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.

This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one. This book will be most helpful for those with practice in basic modeling.

The world of data and analytics has been transformed over the last few years, providing a broad range of technologies to hold and analyse data. It has been a challenge to keep up-to-date with new concepts from NoSQL, to machine learning, to Internet of things and blockchain. The Little Bee series of books were created by IBM to provide an easy introduction to these concepts and many more in any easy and fun format. They will not make you into an expert, but they will highlight the key concepts, advantages and application areas. The books are available online and, if you are based in the UK, you can order physical copies to enjoy.