Course Description
This workshop will explore core and emerging techniques and applications for network analysis, including centrality analysis, community detection, connectivity analysis, path analysis, link prediction, and scalable approaches.
These topics will be explored in the context of real application areas such as precision medicine, neuroscience, resource allocation, cyber security, and anomaly detection, through lectures and experimentation led by experts in the field.
Key Takeaways
Course attendees will gain an understanding of the challenges and best practices related to graphs, their analysis, and visualization of data. You’ll also be able to interpret data into actionable insights in your organization.
Demonstrate the mathematical concepts and programming tools necessary to explain core graph theory, practical and application driven topics.
Apply course concepts and skills to create, interact with, visualize, and analyze graphs.
Discuss random and real-world graphs, and common local and global analysis techniques for operating on graphs.
Explain how graphs can be used to leverage relationships (e.g., correlations, clustering) in real data.
Discuss the basics of more advanced topics such as scaling, ethics, and dynamic graphs.
Demonstrate the course techniques on one or more real-world applications in neuroscience, medicine, or a domain of your choice.
Prerequisites
A working knowledge of calculus, statistics, computer science, software design, and systems engineering practices with some exposure to data science and data structures.
