Summary

This chapter provides 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 is the easiest and fastest access to the graphical capabilities of the package, 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 conclusion, this book covered all the key points of a data science project, presenting you with all the essential tools to operate your own projects using Python. As a learning tool, the book accompanied you through all the phases of data science, from data loading to machine learning and visualization, illustrating best practices and ways to avoid common pitfalls. As a reference, the book touched upon a variety of commands and packages, providing...