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Advanced Data Analytics for Cybersecurity

Designed as an introduction to machine learning in the field of cybersecurity, this course focuses on machine learning technologies and their potential vulnerabilities – along with cybersecurity tools and solutions.

About this course

This microcredential combines big data capabilities with threat intelligence to help detect, analyse and alleviate insider threats and targeted attacks from external bad actors and persistent cyber threats. It covers key topics including statistical methods for identifying patterns in data and making inferences and other intelligent technologies that derive cybersecurity issues from data.

Advanced Data Analytics for Cybersecurity introduces participants to machine learning technologies for cybersecurity and the most common approach to standard process for data analytics. This course offers practice in advanced technologies of data analytics in cybersecurity, identifying security risks, threats, and vulnerabilities to corporate computers and networks.      

Course outline

More information on course content

In this course, you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.

The course is structured into four modules. Each module comprises self-study materials and facilitated online sessions. The four modules and key topics covered are:

Module 1 - Supervised machine learning for cybersecurity I

This module introduces machine leaning, deep learning and an overview of supervised machine learning algorithms from a cybersecurity perspective.

Module 2 - Supervised machine learning for cybersecurity II

This module presents an in-depth case study of attacks on supervised machine learning technologies, in order to explore their potential vulnerabilities to cyber risks.

Module 3 - Unsupervised machine learning for cybersecurity I

This module provides an overview of unsupervised machine learning algorithms, including reinforcement learning and federated learning, from a cybersecurity perspective.

Module 4 - Unsupervised machine learning for cybersecurity II

This module presents an in-depth case study of attacks on unsupervised machine learning technologies, to explore their potential vulnerabilities to cyber risks.

This course is delivered in a scheduled format over ten weeks.

Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.

Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.

Key benefits of a microcredential

  • An end-to-end introduction to cybersecurity in machine learning – build on your understanding of data analytics from a cyber perspective.
  • Develop your understanding of key technologies and concepts along with hands-on activities and case studies to explore real algorithms, attacks and threats.
  • Complete as a self-contained course, or as a potential pathway to future postgraduate study.

This microcredential aligns with the two-credit point subject, Data Analytics in Cyber Security (411180) in one of the following postgraduate offerings:

  • Graduate Certificate of Professional Practice (C11298)
  • Graduate Diploma of Professional Practice (C06136)
  • Master of Professional Practice (C04404)
  • Graduate Certificate of Technology (C11301)
  • Graduate Diploma of Technology (C06137)
  • Master of Technology (C04406).

This microcredential may qualify for recognition of prior learning at this and other institutions.

Who is this course for?

This microcredential is accessible to professionals from a wide range of sectors and backgrounds who have completed our Data Analytics for Cybersecurity Foundations microcredential, or who otherwise have foundational professional experience in the field.

UTS microcredentials are developed for professionals with the capacity to undertake postgraduate tertiary education.

Course information

Course learning objectives

Upon successful completion of this course, you will be able to detect and analyse cyber-attacks using data analytics.


The graded assessment task is an individually written assessment on spam data analysis. This assignment includes reflection on individual practical work on spam data analysis (pre-processing and transformation) for cybersecurity, with artefacts (screenshots/outputs) of the practical work to be submitted in the report.

Length: 2,500 words      

Teaching and learning strategies

This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic, supporting self-study and online learning activities.

Case studies of real-world applications illustrate applications of key tools and techniques. The workshop sessions focus on hands-on activities and examples. Regular formative quizzes throughout the course will allow participants to gauge their progress.

Enrolment requirements

This course is accessible to participants with basic mathematical proficiency (linear algebra and statistics) and some programming experience in Python.

Participant requirements and equipment

To complete this online course, you will need a personal computer with reliable internet access, web conferencing capability and an operating system with a web browser compatible with the Canvas LMS. You will need to be able to run a currently-supported version of Python.



05 April




10 wks


5 hrs p/w

Lead Academic

Tianqing Zhu

Tianqing Zhu

Tianqing is an experienced lecturer in cybersecurity, with an extensive background in teaching and research in privacy preserving, cybersecurity and data analytics.

Tianqing’s research interests include designing novel privacy preserving models, developing efficient algorithms and performing in-depth analytics on a wide spectrum of very large, real-world data sets.

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Book a session

Mon 05 Apr 2021 -
Fri 11 Jun 2021
Expert: Tianqing Zhu
  • 5 April - 11 June 2021, 9am-5pm
  • Online
Mon 07 Jun 2021 -
Fri 13 Aug 2021
Expert: Tianqing Zhu
  • 7 June - 13 August 2021, 9am-5pm
  • Online
Mon 02 Aug 2021 -
Fri 08 Oct 2021
Expert: Tianqing Zhu
  • 2 August - 8 October 2021, 9am-5pm
  • Online
Mon 04 Oct 2021 -
Fri 10 Dec 2021
Expert: Tianqing Zhu
  • 4 October - 10 December 2021, 9am-5pm
  • Online
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