Unstructured Data Science Modeling

Catalog description

This graduate-level course is designed for students interested in learning the foundations of unstructured data analytics. The focus will be on applying these techniques to applications in specific policy related scenarios. We will cover the intuition of the theoretical underpinnings, but the focus here is on using tools to understand and use unstructured data. We will cover topics manifold learning, clustering, topic modeling, and deep neural networks. We will use Python and learn to utilize Jupyter Notebooks. Using these tools, students will learn the underpinnings of each method so that they can choose the most appropriate for particular applications. This course is designed for students in the Jeb E. Brooks School of Public Policy MS in Data Science for Public Policy program. Other students may only enroll with permission of the instructor. As a graduate-level course, students are expected to have thoroughly read all materials prior to class and be well-prepared to discuss readings and cases with colleagues.