In the Master of Science in Complex Network Analysis (Economic and Technological Networks) program, students will build a strong foundation in complex network analysis, supplemented by domain-specific coursework in financial and economic networks. This may include topics ranging from econometrics to deep learning. This broad concentration is designed to give students targeted skill sets that will ultimately lead to real insights in business and finance.
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Students in this program will engage in experiential learning in three ways. First, instructor-led experiential learning will be embedded into coursework and learning assessments as a design principle, guiding the creation of new courses. From the outset of the program, students will be exposed to the complex challenges of sourcing, cleaning, and analyzing network data and will practice gaining insights from real-world situations. Second, research rotation, capstone, or thesis courses provide student-led experiential learning opportunities. While the challenges tackled here will be unique to the student (i.e., their interests and emerging topics in the field), the research will be closely aligned with the authentic tasks faced by network scientists. Third, students are encouraged to participate in a range of cocurricular experiential learning opportunities such as hack-a-thons and cocurricular industry events. These events may occur in response to emergent current events and/or in conjunction with community/industry partners. In this way, these experiential learning opportunities will be driven by business and societal needs. Students who wish to engage in deeper experiential learning may also participate in a co-op.