Graduate

Biomedical Data Science Focus Area Curriculum Requirements

Data Science

The past decade has seen major advances in our ability to acquire data on human health across multiple spatio-temporal scales. This wealth of data poses challenges that have never before been confronted. At the heart of these is understanding how massive biomedical data sets are best analyzed to discover new knowledge about the function of living systems in health and disease, and how this knowledge can be harnessed to provide improved, more affordable health care. Because of their deep and broad cross-training in biology, medicine, and engineering, Johns Hopkins biomedical engineers are ideally positioned to take on this challenge.

The Biomedical Data Science focus area provides an educational curriculum that trains students in how to solve such problems. This training is done in close collaboration with faculty of the Departments of Anesthesiology & Critical Care Medicine, Neurology, Neurosurgery, and Psychiatry. We are creating a common research and teaching space where students and faculty of these departments work together with biomedical engineers to develop novel cloud-based technologies and data analysis methods that are needed to improve our ability to diagnose and treat disease more effectively while reducing costs.

Biomedical Data Science focus area students are expected to complete at least two of the following courses:
  • Introduction to Data Science for Biomedical Engineers (EN.580.664)
  • Neuro Data Design I (EN.580.697)
  • Neuro Data Design II (EN.580.698)
  • Precision Care Medicine (EN.580.680/681)
Focus area electives (choose at least 3):
  • Computational Molecular Medicine (EN.553.650)
  • Data Mining (EN.553.636)
  • Foundations of Computational Biology and Bioinformatics II (EN.580.688)
  • High Dimensional Approximation, Probability, and Statistical Learning (EN.553.738)
  • Introduction to Probability (EN.553.620)
  • Introduction to Statistics (EN.553.630)
  • Machine Learning (EN.601.675)
  • Machine Learning: Data to Models (EN.601.676)
  • Machine Learning: Deep Learning (EN.601.682)
  • Mathematical Foundations of Biomedical Engineering I (EN.580.704)
  • NeuroData Design I (EN.580.697)
  • NeuroData Design IIĀ (EN.580.698)
  • Precision Care Medicine (EN.580.680/681)
  • Statistical Machine Learning (EN.601.775)
  • Statistical Theory (EN.553.730)
  • Unsupervised Learning: From Big Data to Low Dimensional Representations (EN.600.692)
Students will select additional graduate level science, technology, engineering, or math courses with the consent of their advisor to complete the total of 30 credits required for graduation.