Data Science for Social and Information Networks

Catalog description

The world we live in is built upon a myriad of networks: Human society is defined by our interpersonal relationships. Organizations are structured around interconnecting roles and lines of authority between workers, colleagues, and bosses. Global information is conveyed across a world-wide web of linked content. As we have witnessed recently, epidemics spread over a social network of contacts, in the same way in which we buy products as we are influenced by our peers. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. In networks, local interactions among members of small communities can often propagate and further affect the outcomes of an entire system. This course combines theories, models, and algorithms from computer science, economics, and the social sciences to analyze network data and find solutions to business problems. More information: https://shanghai.nyu.edu/is/course-spotlight-network-analytics Prerequisites: Introduction to Computer Programing (to manipulate network datasets), and Calculus. Fulfillment: This course satisfies BUSF Non-Finance Elective; BUSM Non-Marketing elective; IMB Business Elective; Social Science methods; Computer Science elective; Data Science Concentration in AI.