Network analysis provides a perspective on how to find and quantify significant structures in the interaction patterns between different types of actors and on how to relate these structures to properties of the actors. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different types of random graph models, centrality indices, clustering algorithms, global network measures, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing an extensive glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Additionally, it provides and explains in detail R code for all analyses and diagrams shown in the book. Different aspects of network analysis literacy understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation are deepened in various exercises with provided solutions. This text is the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.