This is a 2.5-day course for professionals who wish to enhance their data communication skills and learn the fundamentals of data journalism. In focusing on storytelling rather than specific techniques of data analysis, the course is suitable for anyone who needs to communicate with data in their jobs.

Data storytelling offers an entertaining, engaging and interactive approach to data communication and builds on the premise that humans naturally communicate, interpret and understand information in a story-like manner. This entails three fundamental elements: understanding the story behind data, creating compelling visuals to communicate it, and writing it up into a coherent narrative. Participants will leave the course empowered to understand the advantages of data storytelling approach and equipped with the skills to develop a compelling data story to influence colleagues, clients and stakeholders in their work.

This course introduces academics and professional data analysts to Bayesian inference, using the Stan interface in R. The atmosphere of the workshop will be friendly and supportive, with the goal of teaching the basics of Bayesian inference in Stan for academics and professionals alike from diverse backgrounds ranging from industry to research fields such as population health, social sciences, disaster risk reduction, and many more. We will show participants how one can develop and compile Stan scripts for Bayesian inference through RStudio to perform basic parameter estimation, as well as a wide range of regression-based techniques from the simplest univariate linear models to more advanced multivariate spatial risk models. Participants will leave the course with a clear understanding of the Bayesian approach to data analysis and inference, and its applications in a range of fields.

A three-day course focusing on familiarising participants with the fundamentals of survey design and analysis, combined with practical applications using the statistical programme R.

Specifically, we will cover: sampling and weighting methods and how these relate to questions of representativeness; survey questions and response scale design and to what extent a given question and its associated scale may (or may not) be measuring what was intended; different types of response bias, such as social desirability or non-response; effective ways to describe survey data and simple multivariate analysis; and a quick tour of the increasingly popular field of survey experiments.

On each day, the sessions will conclude with some time to implement the concepts discussed in the lecture. Specifically, participants will do a series of practical exercises, whose solutions will be discussed step-by-step together as a class. Participants will be encouraged to work together and to learn in a collaborative environment.

At the end of the three days,participants will have reached a good level of survey literacy, be able to identify well (or ill) designed surveys and gained confidence in their capacity to interpret survey results. The guiding theme of the course will be to learn how to think critically about what information can (and cannot) be learnt from any specific survey data.