This is the first course that will be jointly taught by the faculty in Computer Science and Biology. A strong design and implementation could pave the way for more interdisciplinary course offerings within the sciences.
Shweta Bansal & Lisa Singh
In a generation that has grown up with Facebook, the World Wide Web, and a highly-connected global economy, the concept of networks is a natural one. This project leverages this familiarity to introduce students to a quantitative way of studying interactions within complex systems. Network science is an emerging discipline that studies network representations and predictive models as a way to explain different physical, social, and biological phenomena. This field focuses on identifying common principles, methods, and patterns that explain network behavior. Network science is inherently interdisciplinary, combining tools from mathematics, computer science, sociology, and physics in intuitive ways to solve problems in both their life and social sciences, as well as in humanities, economics, medicine and law. The combination of quantitative and interdisciplinary scholarship in network science is crucial for students, irrespective of their field of study. Given this background, Principal Investigators Shweta Bansal and Lisa Singh are developing a course that embeds important quantitative and algorithmic approaches and prevalent technologies into a non-majors course focused on networks. Teaching these concepts in conjunction with prevalent technologies is an important step towards transitioning students from technology consumers to technology producers.
Bansal and Singh are developing an interdisciplinary course focused on the emerging science of complex networks and their applications. The material include mathematics and computer science of networks, their applications to biology, sociology, business, transportation and other fields, and their use in the research of real complex man-made and natural systems. Their plan is for students taking the course to learn what networks are, characteristics that are used to define different types of networks, and methods for analyzing networks. Students will have the opportunity to apply their knowledge to the analysis of real world networks using an interactive storytelling environment that integrates programming code execution with text, math, and visual analytics into a single web-based document. This project began in January 2015.
