Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success-and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.
Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to understand data structures and object-oriented programming; clearly and skillfully document your code; package and share your code; integrate data science code with a larger code base; learn how to write APIs; create secure code; apply best practices to common tasks such as testing, error handling, and logging; work more effectively with software engineers; write more efficient, maintainable, and robust code in Python; put your data science projects into production; and more.
