In this chapter, we've discussed graphs and its associated graph theory, exploring its data structure and algorithms. We've also briefly introduced the networkx Python library that provides a rich set of APIs for manipulating and visualizing graphs. We then applied these techniques toward building a sample application that analyzes flight data by treating it as a graph problem with airports being the vertices and flights the edges. As always, we've also shown how to operationalize these analytics into a simple yet powerful dashboard that can run directly in the Jupyter Notebook and then optionally be deployed as a web analytics application with the PixieGateway microservice.
This chapter completes the series of sample applications that cover many important industry use cases. In the next chapter, I offer some final thoughts about the theme of this book which is to bridge the gap between data science and engineering by making working with data simple and accessible to all.