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.

All BME Department M.S.E. students are expected to complete two of the following courses (or equivalent):
  • Systems Bioengineering 1 (EN.580.721)
  • Systems Bioengineering 2 (EN.580.722)
  • Systems Bioengineering 3 (EN.580.779)
Focus area required classes (choose at least 2 of 3):
  • EN553.620 Introduction to Probability, Torcaso, Spring
  • EN553.630 Introduction to Statistics, Athreya, Spring
  • EN601.676 Machine Learning: Data to Models, Saria, Spring
Focus Area Electives (choose at least 3):
  • EN553.636 Data Mining, Budavari, Spring
  • EN553.730 Statistical Theory, Priebe, Fall
  • EN553.738 High Dimensional Approximation, Probability, and Statistical Learning, Maggioni, Fall
  • EN600.692 Unsupervised Learning: From Big Data to Low Dimensional Representations, Vidal, Spring
  • EN601.675 Machine Learning, Staff, Fall, Spring
  • EN601.775 Statistical Machine Learning, Arora, Fall
  • EN601.682 Machine Learning: Deep Learning, Hager, Spring
  • EN580.670/671 Precision Care Design I & II, Winslow & Sarma, Fall & Spring
  • EN580.697/698 NeuroData Design I & II, Vogelstein, Fall and Spring
  • EN580.688 Foundations of Computational Biology and Bioinformatics II, Karchin, Spring
  • 580.691/491 Learning Theory, Spring
Students will select additional graduate level science, technology, engineering, or math courses with the consent of their advisor to complete the total of 10 full courses required for graduation.