Following is a working list of resources for graduate students in the School of Public Health who aim to develop data science skills to enhance their primary course of study. Such study might include developing “data acumen” in relation to public health data, data management, analyzing big data using modern computational tools, data communication, and data ethics as they relate to public health research.
Do you know of additional UW data science courses or resources that may be of interest to public health graduate students? Please send your suggestions to email@example.com.
Summer Institutes (Biostatistics)
Each summer the Department of Biostatistics hosts graduate-level short courses that teach the latest in statistical methods, techniques, and analyses. The Summer Institutes are independent from the standard UW curriculum and result in a certificate of completion rather than UW credit. It is not required to be registered as a UW student to register for the Summer Institutes.
Some institutes offer scholarships to cover registration costs. Early registration rates end in mid-June.
The Summer Institutes include:
- Summer Institute in Statistics for Clinical & Epidemiological Research (SISCER)
- Summer Institute in Statistics for Big Data (SISBID)
- Summer Institute in Statistical Genetics (SISG)
- Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID)
UW Professional & Continuing Education
UW Professional & Continuing Education offers fee-based programs in data science:
- Certificate in Data Science (3 courses over 3 quarters, evenings)
- Master of Science in Data Science (2 years)
Courses on data science are offered across a variety of UW departments, both within and outside of the School of Public Health. Graduate students may elect to register for data science courses relevant to their individual research interests.
Here are some sample courses across several areas of data science relevant to public health.
Data Science Literacy and Reproducible Research for the Health Sciences
- BIOST 544 Introduction to Biomedical Data Science (3-4 credits)
- BIOST 509 Introduction to R for Data Analysis in the Health Sciences (2 credits)
- CHEM 583 / CSE 583 Software Development for Data Scientists (4 credits)
Data Management and Data Acumen
- EPI 560 Data Management for Public Health (3 credits)
- HSERV 523 Advanced Health Services Research Methods I: Large Public Databases; Big Data (4-5 credits)
Statistics, Inference, and Machine Learning
- BIOST 527 Nonparametric Regression and Classification (3 credits)
- BIOST 546 Machine Learning for Biomedical and Public Health Big Data (3 credits)
- CSE/STAT 416 Introduction to Machine Learning (4 credits)
- CSE 546 Machine Learning (4 credits)
Data Visualization and Communication
- HCDE 511 Information Visualization (4 credits)
- CSE 512 Data Visualization (4 credits)
Data Ethics and Ethical Data Science in Public Health Research
- BIOST 532 Research Ethics in the Data Sciences (2 credits)
- GH 532/EPI 586 Responsible Conduct of Research: Global and Local (3 credits)
Other Data Science Courses
- CHEM E 599 – Current Topics in Chemical Engineering: “Topics in Data Science” (1 credit)* This is a topics course that varies in topic – check that topic offered is “Topics in Data Science”