Course Offerings
Explore designing and implementing information technologies to improve healthcare delivery, healthcare management, and health outcomes. Offered on the letter-grade basis only.
Overview of public health and the information systems used to achieve public health goals. This course is divided into three parts: (1) overview of public health, (2) fundamentals of public health informatics, and (3) public health information systems.
Leveraging medical claims data to guide population health interventions, primarily through the use of machine learning models. The course will focus on the data processing pipeline, and no prerequisite knowledge of machine learning models is required
Explore principles and methodologies in health informatics research, including various approaches to data analysis, research design, and the application of informatics to health. Develop skills in reading, reviewing, and writing scientific publications, identifying research questions, initiating research, and communicating findings.
The course is designed for undergraduate students who are interested in understanding, analyzing, designing, evaluating, or developing technologies to serve the health needs of general consumers. It covers the concept of consumer health informatics, health behavior theories, health information seeking and information retrieval, various forms of consumer health systems, and the design and evaluation of such systems.
New Topic for Spring 2025. Description pending submission by instructor, Steve Hershman. Also offered as Informatics 320D.
In this class, we will explore different strategies for including games in collections across libraries, archives, and museums using case studies of specific institutions. We will also address unique qualities of digital and board games that make them challenging to existing practice in the field. Students will have hands-on experience with games through the semester.
Learning key data wrangling maneuvers in abstract and implementations in SQL, Excel, R Tidyverse, and Python Pandas. Maneuvers in data transformations include Nest, Pivot, Mutate (inc. separate/unite), Group/Summarize and Rectangling. Projects include working with "wild caught" data datasets (usually CSV or JSON) and computational notebook environments (e.g., iPython, Jupyter, Rmarkdown, Quarto). Fall 2024 has changes from previous syllabus now that we have Database Design and Introduction to Programming. Nonetheless, the previous syllabus is still useful as it links to course materials that show the teaching approach and type of assignments. http://howisonlab.github.io/datawrangling/#Schedule_of_classes
Exhibits are a powerful way for libraries, archives, museums, and cultural institutions to engage the public with their collections. This course offers students the opportunity to plan and install an exhibit, focusing on objectives such as: crafting a narrative around physical objects; drafting exhibit text; accommodating media preservation issues; building basic display supports; and publicizing the exhibit. Students will learn about the historical origins of modern-day exhibit practices and will visit and evaluate current exhibits on campus and in the Austin area.