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Introduction to Data Science G (11516.1)

Level: Graduate 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


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Unit Outlines

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  • Semester 1, 2021, ONLINE, BRUCE (203812) - View
  • Semester 1, 2021, ON-CAMPUS, BRUCE (200472) - View
  • Semester 2, 2020, ONLINE, BRUCE-ONL (203370) - View
  • Semester 2, 2020, ONLINE, BRUCE (203340) - View
  • Semester 2, 2020, ON-CAMPUS, BRUCE (196045) - View
  • Semester 1, 2020, ON-CAMPUS, BRUCE (192329) - View
  • Semester 2, 2019, ON-CAMPUS, BRUCE (192446) - View

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This unit provides a graduate level introduction to the rapidly developing field of data science, which deals with the dramatic growth in the scale and complexity of data that can be collected and analysed. Data and its analysis and modelling underpins all aspects of work and society in the 21st century. Understanding effective and ethical ways of using vast amounts of data (big data) is a significant challenge to science and to society. Applying and developing techniques for data analysis and decision making requires interdisciplinary research in many areas, including algorithmic reasoning, statistics, technology competence, data handling, big data analysis / modelling, pattern recognition / artificial intelligence, enterprise / cloud computing, data visualisation / communication, as well as privacy and security. This unit will introduce you to the core competencies of data science, starting from the philosophy and ethics of data collection and analysis, to data handling, to statistical modelling and machine learning, to scientific data visualisation and communication. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

This unit will be co-taught with 11372 Introduction to Data Science.

Learning Outcomes

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

1. Demonstrate a solid understanding of the principles of data science;

2. Demonstrate practical knowledge in data preparation;

3. Use appropriate modelling and analyse techniques for data science problems;

4. Demonstrate competent skills in using data science technology; and

5. Communicate results effectively.

Assessment Items

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Contact Hours

Two hours of lectures, one hour of tutorials and one hour of computer laboratories on campus per week.





Assumed Knowledge

Working knowledge of discrete mathematics, algebra and numerical analysis.

Incompatible Units

11372 Introduction to Data Science

Equivalent Units


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