Hands-On APIs for AI and Data Science

Chapter 8. What Data Scientists Should Know About APIs

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 8th chapter of the final book. The GitHub repo for this book is https://github.com/handsonapibook/apibook-part-one.

If you have comments about how we might improve the content and/or examples in this book, or if you notice missing material within this chapter, please reach out to the editor at [e-mail].

What the hand does, the mind remembers.

Maria Montessori

API expertise is critical to being an effective data scientist, but the field of APIs is nearly as wide as the field of data science. A data scientist can’t realistically become an expert in every API specialization, but you don’t need to. You can use the building block approach: master one or two API-related skills through a hands-on project and over time stack additional skills on top of those. In my experience, the best way to acquire the building blocks is through hands-on coding projects that you share with the world for fast feedback. (That’s probably no surprise, given the title of this book.)

The following are some of the topics that will be particularly useful for data scientists in their work.

Using a Variety of API Styles

Out in the wild, there are a few major API architectural styles ...