The Duke University Master in Interdisciplinary Data Science (MIDS) is home for creative problem-solvers who want to use data strategically to advance society. We are cultivating a new type of quantitative thought leader who uses disruptive computational strategies to generate innovation and new insights.
MIDS combines rigorous computational and technical training with field knowledge and repeated practice in critical thinking, teamwork, communication, and collaborative leadership to generate data scientists who can add value to any field.
Duke MIDS is a two-year program designed to help meet the need for knowledgeable data scientists who can answer important questions with data-backed insights. All MIDS students complete a set of eight core courses that cover critical topics in statistics, machine learning, database management, data wrangling, data communication, analytical thinking, team management, and ethics. The core courses are designed to fit together as a cohesive set of learning experiences, and ensure that students have repeated practice interpreting and reporting the results of analyses on real data sets. Accompanying these core courses, students choose a set of approximately eight electives that deepen their expertise in a methodological or domain area. Students culminate their experience with a capstone project that will be completed over the course of at least one year with mentoring from Duke’s world-class faculty.
Why Duke MIDS?
For more information on the syllabus visit the "courses" section: https://mids.duke.edu/
Who Should Apply?
MIDS is open to all applicants who demonstrate a passion for data analysis, a mastery of analytical reasoning, an aptitude for learning quantitative and technical skills, and compelling academic or professional achievement.
We welcome applicants of any age and background, including (but not limited to) recent college graduates with quantitative majors, database engineers who have been in the IT field for years, government professionals who want to integrate data science into federal or local offices, and journalists who want to incorporate data mining into their investigative skills.
Due to our comprehensive approach, our application process requires applicants with primarily quantitative backgrounds to demonstrate their commitment to excelling in the problem-solving, communication, and team-building aspects of data science. Likewise, applicants without quantitative backgrounds are asked to demonstrate their commitment to learning quantitative concepts and skills quickly through mechanisms like online classes or recommendations from colleagues with strong quantitative track records.
We provide resources for students to review and learn critical concepts and skills before beginning the core courses, so that all students can begin the core courses on a level playing ground.
For more information on the financial support, funding and scholarships that are available please visit: https://gradschool.duke.edu/financial-support