Course Description
The workshop will consist of 4 days of expert practitioners leading the participants through practical application of advanced graph analysis visualization and analysis scenarios. It 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.
Describe key mathematical concepts and algorithms used for different domains and problem spaces, with an emphasis on new techniques.
Develop an understanding of how to approach noisy, real world graph problems, including technical approaches and real-world best practices.
Apply course concepts and skills to analyze technical papers and demonstrate how to extract knowledge from real-world graphs.
Discuss the advantages and disadvantages of different techniques and how to match particular approaches to analyses and problem statements.
Prerequisites
Fundamental knowledge of data science and graph analysis. Completion of the prior JHU Lifelong Learning workshop Graphs: Analysis and Visualization is recommended, but not required.
