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.