The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a product is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between producers of data analyses. This will hopefully provide guidance for future work in characterizing the data analytic process.

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

The Wake Forest Conference on Analytics Impact is focused on the impactful use of analytics to solve problems in business, non-profits, government agencies and society. During the pandemic, government officials and healthcare professionals have more so than ever before, had to communicate to the public using healthcare data. How to communicate these data statistically and visually to influence people’s behavior has proven very challenging. What have we learned about communicating with data during this crisis? What did we get right and what failed? This year’s Conference on Analytics Impact is focused on communicating with health care data and lessons learned from the pandemic.

Whether a medical student reading their first journal article or a healthcare professional trying to use the latest research to improve patient care, this course will help you understand data and statistics in the medical literature in an efficient and conceptual manner.

Shinydashboards. This course teaches how to use the shinydashboard R package. By the end you should be able to build a simple dynamic dashboard!

This course aims to provide managers and developers of contact tracing programs guidance on the most important indicators of performance of a contact tracing program, and a tool that can be used to project the likely impact of improvements in specific indicators.

Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationship between the confounders and the outcome are sufficiently controlled, or whether there is a more complex relationship that requires further adjustment. This short commentary highlights this simple tool, providing an example of its utility along with relevant R code.

The data revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a solution is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow.