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About the Book
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:
- 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!
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.
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.