Practical Principles for Data Analysis Design

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.

Statistical Myths and Misuse: Abandoning Outdated Statistical Practices

This talk will focus on best practices for using modern statistics in health sciences.

Causal Inference in R

This 6 week series will cover causal inference model building and evaluation techniques. 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. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

ConTESSA: A Shiny Application to Assist with Evaluating the Impact of COVID-19 test-trace-isolate Programs

This talk will focus on an application, ConTESSA, along with the accompanying R package, tti, designed to help quantify the impact of contact tracing programs. The talk will walk through the technical aspects of the underlying model as well as highlight how R, and in particular shiny, were used to create this product.

Design Thinking: Empirical Evidence for Six Principles of Data Analysis

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.

Causal Inference in R

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.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. 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. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Causal Inference in R

In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. 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. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.

Communicating During a Pandemic: What Worked, What Didn’t and What’s Next

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.

Tips for Statistical Communication and Data Storytelling

Without strong communication skills, all the advanced analysis we have performed might be overrun. At this event, our expert panelists will share tips and advice on how to clearly and effectively communicate statistics, particularly in social media, and answer questions from the audience.