Web and Network Data Science

MSDS 452-DL Graphical and Network Models.

This survey course introduces applications of graphs in data science. It begins with the mathematics of graphs, with nodes (vertices) and links (edges). Students utilize graph and graph-relational databases for knowledge graphs. Drawing on network science, they employ random graph, small world, and preferential attachment models, analyzing connections across information networks and social networks. Students utilize graph neural networks for deep learning. They see event graphs and queuing networks for process and performance analysis. The course ends with graphical probability models and their applications to causal inference and automated reasoning. Prerequisites: (1) MSDS 420-DL Database Systems or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.

Python is the primary language in this course. Students benefit by taking the Python Learning Studio and MSDS 430 Python for Data Science prior to taking this course.

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