In Australia, Digital Transformation Agency
announced the second round of data fellowship for 2017 – 2018. Seven
data specialists were selected to take part in three-month placements under
As reported earlier, the data fellowship
programme by the Australian Government targets current employees of the
Australian Public Service who are interested in building data skills within
their agencies. The selected high-performing data specialists will go through a
three-month full-time placement to develop a solution for a data-related
problem and help to improve government services.
The fellowships give public service staff
the chance to step outside their day jobs and create prototypes that improve or
completely redesign our approach to real-life problems, while their respective
agencies will continue to pay their salaries, superannuation and entitlements,
with all travel and accommodation costs reimbursed.
Participants come from a variety of
government agencies. These fellowships give them access to mentoring, skills
development and new and emerging technologies and techniques. The following are
the seven projects that the data fellows will work on from now to mid-October:
Using text analytics to check
financial condition reports at the Australian Prudential
Regulation Authority. Text analytics will be used to help identify
insights and risks in prudential supervisory reviews of insurance companies’
financial condition reports.
Developing techniques to automate
land-use delineation through advanced geospatial data-analysis and modelling
techniques. These techniques will combine satellite-derived information and
various national datasets to help Australian Bureau of Agricultural
and Resource Economics and Sciences improve methods for building the
Land Use of Australia data series for understanding current and long-term
changes in Australian land use.
Analysing government buying
patterns and provide data about the way government agencies buy products and
services and the sellers they buy from. Using longitudinal network analysis to
look at datasets held by AusTender, the analysis will show the effects policy
changes have on forming networks between government agencies and sellers.
Using machine learning to help
Energy Regulator (CER) detect non-compliance in regulatory schemes.
The effort aims to develop a process for the Small-Scale Renewable Energy
Scheme to help the CER use its resources more efficiently and strengthen the
integrity of Australia’s Renewable Energy Target.
Developing an empirical model
to identify and target biosecurity risks at Australian airports. The model will
help to find risk patterns and identify international travellers who carry a
higher and potential non-compliance biosecurity risk.
Using machine-learning algorithms
to improve survey accuracy for the Australian Bureau of Statistics. The
models will improve the quality and efficiency of the address register held by
Simulation model for
call-centre activities that will replicate the day-to-day telephone activities
of the Department of Human Services. The project aims to show information
including customer wait times, number of transferred calls, busy signals, level
of staff occupancy and more.
Other than training on advanced data
skills, data fellow will also be part of a bigger alumni network after
completing the placement to share learnings and experience.