Master of Science in Data Science (Health Data Science)

Program Learning Outcomes

At the end of the program, graduates should be able to perform the following roles in data science:

  1. Develop scientific conclusions using data science methods on biomedical data in clinical research and translational medicine
  2. Evaluate massive biomedical data sets to reveal patterns, trends, and associations using statistical models and machine learning
  3. Recommend decisions in healthcare delivery processes using machine learning and optimization techniques
  4. Formulate statistical models from biomedical data for meaningful analyses relevant to healthcare
  5. Collaborate with a health professions team on a biomedical data-driven research project
  6. Design visualizations that effectively communicate results and findings to users
  7. Interpret biomedical data for exploration and analysis
  8. Organize data science activities according to policy, privacy, security, and ethical considerations

Course Requirements

Core Courses (Total: 23 units)

Data Science 211Programming and Databases in Data Science3 units (2 units lec., 1 unit lab.)
Data Science 217Data Visualization and Storytelling3 units (2 units lec., 1 unit lab.)
Data Science 220Data Mining3 units (2 units lec., 1 unit lab.)
Data Science 230Statistical Machine Learning3 units (2 units lec., 1 unit lab.)
Data Science 240Big Data Processing3 units (2 units lec., 1 unit lab.)
Data Science 290Research Methods and Ethics in Data Science2 units
Data Science 300.1Thesis Proposal3 units
Data Science 300.2Thesis Implementation3 units

Elective/Cognate (Total: 8 units)

Students must take 4 courses (at least 2 units per course)

Data Science 235Advanced Computational Statistics2 units
Data Science 238Time Series and Forecasting in Data Science2 units
Data Science 250Natural Language Processing2 units
Data Science 260Affective Computing2 units
Data Science 270Pattern Recognition2 units
Data Science 273Advanced Machine Learning Methods2 units
Data Science 280Prescriptive Analytics and Modeling in Data Science2 units
Data Science 283Business Intelligence and Data Analytics2 units
Data Science 285Geospatial Analytics2 units
Data Science 297Special Topics2 units
HI 201Introduction to Health Informatics2 units
HI 210Systems Analysis and Design2 units
Epi 201Principles of Epidemiology3 units
BNF 240Representations and Algorithms in Bioinformatics3 units
APhysics 287Medical Imaging Fundamentals2 units
MC 211Computer-Aided Drug Discovery3 units
MC 212Cheminformatics2 units

Program Requirements

Admission

  • Fulfill general admission requirements of the National Graduate Office for the Health Sciences (NGOHS)
  • Have at least a baccalaureate degree in the sciences with basic training in multivariate calculus up to linear algebra and probability theory. Otherwise, prospective students may opt to take the undergraduate equivalent (Math 85, Math 120, Stat 121) in the BS Computer Science program or the preparatory course Data Science 201 Mathematics and Probability for Data Science.
  • Have a good scholastic ability
  • Have the capacity for self-directed learning as determined by an interview

Retention

Graduation