Course Catalog

The character, success, and costs/benefits of information technologies are socio-technical matters. Because of this, best practice for IT design and integration relies on participants' ability to understand and create for the totality of those settings, including social and technical dimensions. This course provides students with analytic tools for examining socio-technical settings and experience in applying that knowledge in IT modeling, design and management.

An introduction to statistical and probabilistic models as they pertain to quantifying information, assessing information quality, and principled application of information to decision making, with focus on model selection and gauging model quality. The course reviews relevant results from probability theory, parametric and non-parametric predictive models, as well as extensions of these models for unsupervised learning. Applications of statistical and probabilistic models to tasks in information management (e.g. prediction, ranking, and data reduction) are emphasized.

Information modeling is critical to all information systems and analysis. This course introduces students to foundational frameworks (set theory and logics) and basic underlying objects (entities, attributes, and relations) of information modeling. A variety of modeling approaches (use case modeling, relational database design, first-order predicate logic, and semantic web technologies) are considered, and recent developments (non-relational databases and knowledge graphs) are reviewed. Modeling strategies are assessed by their expressiveness and reasoning capabilities.