Determining how to structure, manage, and maintain your data science project are a series of nontrivial tasks which, if performed properly, can go a long way towards making your life easier as your project unfolds and matures. Aiming to ensure reproducibility adds an additional layer of complexity and difficulty, but can help contribute to a project's longevity and credibility. Where does one find comprehensive resources on practically ensuring the reproducibility and maintainability of data science projects? Khuyen Tran has put together a fantastic resource in the free online book titled, rather appropriately, Build a Reproducible and Maintainable Data Science Project. This book introduces Python tools for developing efficient workflows for reproducible and maintainable data science projects.