The course is an introduction to the basic concepts and techniques of Geographic Information Systems (GIS). This course contains lectures and hand-on exercises in ArcGIS. The lectures introduce the basic concepts of GIS, data sources, data structure, data management, map projections and coordinate system, making maps, and spatial analysis. Students will also learn how to use the fundamental knowledge of GIS to solve real world problems. Through the hand-on exercises, students will get familiar with the interfaces and analysis tools in the ESRI software package ArcGIS. Students will also practice applying GIS as a tool and a methodological approach to spatially analyze environment and public health data.

This course is designed to provide students with a general understanding of programming and analytic concepts in R to address challenges that a public health data analyst with a master’s degree might encounter.  The focus of the course is not comprehensive knowledge of R, but rather a basic foundation of knowledge that can be expanded on after the course concludes. The interactive classes will feature a lecture component and a laboratory component. The RStudio environment will be the interface used for all classroom discussion.

Multilevel studies and multilevel analysis have been increasingly used in the public health field. This course will discuss the rationale for multilevel studies and multilevel analysis in public health as well as differences with other study designs and other analytical approaches. Although the course will not be heavily mathematical, the basics of fitting multilevel models for different types of outcomes as well as the interpretation of estimates obtained from multilevel models will be reviewed and practiced. Emphasis will be on conceptual understanding, application and interpretation of multilevel analysis in the context of urban health research. The course will also review and critique empirical applications in urban health research and discuss conceptual and methodological challenges in using multilevel analysis.

As data is collected from more and more sources and across life areas, the applications for using big data for public health purposes are growing. This class will focus on the use of large data sets, specifically claims data and other national survey sources, and provide an overview of structuring data, selecting optimal analytic approaches, and display results in compelling data visualization formats.

Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

This course will provide the landscape of policy analysis in real-world settings using a continuum of data tool approaches. Since policy development and implementation occurs in diverse and fast-paced environments, approaches to studying research and policy questions that keep pace with policymakers will be described. Data approaches from individual case studies to large, publicly available data sets will be reviewed and used. Policy products (e.g., policy briefs, infographics) that resonate with policymakers and constituents will be the key deliverables generated and provide scaffolding for future policy analysis and dissemination.

Improving the quality of data visualizations can help in communicating results more clearly and transparently to other scientists, stakeholders and the general public. This course will use a workshop format to help students build their own data visualization projects using R, with Urban Health examples. This course will include a general introduction to basic data visualization concepts and best practices to generate static figures. This course will also cover basic concepts regarding interactive data visualization and spatial data. This course will provide all the required code, and some of the code may be useful to apply to your own visualizations with some adjustments. Thus, participants with a stronger R background will be able to create more complex visualizations. 

Please note this course takes place over three days (Friday 1:30-5:00pm, Saturday and Sunday 9:00am-4:30pm). This course is designed for researchers who are interested in learning what it means to approach health from a complex systems perspective – exploring why this is needed, what it looks like, and how to leverage complex systems science methods in their own work. Participants will be introduced to many complex systems science methods without focusing in detail on a specific method. The course will help researchers appreciate which tools to use, when, why and how, including whom to partner with and where to learn more.

Please note this course takes place over three days (Friday 1:30-5:00pm, Saturday and Sunday 9:00am-4:30pm). This course will provide students with the foundational knowledge and skills necessary to complete their own analyses of data from the National Health and Nutrition Examination Survey (NHANES). NHANES is a continuous annual survey designed to assess the health and nutritional status of adults and children in the United States. The course is comprised of didactic presentations and a hands-on lab. The course will review the survey design and sampling; survey components (i.e., questionnaires, dietary data, laboratory data, and examination data); measurement; and navigation of data documentation.  During the laboratory component, students will conduct an analysis of NHANES data that covers the following: 1) data acquisition and management (e.g., downloading the data; merging data from multiple data files), 2) combining data from multiple years, 3) use of weights (i.e., which weights to apply, creating weights for multiple survey cycles), 4) use of data from multiple survey components (e.g., combining questionnaire data with lab data), and 5) descriptive and multivariable analyses. Note that the purpose of the class is to provide hands-on, practical experience in conducting analyses of NHANES data. While the course will include a series of scripted labs, there will also be an opportunity for you to conduct an analysis of your own. The course will be taught in Stata, but students with proficiency in other statistical packages can work in other packages.