BME 304L
BME Data Science
Daniel Reker
Description from DukeHub
Data science is vital for extracting insights from complex biomedical data, transforming drug discovery, delivery, omics, diagnostics, and image analysis. This course covers 'Data,' 'Models,' and 'Evaluations' in machine learning, focusing on biomedical engineering challenges and opportunities. Students learn fundamentals and apply algorithms from linear regression to neural networks via case studies and labs with real biomedical datasets, preparing for advanced study and careers in academia, pharma, healthcare, and biotech. Prerequisite: BME 244L and MATH 218D-2 and (EGR 105L or COMPSCI 201)
Skills
- data and statisticsdescription: "data science" and 2 more
- ai and machine learningdescription: "machine learning" and 1 more
- lab workhas a lab component
AI-resilient: 2 of 5
- Yes: Physical, lab or field work · "laboratory"
- No: Live work with people
- Yes: Judgment someone is accountable for · "diagnostics"
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
More in BME
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- Quantitative Physiology with Biostatistical Applications BME 244L9
- Medtech Prototyping Skills (GE) BME 254L
- Modeling Cellular and Molecular Systems BME 260L
- Signals and Systems BME 271D