Network Modeling and Analysis in Python

О курсе

In “Network Modeling and Analysis in Python,” you will learn how different types of network analysis can be used to make sense of complex systems. You’ll learn how algorithms can be used to better understand disease epidemics, human community structure, and the flow of information on social media. This course combines network theory with empirical analysis of real-world networks using the Python library NetworkX. You’ll learn about community structure in networks as well as several popular algorithms for community detection and applications. This course introduces a wide range of advanced network models. You’ll study random network generation models and how they can be used to create realistic graphs and explain how networks function. You’ll also learn about models that explain diffusion and the spread of epidemics in networks, such as the SI, SIS, SIR, independent cascade, and linear threshold models. This is the third course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.

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Daniel Romero

Professor of Information and Professor of Complex Systems