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.

This course introduces students to foundational skills in data analytics, with a focus on mathematical foundations. Students will explore topics that form the backbone of modern data analytics such as machine learning, data mining, artificial intelligence and visualization. Tools for statistics will be introduced to students for how to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making.

This course introduces students to the challenges in large and small organizations related to data analysis and storage. Students will be introduced to economic infrastructure approaches for handling data securely. Platforms which are hosted both on-premises of organizations and in the cloud will be covered in this course.