Software Engineering for Data Scientists

Preface

Data science happens in code. Whether you’re building a machine learning system, exploring your data for the first time, visualizing the distribution of your data, or running a statistical analysis, your coding and computation skills are what make it happen. If you are working on production code, these skills are essential for writing successful, maintainable code. Even if you aren’t working in a production software team, you’ll find it beneficial to write more robust, reproducible code that other data scientists can use easily. And if you’re working alone, good practices will accelerate your coding and help you pick up your code after a break.

I didn’t always see the value of good engineering. Earlier in my data science career, I joined a team where I was the only data scientist. My teammates were software engineers and designers, and I was concerned that it would be hard to increase my skills with no other data scientists to learn from. I expressed my concern to my coworker, a developer. He said, “But learning to write better code will let you do more data science.” This comment stuck with me, and I’ve found since then that improving my software engineering skills has been incredibly beneficial in doing data science. It’s helped me write code that is easier for my coworkers to use and that is still easy to change when I go back to it many months later.

My aim with this book is to guide you on your journey to writing better data science code. I’ll describe best practices ...