PRODUCTION READY DATA SCIENCE
FROM PROTOTYPING TO PRODUCTION WITH PYTHON
Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity?
About the book
Maintainability and scalability challenges stem from the gap between exploratory data analysis and production-grade software engineering practices. This book aims to bridge this gap.
The book covers a wide range of essential topics such as version control, dependency management, unit testing, configuration, logging, and many more! Get your copy and start building data workflows your team will trust.
About the author
Khuyen Tran has built her career solving the problem that haunts most data science teams: data science projects that never make it to production. As a data scientist and developer advocate working across startups and enterprise environments, she’s seen talented professionals hit career walls not because they lack technical skills, but because their code can’t scale beyond their laptops. This insight led her to create CodeCut.ai, where thousands of data professionals have learned to transform promising prototypes into production-ready systems that businesses actually depend on.
Khuyen’s teaching philosophy is built on a simple truth: the data scientists who advance fastest aren’t necessarily the most brilliant—they’re the ones who can build systems others trust to handle real-world pressure. Her practical, example-driven approach cuts through academic theory to focus on the engineering skills that separate career-stagnant prototype-builders from the data scientists who ship critical systems and lead high-impact projects. In Production-Ready Data Science, she distills years of experience into a clear roadmap for transforming your messy scripts into scalable, maintainable code that will accelerate your career and increase your impact. (edited)
About the book
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.
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!
Whether you’re a data scientist seeking to elevate your projects, a machine learning engineer building production-grade models, or a developer venturing into data-driven applications, this book is your comprehensive guide to engineering high-quality, reliable data science solutions.
Book TestimonialS
Don’t just take our word for it. – See what actual readers say about the book