In the wake of the digital revolution, masses of Digital Behavioral Data (DBD) are becoming available for social research. Typically, this data has a relational dimension. As a consequence, network analysis is becoming increasingly important as a social science method. The social networks constructed from DBD, however, are much larger than the small-scale structures classically studied in the past. They give rise to higher-order structures and functionalities (complexity) that spring from lower-order processes. Plus, DBD typically allows for the analysis of the dynamics of these so-called complex networks. In this course, we present a basic understanding of social relations, the structures and dynamics which they collectively self-organize into, and how network methods and programming in Python can be deployed in practical application scenarios. Following an introduction to data formats, network construction, and plotting, the first day covers node-level measures such as centrality. The second day deals with community detection and stochastic blockmodeling, the main classes of methods at the meso-scale of analysis. The third day focuses on macro-scale structures and dynamics. It covers the small-world and scale-free properties that make networks complex and why these are relevant for the social sciences. The fourth day is dedicated to simulations of social mechanisms (network generation) and dynamics on networks. The last day is reserved for group projects. The program plan alternates between demonstrations and exercises. The former presents the theoretical and technical background of network analysis, while the latter allows participants to apply acquired knowledge with code directly.