This course will provide students with an introduction to the principle of risk management and its three key elements: risk analysis, risk assessment and vulnerability assessment. Students will also learn the differences between quantitative and qualitative risk assessment, and details of how security metrics can be modeled/monitored/controlled and how various types of qualitative risk assessment can be applied to the overall assessment process. Several industry case studies will be studied and discussed. Students will work together in teams to conduct risk assessments based on selected case studies or hypothetical scenarios. Finally, they will write and present their risk assessment reports and findings.

This course explores how the fields of psychology, digital design, and computing converge in the design, development, and evaluation of new technologies that people find effective and enjoyable to use. Students will investigate the field of human-computer interaction (HCI), with a focus on how users' various sensory, motor, and cognitive abilities are essential to their successful use of technology. Students will be exposed to modern research methods and paradigms in field of human-computer interaction, including predictive modeling, heuristic evaluation, interpretive methods, and experimental user testing. Students will learn key design principles and guidelines and apply them to analyze existing designs and conduct a design process that is centered on human users of technology.

Students will survey and apply contemporary techniques used in analyzing and modeling information requirements. Requirements will be elicited in a variety of domains and abstracted at conceptual, logical, and physical levels of detail. Process, data, and state modeling will be applied in projects that follow a systems development lifecycle. Object-oriented modeling will be explored and contrasted with data and process oriented modeling. Individual and team modeling assignments will be required.

The second course in a two-course, senior level, system development capstone project. Student teams complete development of their system project and package the software and documentation for deployment. Usability testing practices introduced in prior course work are reviewed, and additional methods and processes are introduced. Teams present their developed system and discuss lessons learned at the completion of the course.

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.