Certificate in Biomedical Data Science

For Master’s Students

Gain a Competitive Advantage in Industry and Academia

Biomedical engineering is the centerpiece discipline to apply data science techniques for major positive impact.

At Duke, you’ll learn from experts how to translate data into actionable health insights—and increase your competitiveness in a rapidly expanding industry.

How to Apply
Wilkinson Building in fall with Medical Center in background

Requirements

  • Be a Duke Master of Engineering (MEng) and Master of Science (MS) student intending on a career relating to biomedical data science
  • Complete all departmental requirements for a master’s degree
  • Complete at least four (4) of the approved courses relevant to data science. At least two (2) of these courses must be offered through Duke BME
  • Complete a biomedical data science-relevant project and submit a two-page abstract and poster to the Biomedical Data Science Committee. The project could result from previous course projects, independent study, or a master’s thesis. It must have a non-trivial technical novelty and demonstrate proficiency in developing novel methodologies or scientifically utilizing a broad range of advanced data science tools to solve impactful biomedical problems

Faculty

Jessilyn Dunn Profile Photo
Jessilyn Dunn Profile Photo

Jessilyn Dunn

Assistant Professor of Biomedical Engineering

Timothy Dunn Profile Photo
Timothy Dunn Profile Photo

Timothy Dunn

Assistant Professor of Biomedical Engineering

Sina Farsiu Profile Photo
Sina Farsiu Profile Photo

Sina Farsiu

Director of Master’s Studies, Anderson-Rupp Professor of BME

Roarke Horstmeyer Profile Photo
Roarke Horstmeyer Profile Photo

Roarke Horstmeyer

Assistant Professor of Biomedical Engineering

Daniel Reker Profile Photo
Daniel Reker Profile Photo

Daniel Reker

Assistant Professor of Biomedical Engineering

Courses

Courses for this master’s certificate are offered by departments and programs within Duke’s Pratt School of Engineering, School of Medicine, and Trinity College of Arts & Sciences.

    • BME 590D: Deep Learning App HealthCare
    • BME 548L: Machine Learning and Imaging
    • BME 590L: Computational Foundations for Biomedical Simulations
    • BME 671L: Signal Processing and Applied Mathematics
    • BME/B&B 590: Intro Biomedical Data Science
    • BME 590: Machine Learning in Pharmacology
    • BME 544: Digital Image Processing
    • BME 503: Computational Neuroengineering
    • BME 574: Modeling and Engineering Gene Circuits
    • BME 590: Data Science and Health
    • EGR 590-06: AI for Everyone
    • BME 590: Deep Neural Network Models of the Nervous System
    • BME 790L: Linear Algebra
    • BIOSTAT 824: Case Studies in Biomedical Data Science
    • BIOSTAT 707: Statistical Methods for Learning and Discovery
    • CEE 690-05: Health and Environmental Data Science
    • CBB 540: Statistical Methods for Computational Biology
    • CBB 561, 662 or 663: Algorithms in Computational Biology
    • COMPSCI 527: Introduction to Computer Vision
    • COMPSCI 671D: Theory and Algorithms for Machine Learning
    • ECE 685D: Introduction to Deep Learning
    • ECE 590: Advanced Deep Learning
    • ECE 590: Computer Engineering ML and Deep Neural Nets
    • ECE 590: Wearable and Ubiquitous Computing
    • ECE 588: Image & Video Processing

Important Notes for Students

  • Some of the courses listed above have a significant overlap. Thus, only one (1) course from each of the following course groups will be counted among the four (4) courses required for this certificate:

    • Group 1: BME 544, ECE 588
    • Group 2: BME/B&B 590 (Intro Biomedical Data Science), BIOSTAT 707 (Statistical Methods for Learning and Discovery), CBB540: Statistical Methods for Computational Biology
    • Group 3: BME 590: Data Science and Health, EGR 590-06: AI for Everyone and CEE690-05: Health and Environmental Data Science
    • Group 4: CBB 561, CBB 662 and CBB 663

  • There is no list of universal course prerequisites for this program. Instead, each instructor sets the prerequisites for enrollment in each course.

    Before enrolling in a course, students are highly encouraged to contact the instructor to:

    • Learn the prerequisites for the course, including the required programming language background
    • Obtain and carefully review the course syllabus

Suggested Course Schedules

Data science is diverse. There is no fixed course schedule, but here are three examples.

  • Semester 1 Semester 2 Semester 3
    BME/B&B 590: Intro Biomedical Data Science or ECE 685D: Introduction to Deep Learning BME 544: Digital Image Processing, or COMPSCI 527: Introduction to Computer Vision BME 548L: Machine Learning and Imaging
    BME 590D: Deep Learning App HealthCare
  • Semester 1 Semester 2 Semester 3
    BME/B&B 590: Intro Biomedical Data Science CBB 662A: Algorithms in Structural Biology & Biophysics BME 590L: Machine Learning in Pharmacology
    BME 574: Modeling and Engineering Gene Circuits
  • Semester 1 Semester 2 Semester 3
    BME/B&B 590: Intro Biomedical Data Science, or ECE 685D: Introduction to Deep Learning COMPSCI 671D: Theory and Algorithms for Machine Learning BME 590L: Computational Foundations for Biomedical Simulations
     ECE 590: Advanced Deep Learning

How to Apply

The first step is to apply to a Duke Engineering master’s program.

Apply to Duke

How to Register

The next step is to register—send us a Letter of Interest by July 1 for fall graduation or Nov. 1 for spring graduation.

Send Letter
Wilkinson Building in fall with Medical Center in background