Seeing More In Your Data: New Visual Tools for Business Analytics

Abstract

Recent advances in data visualisation from our research group provide business analysts with a richer and more flexible visual toolkit. This talk presents new approaches for visualising uncertainty, using computer vision to automatically interpret plots, and diagnosing complex machine learning models using high-dimensional visualisation methods. The new work will be illustrated with examples and implications for analysis and interpretation in various settings. A core focus of the group's work is open-source software and reproducible research. I will share the underlying workflows and how we support transparency, collaboration, and practical adoption of our research.

Short bio

Dianne Cook is a Professor of Statistics in Econometrics and Business Statistics at Monash University in Melbourne, Australia. She holds a PhD in Statistics from Rutgers University. Her research focuses on statistical graphics, with an emphasis on interactive visualisation of high-dimensional data and statistical inference for data visualisation. At Monash, she teaches courses in machine learning and data analysis. Di is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and a Board Member of the R Foundation. She is a past editor of the Journal of Computational and Graphical Statistics and The R Journal, and author of numerous R packages.