To
give talented Australian Public Service staff an opportunity to develop
advanced data skills, data fellowship offered by Australian Government is now
open for application.
According
to the announcement
made by the Digital Transformation Agency (DTA), 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.
Up
to 10 high-performing data specialists in the Australian Public Service will be
selected to develop a solution for a data-related problem or opportunity.
The
data fellowship is a 3-month full-time placement with no cost to data fellows. Agencies
will continue to pay their salaries, superannuation and entitlements, with all travel
and accommodation costs reimbursed.
Before
their application, applicants should make sure that they have approval from the
Data Champion
or a senior executive of their agencies and are able to start the project with
3 months of the application closing date in March.
Data
Champions are senior officials within Government agencies tasked with promoting
data use, sharing and reuse within their organisations.
Data
fellows will work with Data61 or another partner organisation during their
placement on projects involving data analysis, forecasting or API development.
The DTA and Data61 will consult with the data fellow’s agency on the start
date. With offices in most major cities in Australia as well as some regional
locations, most data fellows will be placed in the Data61 office closest to
their current locations.
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.
Some
of the previous projects that data fellow alumni worked on include:
(1)
Using social
media resources and data on trends such as travel, retail, home and car sales, to
provide a new real-time indicator of household consumption and spending
(2)
Using
health-related data sets to create expenditure models for evidence-based policy
design
(3)
Applying
machine learning techniques to compare different GDP modelling in Australia, so
as to create a new way of predicting economic growth in Australia.
(4)
Applying
machine learning techniques to conduct predictive analysis based on
lightweight feature, such as metadata, to
expanding a real-time file identification system that supports digital
forensics
(5)
Using
vessel and trade data to design an agent-based model of a container terminal
model for data analysis. The model will later scale up to the wider
intermodal supply chain.
(6)
Using machine
learning to streamline how the Australian Industry publication is compiled and
produced. This included improvements to the processing cycle of the Economic
Activity Survey.
(7)
Designing
a microsimulation model to conduct simulation for health risk and assess
hospitalisation risk in chronic disease patients.
(8)
Building
an empirical model to estimate
greenhouse gas emissions and predict the changes of terrestrial soil
carbon. By monitoring soil carbon in Australia’s crop and grasslands, this
model can be used as a validation tool for the official estimates of greenhouse
gas emissions.
(9)
Developing
techniques to detect harmful trading detection techniques using data from the Australian
Securities and Investments Commission. These techniques find patterns of
repeated misconduct and relationships between entities of interest.
(10) Building a process and platform that analyses GPS data from road freight vehicles.
This provided insights into congested areas of the road network, rest patterns
of truck drivers, and changes in road freight activity.
