industry & research
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Businesses wanting to get ahead of
competitors must produce services and
products that improve on what’s currently
available. An ability to deliver insights
and solutions by analysing data will give
businesses a competitive edge.
Marketing professionals, for example,
will need to sharpen their skills to monitor
the effectiveness of digital marketing
and other campaigns. In the logistics
sector, technological innovation is
creating significant opportunities to
improve operations across functions,
such as demand forecasting, inventory
management and supply chain visualisation.
UPWARD PRESSURE ON SALARIES
Trends in Data Science in Australia ,
forecasts that the Australian data science
workforce will rise from 301,000 persons
in 2016–17 to 339,000 in 2021–22.
This represents an annual average
growth rate of 2.4 per cent, significantly
higher than the 1.5 per cent a year growth
rate that is forecast for the Australian labour
force overall in the same period.
A broad range of industries such as
finance, health and medicine, defence,
logistics, marketing and agriculture are
beginning to rely on analytics to enhance
their core activities and product offerings.
Large technology companies such as
Google, Facebook, Netflix and Amazon
are also incorporating data analytics and
machine learning techniques into their
core offerings.
Data farming has been in the news
recently, with Facebook embroiled in
controversy due to its association with
data analysis firm Cambridge Analytica.
Nevertheless, given the potential benefits,
it is inevitable that legitimate forms of
data mining will become more important
than ever.
The shortage of qualified professionals is
already pushing up salaries, with some top-
level data scientists in Australia commanding
salaries of more than $200,000, according
to research by jobs website Indeed.com.au.
Indeed reveals that the annual average
salary for data scientists in Australia is
$111,911. This shows that data scientists
command a premium compared to
other IT professionals. Salaries for web
developers, for instance, average $78,917,
while systems engineers earn $96,480.
The average income of data scientists
also compares favourably with the average
of $93,995 earned by solicitors and the
$70,048 earned by accountants.
Indeed’s figures on data scientist incomes
are based on 2174 salaries submitted
anonymously to it by data scientist
employees and users and collected from
past and present job advertisements on
Indeed in the past 36 months.
POSTGRADUATE STUDY IMPACT
What is even more striking is the positive
impact that postgraduate study has on
salary levels.
Deloitte Access Economics predicts
that data scientists who have completed
postgraduate study in information technology
will have an average income of $130,176 in
2021–22, up from $111,634 in 2016–17.
The Future of Work report says
that workers who have completed a
postgraduate qualification in information
technology earn a lifetime wage premium
of 51 per cent compared with workers in
the field with no post-school qualifications.
This premium is directly attributable
to their postgraduate qualifications,
which underpin their increased skills and
productivity. In particular, acquiring data
science skills through further study enables
workers who are already qualified in their
current industry to perform their existing
roles more efficiently and to take on
expanded responsibilities.
Further study in the data science area
can also build core technical competencies
for individuals employed in other fields,
enabling them to pivot towards data-related
roles and develop a greater understanding
of the strategic and business applications of
data analytics.
Postgraduate education can deliver other
benefits as well. Since individuals who
succeed in completing their education can
be seen as more capable overall, achieving a
higher degree provides a way for individuals
to “signal” this capability to employers.
Computer programming skills will remain
fundamental to the data science area, to
ensure individuals build familiarity with
computer languages such as R, Python,
SQL, SAS and MATLAB.
At the same time, there is a need to
develop an understanding of the whole
life cycle of data, including acquisition,
management and pre-processing, as well
as mathematical and statistical analysis,
visualisation, reporting and decision-making.
Learning modules in key areas such
as computer programming, statistical
analysis, machine learning and information
management enable individuals to develop
the expertise they will need in a career
in data science across a range of sectors
and applications.
Businesses are also increasingly
demanding technical specialists who are
skilled in business translation. This is the
ability to understand an organisation’s
strategy and functions and to ensure that
data-driven insights can support these
broader strategies.
Australian universities are responding to
the need for data science skills by la