We are all surrounded by data and the easiest way to understand what it is trying to tell us is to draw a picture that helps summarise them in some way. Often, standard visual techniques (like histograms and bar charts) are used, and used well but sometimes they aren’t. With the advent of new technologies that are now widely accessible to industry and the general public, there have been many attempts at proposing new ways of visualising data. Some of them have been very helpful while others have been less than useful. In this talk I shall present an overview of good, bad, and plain ugly attempts to visualise data in the news and social media and explain what makes them so. There will be zero maths involved, but plenty of pretty (and not so pretty) pictures . . . in colour.

This presentation will briefly describe some of the issues that were investigated during my SSP in S1, 2017, which consisted of visiting various colleagues throughout Europe. Our work was concerned with several aspects of categorical data analysis, which was primarily focused on the visualisation of categorical data through correspondence analysis. Speaking of visualisation, I will also provide a pictorial overview of my visit throughout Europe.

Space! For Star Trek fans it’s the final frontier - with all of vastly, hugely, mindboggling room it contains, it allows scientists and researchers of all persuasions to go where no one has gone before and explore worlds not yet explored. Like Star Trek fans, many mathematicians and statisticians are also interested in exploring the dynamics of space. From a statisticians point of view, our often data driven perspective means we are concerned with exploring data that exists in multi-dimensional space and trying to visualise it using as few dimensions as possible.

This presentation will outline the links between the analysis of categorical data, multi-dimensional space, and the reduction of this space. The technique we explore is correspondence analysis and we shall see how eigen- and singular value decomposition fit into this data visualisation technique. We shall therefore look at some of the fundamental aspects of correspondence analysis and the various ways in which categorical data can be visualised.

Despite not being a hot topic of discussion amongst Australian (and many other English speaking) statisticians, correspondence analysis has gained an international reputation for being a versatile and intuitive approach for graphically summarising the association between categorical variables. Its popularity is especially evident in Europe, and also amongst researchers in the ecological and marketing research disciplines. Over the past decade or so there has been increasing attention paid to the methodological development of correspondence analysis and its application in Australia. This talk will describe one very biased perspective to this development - mine. In particular I will pay special attention to developments that have been published, and have been in development, since 2012. I will also provide some insight into future developments.