Data Science

Advanced Graph Analysis and Applications

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Participants in this program will engage in a variety of interactive exercises to get hands-on experience in the process of transforming graph-based data into insight for making better decisions.

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Duration: 4-Day Course

Course Description

Analysis and Visualization

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.


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.

Who Should Take this Course

Scientists and engineers with a fundamental understanding of network analysis who want to learn more about methods and models that can be applied to current and emerging problems of scientific and National Security interest.

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Will Gray-Roncal
Principal Research Scientist
Will Gray-Roncal is a Principal Research Scientist at the Johns Hopkins University Applied Physics Laboratory, with expertise in data science, neuroscience, artificial intelligence, precision medicine, and learning research, including leadership of the CIRCUIT Program.