This course surveys the fundamental concepts and theories used in organizing and structuring data that arise in a wide range of contexts (e.g. lab sciences, economic, social services, computing system performance, environmental sustainability). Course topics include data organization theory, basic relational model theory, normalization theory, relational algebra, data quality, data integrity and security, and mapping a data model into a database schema. Students will learn how to construct and use databases while maintaining the security and integrity of data as well as develop AI literacy in the context of data model design, learning how to apply and critique Artificial Intelligence responsibly and ethically. Hands-on assessments are included in this course.

In this course, students will build applications that interact with databases. Through programming exercises, students will work with multiple databases and programmatically invoke the advanced database processing operations that are integral to contemporary computing applications. Topics include the database drivers, the data layer, connectivity operations, security and integrity, and controlling database access.

Rapidly expanding collections of data from all areas of society are becoming available in digital form. Computer-based methods are available to facilitate discovering new information and knowledge that is embedded in these collections of data. This course provides students with an introduction to the use of these data analytic methods, with a focus on statistical learning models, within the context of the data-driven knowledge discovery process. Topics include motivations for data-driven discovery, sources of discoverable knowledge (e.g., data, text, the web, maps), data selection and retrieval, data transformation, computer-based methods for data-driven discovery, and interpretation of results. Emphasis is placed on the application of knowledge discovery methods to specific domains.