Gergana Daskalova: "How to teach coding to increase diversity"

Gergana will discuss what they have learned across four years of Coding Club and introduce some of the approaches being taken in the new course to make data science skills accessible to broad audiences, as well as sharing some practical tips for creating online teaching materials. To illustrate those concepts, data visualisation examples will be presented on what Gergana calls the beautification journey.

Gergana is a PhD student in Global Change Ecology at the University of Edinburgh with research interests in biodiversity change, agro-ecology and quantitative syntheses. Gergana has led over 40 workshops on programming and statistics using R, covering themes such as working with big data, data visualisation, version control and hierarchical modelling.

Daniela Castro Camilo: "R-INLA: Bayesian inference for everyone"

Over the last decade, Bayesian inference has become widely used in many scientific fields, mainly due to significant advances in computational statistics. One of several recent developments in Bayesian statistics is the integrated nested Laplace approximation (INLA), efficiently implemented in the R-INLA package, which allows fast and accurate inference in complex models.
Leveraging on R-INLA's fast computation capacities, we will see examples of how to fit well-known regression models, as well as more sophisticated temporal and spatial models. I will provide an extensive list with recent examples of applications using the R-INLA package, that will hopefully give a feeling on INLA's versatility and potential use. R codes to reproduce the example will be available after the talk.

Daniela is a Lecturer in Statistics at the University of Glasgow. Her research focuses on the theory and applications of multivariate and spatial extremes, with a particular interest in environmental, geological, and ecological applications.

Come along to hear our two speakers. Afterwards we'll head to the Howlin' Wolf for post-meetup catchup!