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 - Dr Bradley Wakefield
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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, Dr Brad Wakefield, Professor Alberto Nettel-Aguirre
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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 - Dr Bradley Wakefield
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Should I use R? - Dr Brad Wakefield, Professor Marijka Batterham
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Teaching Programming & Modelling to First-Year Undergraduates: Tips and Lessons Learned - Associate Professor Jenny Fisher
Causal Diagrams: A tool to better understand results from epidemiological and machine learning analyses - Associate Professor Ian Shrier
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