Big Data is everywhere. Whether you are in academia or industry, insights and answers to complex questions are often hiding within a vast amount of available data. In many cases, this concerns relational datasets which can be represented or indexed by graphs. These can either correspond to a true network or are a network-based representation. To excel in the analysis of such datasets it is paramount to have a fundamental understanding of the underlying principles, tools, and network models, as well as knowing their corresponding motivation, usefulness, and potential pitfalls. This course covers several important methods for network analysis and provides students with the right knowledge to use tools and concepts to tackle, in a principled way, research questions and challenges in this important area of data science. The design of the course will combine acquisition of relevant knowledge with actual experience in analyzing networks through relevant problems real-world network analysts might face. In addition, students are taught to find, read and understand relevant literature in this field.