Explore the fundamentals of network analysis in this comprehensive lecture by Professor Gesine Reinert from the University of Oxford. Delve into various network representations of complex data, learning about network summaries and parametric models. Discover statistical inference techniques using network summaries and parametric models, and gain insights into nonparametric approaches. Cover essential topics including types of networks, adjacency matrices, degree distributions, clustering coefficients, transitivity, motifs, betweenness, and network models such as the Strogatz model and power law distributions. Gain practical knowledge through examples like London congestion and citation networks, and understand key concepts such as the small world phenomenon and triangle distribution. Equip yourself with the tools to analyze and make sense of complex network data in this informative 1 hour 37 minute lecture from the Alan Turing Institute.