Data and decision science initiative

The DDSI facilitates networking of researchers throughout UOW who use data and decision science or research data and decision science methodology. There is substantial overlap in current methodological requirements of data science, handling big data, and increased transparency in reproducible reporting of analysis between disciplines. The value add through the development of this network is the consolidation of resources and knowledge sharing, allowing the ability to improve research performance and industry engagement by the addition of novel and cutting edge data science methodology to grant applications, and upskilling of PhD students, post-doctoral and other research staff in data science methodology. Relevant methodologies include integration of data from multiple sources, analysis of linked data, reproducibility of research, machine learning, causal analysis of large-scale observational data, Bayesian design and decision making, ethics and privacy.

Watch some of our Data and Decision Science Network Presentations

Interactive Data Visualisation - Bradley Wakefield
--Slides, Scripts

The net benefit correspondence theorem - Senior Professor Simon Eckermann

Machine Learning Taster - Professor Alberto Nettel-Aguirre

Which stats package should I use? - Professor Marijka Batterham, Brad Wakefield, Professor Alberto Nettel-Aguirre
--Slides

Transparency and reproducibility for quantitative research: Like the Layers of an Onion - Professor Ben Marwick

Linked Sensitive Data - a Beginners Guide - Dr Felicity Flack

Jamovi, a free interface for using R - Bradley Wakefield
--Slides

The figure outlines newly created teaching and education developments involving data science. These include developments in undergraduate and postgraduate degrees, liaising with the Graduate Research School to develop postgraduate training in Data Science, developed and planned internal and external short courses which can be developed into modules for micro-credentials.