Many natural and social systems consist of a large number of interacting components, and the structure of these systems can be captured as graphs or networks. Network analysis can be used to study how pathogens, behaviors and information spread in social and contact networks, having important implications for understanding epidemics and planning effective interventions. In a biological context, at a molecular level, network analysis can be applied to study gene regulation networks, signal transduction networks, protein interaction networks, and metabolic networks. This introductory course covers network measures (e.g., clustering), properties (e.g., community structure), network types (e.g., bipartite), network models (e.g., Erdos-Renyi), and basic processes on networks (e.g., epidemic spreading). While the covered material essentially applies to all networks, we will focus on social and biological networks in more detail. To be able to analyze and model networks, we will also cover the basics of the Python programming language and its NetworkX module. The course contains a number of hands-on computing exercises, and it is recommended that you bring your own laptops to class. A few loaner laptops will likely be available. The primary course textbook, as well as the software used, is available for free download.