About us

Students learning in the Science facilities at the UNSW Kensington campus

The UNSW Data Science Hub (uDASH) was formed with a single purpose: to bring together UNSW’s full spectrum of data specialists to solve complex, real-world challenges–your challenges. We translate the data generated by business, government and industry into a story, and from that story, we create a solution.

In today’s world, we are inundated by large amounts information that is often meaningless without a central body through which it can be curated and interpreted. The team at uDASH comprises over 90 data scientists from across UNSW psychology, medicine, physics, law, mathematics, education, business, marketing and economics. uDASH exists to see meaning and patterns where others see bits and bytes of information.

When you collaborate with us, you will be matched to a team of uDASH scientists who will work with you to develop a scope of research and work to help you achieve the desired outcomes and insights from your data. These data could be experimental findings, survey outcomes (and design), user behaviour or even the irregular distribution of trees. We can undertake deep analyses with our suite of tools, algorithms and visualisations, and apply powerful proprietary models and simulations. We ask the questions to reveal the bigger picture, so you can see the forest beyond the trees.

Our team

Our themes

As a broad, but by no means exhaustive grouping, the research of uDASH data science can be divided into six primary themes:

Fundamental data science is concerned with developing the foundations of new data science theory, methods, and tools. These will typically have their roots in Mathematical, Statistical or Machine Learning.

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Environmental data science is concerned with generating new data-driven insights into environmental problems at all scales from the microbial to the globe. New complex and large datasets now exist from earth observation platforms, from spatial and temporal climate, and from biodiversity observations including (meta-)genomics. Using these datasets in informative ways requires an emerging set of complex methods. This theme facilitates the connection of method experts with environmental experts to address these environmental problems.

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Health data science is concerned with generating data-driven solutions to complex real-world health problems (in a broad definition) through the comprehension of complex big data and development/employment of customised analytical, statistical and machine learning approaches. The theme addresses both fundamental and translational research questions in a wide range of areas including health, medicine, pharmacology, psychology, and biology.

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Physical data science is concerned with working with data science methods, and tools to open new scientific opportunities across the Schools of Physics, Chemistry and Material Science & Engineering.

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Business data science uses the latest methods and tools to convert data into insights for business and economic decision making. For almost any type of business there are unprecedented opportunities to learn from increasingly detailed, real-time and complete data to help inform decisions in finance, accounting, marketing, operations, human resources and strategy.

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Data Visualisation uses modern digital technologies to reinvent scientific discovery. We connect the capabilities and experience of researchers from across the fields of simulation, modelling, human-computer interaction, computer graphics, artificial intelligence and visual analytics into frameworks with a focus on responsible tech outcomes. 

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Data4Good aims to use the power of data to help drive systemic change for the good of society. We help organisations understand and make the best use of their data for maximum impact and to drive change. We are particularly passionate about Education, Climate Change, Gender Equity and Policy and Justice.

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