the book
“Having followed Khuyen’s work for years, I was thrilled to see her distill industry best practices into one comprehensive resource. Too much data science lives and dies in demos, but her practical snippets on topics like configuration management, logging, and data validation fill in the missing pieces needed for real-world deployment.
This book will help you ship better code, collaborate more effectively, and drive meaningful results.”
Kevin Kho
AI Engineer at Drata & core maintainer of Fugue
“If your attempts at creating more efficient, robust Python projects and code often result in large collections of browser tabs and bookmarks, but not much progress, grab this book and get off the struggle bus.
Khuyen Tran has written a concise, approachable manual for going from good to great with Python.”
Glen Otero, Ph.D.
Founder of Linux Prophet and Director of Scientific Computing at CIQ
“Many people get into data science without any background or training in software engineering. Keen to improve their skills, they consult textbooks or courses, but the information presented can often be overwhelming and feel irrelevant to them.
Here, however, Khuyen presents key concepts in a clear and understandable way. She gives readers enough material such that they can upskill, and does so without overloading them with unneeded details.“
Marco Gorelli
Creator & lead developer of Narwhals
I’ve been a follower of Khuyen’s newsletter, CodeCut, for a while, and this book is a great extension of the practical advice she’s known for.
The book directly addresses the common challenge of moving from a simple notebook to a more structured, production-ready project and offers clear, actionable guidance on essential topics that are often overlooked, such as configuration management, logging, and data validation.
I would definitely recommend this book to both beginners and experts alike.
Siddhant Sadangi
Developer Experience Engineer of Neptune.ai
“Production Ready Data Science” is a must-read for anyone working with or interested in data. Khuyen offers valuable insights and practical examples to help readers elevate their data projects into robust, maintainable, real-world systems.
Whether you’re a seasoned professional or just starting to productionize data systems, this book delivers clear, actionable value. I highly recommend it to all data practitioners.
Michael French
Data Engineer