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Data Science Technology and Systems PG (11523.1)

Level: Postgraduate Level
Credit Points: 3
HECS Bands:

Band 2 2013-2020 (Expires 31 Dec 2020) Band 2 2021 (Commenced After 1 Jan 2021) Band 3 2021 (Commenced Before 1 Jan 2021)

Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


Possible changes to your unit's learning activities and assessment items

As a result of the Australian Government's and or the ACT Government’s directives requiring physical distancing and restrictions on movement because of the COVID-19 pandemic, you may find that learning activities and/or assessment items in some units you are studying have changed. These changes will not be updated in the published Unit Outline but will be communicated to you via your unit’s UCLearn(Canvas) teaching site. The new learning activities and/or assessment items will continue to meet the unit's learning outcomes, as described in the Unit Outline.

New learning activities and/or assessment items are available on your unit's UCLearn(Canvas) teaching site. Please contact your Unit Convener with any questions.


Data science is rapidly developing into a major component of businesses and organisations in the 21st century. Part of this rapid development is an increasing number of advanced data science technologies and systems. This unit provides a hands-on learning experience of selected, up-to-date data science tools for students who are seeking to work at the advanced technological level. Topics include but are not limited to the intersection of data science and cloud computing such as Amazon Web Services and IBM Watson Studio, Apache Hadoop and similar ecosystems, Google Cloud Compute, TensorFlow architectures, working with GPU systems and data warehouses, advanced machine learning / artificial intelligence tools such as PyTorch and others. This unit is aimed at an advanced level and will be taught using real-world scenarios.

Learning Outcomes

After successful completion of this unit, students will be able to:

1. Demonstrate advanced knowledge and skills in data science technologies and systems and their ethical use;

2. Comfortably work with cloud storage and computing tools;

3. Critically reflect on new developments of data science technologies and systems; and

4. Show a sound understanding and practical skills in working with advanced machine learning and artificial intelligence tools to analyse, model and predict complex data problems.

Contact Hours

Four hours of problem-based learning activities, interactive workshops and practical work in laboratory classes on campus per week.


11521 Programming for Data Science G and must have passed 24 credit points of any postgraduate IT course.



Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis. Sound understanding and practical skills in programming.

Incompatible Units


Equivalent Units


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