Curriculum

The required BHI core coursework reflects the importance and interaction of our practice domains ranging from human biology to clinical medicine and consumer health to public health. Students complement their core coursework with additional courses in statistics and advanced research methods, as well as electives drawn from other UW academic units. Departmental seminars are also offered that present new and integrative research in the field.

Please see the MS coursework requirements and PhD coursework requirements for full details.   Below is a more informal listing of the classes, including some example electives:

BHI Program Core Coursework Requirements: All courses listed below are to be numerically graded, with the exception of Research Seminars, and the student must pass all these with individual grades of at least 2.7. Courses count for 3-4 credits (in parentheses), for a total of 34 credits.

  • BIME 530   Introduction to Biomedical & Health Informatics (3)
  • BIME 533   Public Health and Informatics (3)
  • BIME 534   Biology and Informatics (3)
  • BIME 535   Clinical Care and Informatics (3)
  • BIME 537   Informatics Research and Evaluation Methods (4)
  • BIME 539   Teaching, Learning and Communication in BHI (4)  (not required for PhD Qualifying Exam) 
  • BIME 550   Knowledge Representation and Applications (3)
  • BIME 554   Biomedical Information Interactions and Design (4)
  • BIME 543   Consumer Health and Informatics (3)*
  • BIOST 517 Applied Biostatistics I (or BIOST 537 Survival Data Analysis in Epidemiology or other equivalent biostatistics graduate course with approval) (4)**

*  effective for Autumn 2015 cohort and later
** effective for Autumn 2014 cohort and later

See our course description page for more detailed information about each course. The above nine courses are required for all MS and PhD students. (In addition, all postdoctoral fellows must take four of the BIME courses; please see postdoc coursework requirements.) BHI core courses may be completed during one’s first year, as shown in the table below. Each course is offered only once a year in the quarters indicated. MS Students are not required to take BHI core courses all in their first year+, but can take core courses with more flexibility in order to incorporate electives, rotations, and research.

Fall Quarter – First Year 

  • BIME 530 Introduction to Biomedical & Health Informatics
  • BIOST 517 Applied Biostatistics I
  • BIME 543 Consumer Health and Informatics

Winter Quarter – First Year

  • BIME550 Knowledge Representation and Applications
  • BIME533 Public Health and Informatics
  • BIME537 Informatics Research and Evaluation Methods

Spring Quarter – First Year 

  • BIME554 Biomedical Information Interactions and Design
  • BIME534 Biology and Informatics
  • BIME535 Clinical Care and Informatics

Fall Quarter – Second Year

  • BIME539 Teaching, Learning and Communication in BHI  (not required for PhD Qualifying Exam) 

Graduate students must also take a number of  research seminars and electives. The electives below are just examples, and there are many other courses that may be appropriate electives for BHI students.

BHI Research Seminars:

  • BIME 590 Selected Topics in Biomedical and Health Informatics
  • BIME 591 Biomedical and Health Informatics Research Colloquium

Example electives outside BHI:

ASTR 598 Topics in Theoretical Astrophysics
BIOST 509 Introduction to R for Data Analysis in the Health Sciences
BIOST 544 Introduction to Biomedical Data Science
BIOST 546 Machine Learning for Biomedical and Public Health Big Data
CHEM E 599D Data Science Seminar/Topics in Data Science
CHEM E 599F eScience Community Seminar/Big Data Seminar Course
CSE 442 Data Visualization
CSE 512 Data Visualization
CSE 517 Natural Language Processing
CSE 527 Computational Biology
CSE 544 Principles of Database Systems
CSE 546 Machine Learning
CSE 583 Software Development for Data Scientists (4)
DATA 515A Software Development for Data Science
EPI 555 Statistical Methods For Spatial Epidemiology
HIHIM 405 Introduction to Health Data Analytics
HSERV 521 Advanced Qualitative Methods in Anthropology and Public Health
HSERV 527 Intro to Qualitative Methods
HSERV 584 Assessing Outcomes in Health and Medicine
HSMGT 514 Health Economics
HSMGT 552 Health Administration and Business Law
INFX 565 Designing Information Experience
INSC 518 Seminar in Human Information Interaction
INSC 541 HCI Design Foundations for Interactive Systems
INSC 543 Value Sensitive Design
LING 570 Shallow Processing Techniques for Natural Language Processing
LING 571 Deep Processing Techniques for Natural Language Processing
LING 572 Advanced Statistical Methods in Natural Language Processing
STAT 509 Econometrics I: Introduction to Mathematical Statistics
STAT 512-513 Statistical Inference
STAT 535 Statistical Learning: Modeling, Prediction, and Computing
VALUES 512 Justice Matters