Public health depends on widespread and rapid access to data to drive decisions. Using the Data Modernization Initiative (DMI), Washington state is modernizing disease surveillance, health information systems, and providing data science training to produce a public health community that leverages timely, relevant, and actionable data.
As part of modernization, the Public Health Data Learning Center offers free training, tools, and resources to support public health professionals through the process of data systems change.
Interested in hearing more about our trainings and events? Sign up for the Data Learning Center Newsletter.
Start Learning
Browse the training categories below to begin building the skills you need for data systems change. Training is provided for beginning and intermediate skills.
| Title | Description | Category | Difficulty | Length | Organization | Type | Link to edit Content |
|---|---|---|---|---|---|---|---|
| Sexual Orientation, Gender Identity and Expression, and Sex Characteristics | This training covers the health issues and disparities affecting sexual and gender minorities and the factors that should be considered when collecting and analyzing data on these populations. |
Justice, Equity, Diversity, and Inclusion | Beginner | 45 minutes | Region III Public Health Training Center | Self-paced online course | |
| How Not to Use Data Like a Racist | This webinar covers how to incorporate cultural sensitivity and equitable practices into data collection and visualization. |
Justice, Equity, Diversity, and Inclusion | Beginner | 2 hours | Resource Center for Minority Data (University of Michigan) | Webinar | |
| Decolonizing Data to Address Missing and Murdered Womxn | This webinar describes the lapses in data use among government agencies contributing to the problem of MMIWG2S, and the strategies used by Indigenous activists to address the problems. |
Justice, Equity, Diversity, and Inclusion | Beginner | 1 hour | Region X Public Health Training Center | Webinar | |
| Centering Racial Equity in Data Use | This training describes how racist structures in the US impact data practices and how the professionals who use data can center racial equity in their practices. |
Justice, Equity, Diversity, and Inclusion | Beginner | 1 hour | Resource Center for Minority Data (University of Michigan) | Webinar | |
| An Anti-racist Imperative for Public Health Data | The purpose of this session is to explain the ways public health data can address or contribute to social inequalities and describe anti-racist ways to approach data collection. |
Justice, Equity, Diversity, and Inclusion | Beginner | 1.5 hour | Region I Public Health Training Center | Webinar | |
| Addressing Rural Health Disparities with Data | This webinar demonstrates how public health professionals can identify, communicate about, and address health inequities in rural communities. |
Justice, Equity, Diversity, and Inclusion | Beginner | 1 hour | Region X Public Health Training Center | Webinar | |
| Addressing Health Equity: A Public Health Essential | This self-paced training illustrates the layers of social conditions associated with health disparities and provides a framework for public health strategies to address these disparities. |
Justice, Equity, Diversity, and Inclusion | Beginner | 1.5 hours | University at Albany, Center for Public Health Continuing Education | Self-paced online course |
Do you have feedback about these training? We’d love to hear from you. Contact us at DataModFeedback@doh.wa.gov or take our survey.
About the Learning Center
Created in collaboration with DOH and the University of Washington School of Public Health, the Public Health Data Learning Center is a learning resource center for professionals who use data to drive decisions in public health.
The learning center provides a variety of training and professional development materials for those engaged in all areas of data systems change.
We are committed to DMI priorities. For more information about DMI in Washington state, contact DataModFeedback@doh.wa.gov.