Graphical, Network, and Causal Models

MSDS 452-DL Graphical, Network, and Causal Models.

This survey course introduces applications of graphical and network models in data science. It begins with the mathematics of graphs: nodes (vertices), links (edges), graph types, and graph algorithms. Drawing on network science, students employ random graph, small-world, and preferential attachment models. They identify communities and analyze influence and connections across communication, information, economic, and social networks. Using network science, probabilistic graphical models, and Bayesian inference, students explore the competitive landscape of industries and firms. They build causal models to forecast business outcomes, including marketing and financial performance. 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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