This chapter provided an overview of essential data science by providing examples of both basic and advanced graphical representations of data, machine learning processes, and results. We explored the pylab module from matplotlib, which gives the easiest and fastest access to the graphical capabilities of the package. We used pandas for EDA, and tested the graphical utilities provided by scikit-learn. All examples were like building blocks, and they are all easily customizable in order to provide you with a fast template for visualization.
In the next chapter, you'll be introduced to graphs, which are an interesting deviation from the predictors/target flat matrices. They are quite a hot topic in data science now. Expect to delve into very complex and intricate networks.
