Network Analysis
Network analysis is an essential discipline that spans numerous fields, including social network analysis, biological research, communication systems, transportation logistics, and organizational behavior. Network analysis enables us to understand complex relationships, optimize network efficiency, predict behavior, and uncover hidden patterns in data. This course provides a comprehensive introduction to the principles and techniques of network analysis, focusing on graph-based networks. It begins with thoroughly exploring the fundamental concepts and terminology, including nodes, edges, and paths, as well as the distinction between different types of networks, such as social, biological, and technological networks.
Participants will learn how to mathematically and visually represent networks using tools like adjacency matrices and lists. The course will also explore network measures, including node-level metrics like degree and centrality, path-level measures like shortest paths, and network-level properties like density and modularity. These measures are crucial for analyzing and understanding the structure and dynamics of networks. Python-based code implementation using tools like NetworkX is also outlined.
By the end of this course, participants will not only have a solid understanding of network theory and its applications but also the practical skills to analyze, model, and interpret complex networks effectively. Whether you are working in data science, engineering, biology, or social sciences, this course will equip you with the skills needed to leverage network analysis in your field, making you a more capable and effective professional.
Course Lessons
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Course Instructor
Dr. Rahul Rai
CEO AIBrilliance
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