Includes bibliographical references and index.

The book covers the implementation of graph theory in R and Python for the purpose of the analysis of people networks. It starts with an overview of the importance of networks and graphs in people analytics and a brief tutorial of the R programming language for those with no programming experience. The main content starts with the definition of graphs and their creation and visualization in data science languages, followed by a chapter on how to restructure regular everyday data in ways that it can be used in network analysis. It then proceeds to cover critical elements of network analysis including distance, centrality, community, assortativity. Extension chapters include an introduction to graph databases and advanced graph visualization options. Real data sets will be used for both illustration and for end of chapter practice exercises and a final chapter will include data sets and exercises ideal for larger personal or group projects of varying difficulty level.