Why Interdisciplinary Data Science?

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.

All fields need data scientists

You want to use data to advance the industry or field you are most passionate about.

Effective data scientists need depth and breadth

Data scientists who have proficiency in diverse ways of thinking and deep content expertise innovate most effectively.

World problems require data scientists with diverse backgrounds

Data analysis and creative engineering need to be integrated with nuanced domain knowledge, collaborative leadership, and effective management skills to harness data’s full potential.

There has never been a better time to be a data scientist at Duke, and I'm delighted to be able to partner with departments to deliver the interdisciplinary curriculum that's a Duke signature.

Robert Calderbank, Director, iiD

I'm 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.

Tom Nechyba, Director, SSRI

Program Overview

Photo of Jana Schaich Borg
Jana Schaich Borg
Faculty Director, MIDS

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?

World-renowned faculty

Work with Duke’s elite faculty in fields across the university, including computer science, statistics, math, economics, political science, sociology, medicine, neuroscience, law, and history.

Personalized pathways

Experience the full range of the data science ecosystem and graduate as an expert in at least one analytical approach or branch of technology—you decide where to pursue breadth vs. depth of knowledge.

Diverse student body

Study and collaborate with talented and passionate students of different ages, backgrounds, and skill sets.

Comprehensive training

Develop quantitative acumen, technical expertise, domain knowledge, leadership skills, and project management experience all in one program.

Collaboration across disciplines

Graduate knowing how to work with any kind of group, and being able to explain the actionable significance of analyses to any kind of audience.

Critical thinking about real problems

Practice applying data science concepts to contemporary problems throughout the two years of the curriculum, so that you graduate being able to think through any computational challenge logically and methodically.

MIDS Courses

DATA COLLECTION METHODS IN SURVEY RESEARCH

UNC/Odum Institute and Duke/SSRI
Davis Library 219 / Gross Hall 230E
Tuesdays 2:00 – 4:45 PM
Instructor: Doug Currivan

Soci 760 (UNC) / IDS 690-01 (Duke)​

Start date: Fall 2017


This course examine the effects of key survey design decisions on data quality, concentrating on the impact of modes of data collection on coverage error, nonresponse error, and measurement error. The course focuses on advances in computer assisted methodology and comparisons among various data collection methods (such as telephone versus face to face, paper versus computer assisted, interviewer administered versus self-administered).  It will also examine the literature on interviewer effects, including literature related to the training and evaluation of interviewers.  The course will also review current literature on the reduction of nonresponse and the impact of nonresponse on survey estimates.

This course meets Tuesdays from 2:00 – 4:45 p.m. and will alternate between the two campuses, with connections via interactive video allowing students to choose between attending on their home campus or traveling between Duke and UNC to attend sessions at both campuses in person.  The course is taught via a traditional interactive presentation and discussion format.

Year 1 Fall
1st half2nd half
Data to Decision
Modeling and Representation of Data
Optional mini-courses or mini-projectData Wrangling and Introduction to Text Analysis
Elective
(Data Seminar)
Year 1 Spring
1st half2nd half
Principles of Machine Learning
Data Mangement Systems
Data Logic, Visualization,
and Storytelling
Data Science Ethics
Elective
(Data Seminar)
Year 2 Fall
1st half2nd half
Capstone
Elective
Elective
Elective
(Data Seminar)
Year 2 Spring
1st half2nd half
Capstone
Elective
Elective
Elective
(Data Seminar)

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.

Frequently Asked Questions

Anyone interested in advancing their career or changing career paths by developing interdisciplinary skills is encouraged to apply.

No, we do not have an online program.

We anticipate many of our students will have work experience, but it is not required.

Applicants from a range of academic backgrounds will be considered.

GRE scores are required. If English is not the first language, TOEFL scores are required.

Yes, MIDS is a full-time degree program and qualifies for a visa. International applicants are encouraged to apply as early as possible in order to allow ample time to clear the student visa process. Non-citizens residing in the U.S. are encouraged to apply early as well. Applications can (and should) be submitted in advance of supporting documents, such as recommendation letters, transcripts, and language test scores.

Applicants will apply directly through the Duke Graduate School: https://gradschool.duke.edu. Application is now open for 2018.