The Duke University Board of Trustees has approved a new master’s degree program to help meet the need for well-rounded data scientists who can answer questions that matter with data-backed insights. The MS Degree in Interdisciplinary Data Science (MIDS) emerges from a particular vision of data science as an inherently interdisciplinary and team-based endeavor. It borrows technical tools from a variety of quantitative disciplines like computer science and statistics, and connects them to inquiry in nearly all disciplines, including natural sciences, social sciences and humanities. Launching in fall 2018, students will receive interdisciplinary training within the quantitative sciences, exposure to problems in a variety of disciplines, and direct experience in interdisciplinary team-based science.
Robert Calderbank, Director of the Information Initiative at Duke (iiD) and Faculty Director of MIDS, stated, “There has never been a better time to be a data scientist at Duke, and I am delighted to be able to partner with departments to deliver the interdisciplinary curriculum that is a Duke signature.”
Initially, the program expects to recruit 20 students seeking training in quantitative methods and exposure to how these methods are applied to different areas of interest through team-based projects. In the future, this number is expected to grow, with students coming from increasingly diverse academic backgrounds.
“We see data science as inherently interdisciplinary and team-based. I am thrilled about the network of partnerships around the university that will distinguish this degree as one that allows students to take data science into many different domains,” explained Tom Nechyba, Director of the Social Science Research Institute (SSRI) and Faculty Director of MIDS.
Hosted jointly by the Information Initiative at Duke (iiD) and the Social Science Research Institute (SSRI), it invites units across the Duke campus to contribute elective courses. These elective courses will constitute half of the courses taken by students, giving them ownership of their focus and direction within the program. This intellectual vision of interdisciplinary and team-based data science offers not only a degree curriculum but also a complementary new model for advancing university-wide engagement in graduate education across departments and schools.