Introduction
Our approach to learning in this book is highly applied since hands-on learning is the quickest way to understand abstract concepts. With this in mind, the focus of this chapter is to introduce Jupyter Notebooks—the data science tool that we will be using throughout this book.
Since Jupyter Notebooks have gained mainstream popularity, they have been one of the most important tools for data scientists who use Python. This is because they offer a great environment for a variety of tasks, such as performing quick and dirty analysis, researching model selection, and creating reproducible pipelines. They allow for data to be loaded, transformed, and modeled inside a single file, where it's quick and easy to test out code and explore ideas along the way. Furthermore, all of this can be documented inline using formatted text, which means you can make notes or even produce a structured report.
Other comparable platforms—for example, RStudio or Spyder&...