Academics - Curriculum Overview

The Duke University Master in Interdisciplinary Data Science (MIDS) curriculum is designed to prepare you to be able to add value to any data-driven team the day you graduate.

While at Duke, you'll develop your quantitative expertise through coursework in statistics, math, computer science, and other analytical disciplines. You'll also develop your technical prowess through lessons in programming and computational computing and your professional skills through workshops and continual coaching in communications, teamwork, and leadership.

Student working on laptop

The components of the Master in Interdisciplinary Data Science program are:

Online summer review in statistics, linear algebra, and programming to make sure all students arrive to campus on the same playing field.
Pre-orientation bootcamp to introduce you to your classmates and the technical and professional practices we will be using throughout the program.

Seven required core courses that cover critical topics in statistical modeling, machine learning, programming, data wrangling, text analysis, database systems, data visualization, data regulations and ethics, and data interpretation.

Up to eight additional electives in departments across the University to deepen and broaden one’s knowledge in the topics students are most passionate about, with the option to major in specific concentrations.

Data science seminar that hosts outside speakers to discuss the latest developments and issues in data science.

Required summer internship between Years 1 and 2.

One-year capstone project with Duke’s world-class research faculty and outside partners.

Structured training to develop communication, teamwork, and leadership skills.

Career development events and workshops.

Guidance from a Duke approved mentor to help you through the MIDS program.

All of the MIDS core courses and programming will use hands-on team-based active learning experiences with messy data sets to ensure students graduate practiced in handling the challenges of real-life data.

The core courses will also emphasize frameworks that help students learn how to ask “the right questions” and provide actionable answers to those questions.