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. 

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.

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.

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.