The course Network Analysis in Systems Biology provides an introduction to Big Data analysis in systems biology including statistical methods used to identify differentially expressed genes, performing various types of enrichment analyses, and applying clustering algorithms. You will also learn how to construct, analyze and visualize functional association networks that can be created from many resources, including gene regulatory networks connecting transcription factors to their target genes, protein-protein interaction networks, cell signaling pathways and networks, drug-target and drug-drug similarity networks and other functional association networks. Methods to process raw data from genome-wide mRNA expression (microarrays and RNA-seq) will be presented. Processed data will be clustered, and gene-set enrichment analyses methods will be covered. The course is mostly about practical tutorials for analyzing various high content experimental datasets, but it also contains theoretical discussions about the mathematics behind the methods and tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology but also math, physics, chemistry, computer science, biomedical and electrical engineering.