In this chapter, we glanced at the usefulness of linear models under the data science perspective and we introduced some basic concepts of the data science approach that will be explained in more detail later and will be applied to linear models. We have also provided detailed instructions on how to set up the Python environment; these will be used throughout the book to present examples and provide useful code snippets for the fast development of machine learning hypotheses.
In the next chapter, we will begin presenting linear regression from its statistical foundations. Starting from the idea of correlation, we will build up the simple linear regression (using just one predictor) and provide the algorithm's formulations.
