Exploratory data analysis has significant potential within the analysis of complex, heterogeneous data where analysts are seeking observations that they can answer questions with and can action on within their application domain. As an open-ended process, exploratory analysis sessions can lead to several unexpected findings but with potential for high numbers of false positives and with limited guidance on when to conclude the analysis. This project aims to provide mechanisms and heuristics to increase the likelihood of success and the efficiency of exploratory analysis processes and investigates answers to questions such as, “When to know that the results are stable and actionable?”, “When does a data set stop being useful during exploratory data analysis session?”, “How to action on findings from an exploratory analysis process?” to name a few.
Review literature within visual analytics and exploratory data analysis for data analysis strategies
Develop a theoretical framework to inform and guide users on the progress of an exploratory analysis process towards robust, stable and actionable findings
Design, develop, and evaluate interactive visualisations to support the above
Experience in designing and implement interactive visual representations
Good understanding of statistics, exploratory analysis, and related data-science techniques
Interest in user-centred design processes