Public Health Informatics Courses

 

INFO 521 (3) Database Development for Public Health: Fall. This course will cover the principles utilized in data management and database development for purposes of Public Health. This is primarily a skills-based course - the students will learn to create a relational database using Microsoft Access 2013, as well as gaining an understanding of the important terminology, standards and data management principles utilized by data management teams. Sample Syllabus

INFO 530 (2) Geographic Information Systems: Fall and Spring. Introduces the use of geographic information systems (GISs) in the analysis of public health data. Addresses basic GIS operations such as buffering, layering, and spatial queries, and develops GIS skills through homework and case studies. Addresses introductory cartography and basic statistical aspects of spatial analysis. Sample Syllabus

INFO 532 (4) Geographic Information Systems for Public Health: Fall and Spring. The course introduces the use of geographic information systems (GISs) in the analysis of public health data. We develop GIS skills through homework and case studies and particularly address basic GIS operations such as buffering, layering, and spatial queries as well as more advanced GIS capabilities such as geodatabases. In addition to GIS issues, we address introductory cartography and basic statistical aspects of spatial analysis. Sample Syllabus

INFO 550 (2) Data Science Toolkit: Fall. Prerequisites: BIOS 544 or BIOS 545, R programming experience needed, or permission of the instructor. This course is an elective for Masters and PhD students interested in learning some fundamental tools used in modern data science. Together, the tools covered in the course will provide the ability to develop fully reproducible pipelines for data analysis, from data processing and cleaning to analysis to result tables and summaries. By the end of the course, students will have learned the tools necessary to: develop reproducible workflows collaboratively (using version control based on Git/GitHub), execute these workflows on a local computer (using command line operations, RMarkdown, and GNU Makefiles), execute the workflows in a containerized environment allowing end-to-end reproducibility (using Docker), and execute the workflow in a cloud environment (using Amazon Web Services EC2 and S3 services). Along the way, we will cover a few other tools for data science including best coding practices, basic python, software unit testing, and continuous integration services. Sample Syllabus

INFO 560R (VC) Current Topics in Public Health Informatics:  Fall and Spring. A faculty member offers a new course on a current topic of interest to both masters and doctoral students.

INFO 597R (VC) Directed Study: Fall and Spring. Provides an in-depth exposure to specific topics not covered in regular courses, for example, statistical genetics and specialized experimental designs.