This section introduces us to the world of scientific (or data-oriented) Python, including its own IDE—Jupyter—and a suite of packages that assist in working with data. We will perform exploratory data analysis, draw some charts, and train a number of models. All of this will be implemented through our three projects, utilizing the data we collected in the first section.

This section comprises the following chapters:

• Chapter 10, Python for Data Applications
• Chapter 11, Data Cleaning and Manipulation
• Chapter 12, Data Exploration and Visualization
• Chapter 13, Training a Machine Learning Model
• Chapter 14, Improving Your Model – Pipelines and Experiments