Summary

This chapter showed how to perform a qualitative analysis that is useful as a first step when doing data analysis. We showed some descriptive statistics techniques and how to implement them programmatically. With these skills, we are able to perform simple yet powerful analyses and save the results for later use. Specifically, we showed how to do basic data cleaning, how to create graphs programmatically, how to create matrix scatter plots and matrix correlations, how to perform Principal Component Analysis, and how to combine these tools to understand the data at hand. Finally, we touched on the basics of high-quality code and showed how to transform your initial data analysis code into programs that are modular, flexible, and easy to work with.

In Chapter 3, Predicting Votes with Linear Models, we'll show how to extend the current analysis with qualitative tools. Specifically, we'll show how to use linear models to understand the quantitative effects of variables on the proportion of votes in favor of the UK leaving and remaining in the EU, how to make predictions for wards whose vote data we don't have, and how to measure the accuracy of those predictions with the data we do have. These are essential skills for any data analyst and, just as we did in this chapter, we'll see how to implement them programmatically.