Data Science is Interdisciplinary
Duke University Master in Interdisciplinary Data Science (MIDS)
combines rigorous computational and technical training with field knowledge and repeated practice in critical thinking, teamwork, communication, and collaborative leadership to produce data scientists who can add value to any field.
Why Choose Duke MIDS?
- Experienced faculty
- Small class sizes
- Interdisciplinary curriculum
- Team-based science
- Capstone Projects
Our Students Bring a Global Perspective
Duke MIDS by the Numbers
students enrolled
countries represented
age range
%
prior work experience
Alumni: Get Involved
Mentor a student, attend a workshop, catch up with former classmates, and more!
Become a Partner
Learn how you can help MIDS students—and how they can help you.
Four Faculty Elected to American Academy of Arts & Sciences
Robert Calderbank, MIDS Director, has been elected members of the American Academy of Arts & Sciences. Founded in 1780, the Academy honors excellence and convenes leaders to examine new ideas, address issues of importance to the nation and the world, and advance the public good. This year’s election of 261 new members continues a tradition of recognizing accomplishments and leadership in academia, the arts, industry, public policy, and research.
Two Faculty Recognized for Exceptional Co-Leadership of Bass Connections Team
Kyle Bradbury and Jordan Malof are the joint winners of the 2022 Bass Connections Leadership Award. This award recognizes outstanding faculty and staff team leaders for their creativity, intellectual vision and commitment to student mentoring on Bass Connections project teams.
New MIDS Faculty: Andrea Lane
We are thrilled to announce a new member of the MIDS team. Andrea will be teaching IDS 702, Modeling and Representation of Data. She is a recent PhD graduate of the Rollins School of Public Health at Emory University and holds a BSPH in biostatistics, BA in mathematics, and music minor from UNC Chapel Hill. Andrea is passionate about statistics research with public health applications and particularly interested in the intersection of causal inference and multi-omics.