Duke University

Overview

The Master of Science in Interdisciplinary Data Science (MIDS) at Duke University is tailored for individuals seeking to establish a robust foundation in data science while applying their skills to diverse real-world challenges. The program emphasizes a structured curriculum, collaborative learning, and a capstone project, enabling students to cultivate the technical and communication skills necessary to spearhead data-driven initiatives.

What you'll study

The MIDS curriculum is designed to progress from foundational skills to advanced methodologies and practical applications. Students engage in core coursework, select electives that align with their interests, and culminate their studies with a capstone project.

• Foundational bootcamp: An introductory training session aimed at supporting students from varied academic backgrounds.
• Core MIDS courses (examples):
  • Data Science Dialogues
  • Data Science Ethics
  • Data Logic, Visualization, and Storytelling
  • Data Management Systems
  • Principles of Machine Learning
  • Modeling and Representation of Data
• Electives: Students can choose courses to enhance expertise in fields such as machine learning, analytics, data systems, or domain-specific applications (availability may vary).
• Capstone project: A hands-on project where students tackle a data-driven issue, devise a solution, and effectively communicate their findings.

Note: Course offerings and sequencing may vary by cohort. Prospective students should confirm the latest curriculum details with Duke MIDS.

Entry requirements

• Minimum qualification: A bachelor's degree in engineering or science from an accredited institution.
• Transcripts: Required, including an estimate of GPA and the grading scale.
• Standardized test: GRE® General Test (or equivalent) — official results must be submitted.

Career prospects

Graduates of the MIDS program are well-equipped for a variety of career paths in data science and analytics. With a solid foundation in both technical and communication skills, they are prepared to lead initiatives across multiple sectors, contributing to data-driven decision-making and innovative solutions.