Summary

In this chapter, you have seen the fundamentals of data analysis in Jupyter. We began with usage instructions and features of Jupyter such as magic functions and tab completion. Then, transitioning to data-science-specific material, we introduced the most important libraries for data science with Python.

In the latter half of the chapter, we ran an exploratory analysis in a live Jupyter Notebook. Here, we used visual assists such as scatter plots, histograms, and violin plots to deepen our understanding of the data. We also performed simple predictive modeling, a topic which will be the focus of the following chapter in this book.

In the next chapter, we will discuss how to approach predictive analytics, what things to consider when preparing the data for modeling, and how to implement and compare a variety of models using Jupyter Notebooks.