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Applied Data Analytics for Cybersecurity

Build on your advanced knowledge of big data in cybersecurity to focus on threat detection and application of artificial intelligence (AI).

About this microcredential

Applied Data Analytics for Cybersecurity covers big data capabilities with threat intelligence to help detect, analyse and alleviate the insider threats, as well as targeted attacks from external bad actors and persistent cyber threats. It introduces a number of new cybersecurity challenges, such as malware analysis, adversarial machine learning, deep learning-based anomaly detection, privacy preserving data analytics, etc.

This microcredential introduces students to the AI technologies for cybersecurity and to the most common approaches to data analytics. It offers practice in the applied data analytics of cybersecurity, identifying security risks, threats and vulnerabilities to corporate computers and networks.

Key benefits of this microcredential

  • A deep dive into applying AI in cybersecurity – 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 stand-alone program, or combine this with other microcredentials to stack towards future awards study with UTS.

This microcredential aligns with the 2 credit point subject, Applied Data Analytics for Cybersecurity (42899) in the Graduate Certificate in Professional Practice (C11298), Graduate Diploma of Professional Practice (C06136), Master of Professional Practice (C04404), Graduate Certificate in Technology (C11301), Graduate Diploma in Technology (C06137) and Master of Technology (C04406).

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

Who should do this microcredential?

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

UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education. Course admission requisites may apply for future entry into awards courses.       


Full price: $1,595.00 (GST-free)*

*Price subject to change. Please check price at time of purchase. 

Enrolment conditions

COVID-19 response 

Additional course information

Course outline

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

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

Module 1 - Malware analysis

  • This module introduces the concept of malware analysis. Participants will get an overview of static vs dynamic analysis, the four layers of malware analysis and how to manage computer and mobile-targeted malware.

Module 2 - Adversarial machine learning

  • This module introduces the concept of adversarial machine learning. Participants will get an overview of the types of attacks against machine learning (such as evasion, poisoning and inference attacks) and how these types of attacks are executed.

Module 3 - Deep learning-based anomaly detection

  • This module introduces the concepts of intrusion detection and types of intrusion detection systems. Participants will get an overview of botnets and detection techniques, anomaly detection techniques, backpropagation, deep learning models and generative and discriminative models.

Module 4 - Privacy preserving data analytics

  • This module introduces the topics of privacy issues, data collection and analytics. Participants will get an overview of privacy preserving technology, data anonymisation, attacker models and differential privacy mechanisms, advantages and disadvantages.

The 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.

Course delivery

This microcredential 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 microcredential will allow learners to gauge their progress.              

Course learning objectives

Upon successful completion of this microcredential, participants will be able to synthesise data analytics with other techniques to appropriately set rules for intrusion detection.      


The assessment task comprises an individually written assessment on intrusion detection analysis. It includes reflection on individual practical work on intrusion detection analysis (pre-processing and transformation) for cybersecurity, with artefacts (screen shots/outputs) of the practical work required to be submitted in the report.

Length: 2,500 words      



  • To complete this online course you will need a personal computer with reliable internet access, web conferencing capabilities and an operating system with a web browser compatible with the Canvas LMS. You will also need to be able to run a currently-supported version of Python.
  • This microcredential is accessible to participants with basic mathematical proficiency (linear algebra and statistics) and some programming experience in Python.


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