Summary

In this chapter, we've gone over the basics of using Jupyter Notebooks for data science. We started by exploring the platform and finding our way around the interface. Then, we discussed the most useful features, which include tab completion and magic functions. Finally, we introduced the Python libraries we'll be using in this book.

As we'll see in the coming chapters, these libraries offer high-level abstractions that allow data science to be highly accessible with Python. This includes methods for creating statistical visualizations, building data cleaning pipelines, and training models on millions of data points and beyond.

While this chapter focused on the basics of Jupyter platforms, the next chapter is where the real data science begins. The remainder of this book is very interactive, and in Chapter 3, Preparing Data for Predictive Modeling, we'll perform an analysis of housing data using Jupyter Notebook and the Seaborn plotting library.