This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics.

Within the city framework, information technologies are a key for providing new services and applications that contribute to success. This course examines a cross-section of information and computing technologies that can best contribute to city sciences. Students will learn of the concept of the Internet of Things, geographic information systems (GIS), ubiquitous computing, and wearable interfaces as they relate to future cities. Additionally students will look at existing technologies in environmental sensing for monitoring and improving cities. This course is only offered at RIT Dubai campus.

The research and thesis course unfolds over two semesters. Students will receive instruction in standard research methods, as well as the best practices for writing and presenting project findings. The remainder of the course is devoted to students working individually with a faculty supervisor on the research, organization, analysis, and eventual presentation of their project findings. To complete the thesis course, students must successfully defend their work before a committee consisting of a representative sample of the program faculty, the student’s supervisor, and the program director. The committee is responsible for determining the student’s final grade for the thesis. The course is graded on the (R, U, or I) basis with a successful defense of the completed thesis being the deciding factor. Department approval required for enrollment.

This course allows participants to depict the challenges of data analytics projects and guides them to use the necessary leadership, innovation, entrepreneurial and data analytics skills to develop successful data-driven business models for economic value creation.

Special topics are experimental courses announced as offered. Variable credit.