网络安全学士

In Network Security and Infrastructure Protection, students will be introduced to principles of network operational protocols and network security concerns in relation to vulnerabilities and threats. Students will explore and use contemporary network security tools and to detect vulnerabilities and assess their impacts on digital infrastructures. In addition, students will analyse the benefits of network protection with a focus on next generation firewalls and advanced access management, evaluate network performance and present the integration of network security solutions into a digital infrastructure.

Pre-requisite: MIS102 Data and Networking

Assessment Tasks: Report, Presentation

Delivery Mode: On Campus, Online

In this subject, students will explore how to reduce vulnerabilities in software programs to protect applications and ensure systems security and data integrity. This subject will deliver essential topics, such as developing resilient software programs and sustained software risk management to protect users and data against cybersecurity attacks. Upon successful completion of this subject, students will have the skills and knowledge needed to develop secure software solutions that incorporate advanced software security, data protection, and incident management according to secure software governance standards.

Pre-requisite: ISE102 Introduction to Software Engineering

Assessment Tasks: Report, Presentation

Delivery Mode: On Campus, Online

Contemporary cybersecurity requires proactive and offensive security approaches to protect business systems. This subject provides the opportunity for students to examine and understand the principles of offensive security paradigms and the impact on business computer systems. Students will explore and apply ethical hacking and penetration testing practices for vulnerability testing in order to propose security recommendations.

Pre-requisite: CBS131 Cybersecurity Principles

Assessment Tasks: Report and presentation

Delivery Mode: On Campus, Online

Ethical practice, teamwork and professional communication skills are the key concepts that students will explore and apply in IT Professional Practice. Industry focused learning activities and assessment tasks allow students to examine authentic case studies to identify compliance issues that compromise the safeguarding of IT governance. Working with peers, students will practice interpersonal and problem-solving skills as they collaboratively design and develop an IT solution. The learning journey concludes with students presenting their final solution, demonstrating the ability to professionally articulate their ideas to an audience.

Assessment Tasks: Report, Journal, Presentation

Delivery Mode: On Campus, Online

This subject provides students with introductory knowledge of data, life cycle of data science, ecosystem of data science, data scientist tools and relevant industry scenarios. In addition to students being introduced to the ideas behind turning data into information, they will also be introduced to the data scientist's toolbox that includes basic statistical knowledge and fundamentals of Python programming language. Key topics include data scientist skills and responsibilities in a business setting such as planning, performing and presenting insights, data manipulation tools and techniques, and data science codes of ethics.

Assessment Tasks: Essay, report, programming project

Delivery Mode: On Campus, Online

This subject provides students with an opportunity to work collaboratively on a series of projects, enhancing skills in a project-based environment such as time management, prioritisation, resilience and working within a team of people across multiple specialisations. Additionally, students will be challenged to find creative solutions to product development and small-scale rapid prototypes in the context of software engineering. Students will engage in peer learning through agile development and processes. This learning experience provides an opportunity to enhance self-development and promote continuous learning.

Assessment Tasks: Prototype project, Presentation, Report

Delivery Mode: On Campus, Online

This subject introduces students to the concepts, principles, and techniques of machine learning, such as supervised and unsupervised methods, learning theory, reinforcement learning and model performance improvement. Applications such as robotics, data mining, computer vision, bioinformatics and natural language processing as well as the risks and limitations of machine learning will also be explored.

Pre-requisite: CLR204 Classification and Regression

Assessment Tasks: Presentation, source code, report

Delivery Mode: On Campus, Online

In Cloud Architecture, students will explore and examine the interwoven elements of cloud computing architecture that comprises hardware, software, and networking. Building upon their database and networking design skills, students will apply their knowledge on the design, construction, and testing of cloud architecture, with explicit consideration of relevant governance, cybersecurity, and ICT service requirements. Through collaborative project based learning, students will construct and present schematics and simulated prototypes to communicate IT solutions which are scalable, efficient, dependable and cost effective.

Pre-requisite: NDS203 Network and Database System and ICC104 Introduction to Cloud Computing

Assessment Tasks: Report, prototype

Delivery Mode: On Campus, Online

Human-centric cybersecurity in a smart society introduces students to cybersecurity strategies that address threat landscapes, vulnerability trends and human related risks. Increasing use of industry 4.0 technologies and the emergence of cyber-physical systems mean that cybersecurity is no longer contained to an organisation but becomes ubiquitous to daily life. In this subject there is a strong focus on human factors and intelligent technologies in the development of cybersecurity solutions that protect human users and digital societies. Students explore and evaluate innovative and smart cybersecurity solutions that are designed to safe-guard human activities within digital society to present to stakeholders.

Pre-requisite: NIS244 Network Security and Infrastructure Protection

Assessment Tasks: Report, prototype

Delivery Mode: On Campus, Online

Planning, designing, securing and managing enterprise computing systems against cybersecurity attacks are the core themes that students will explore as their knowledge of cybersecurity continues to evolve. Security technologies and tools will be examined and assessed with consideration of cybersecurity standards and IT governance frameworks. Collaborating with peers, students will critically analyse and assess potential security risks to an enterprise system, culminating in the development and recommendation of an IT solution to secure its digital infrastructure. These skills will be developed and utilised further as students move on to their WIL major project.

Pre-requisite: EPT232 Ethical Hacking and Penetration Testing

Assessment Tasks: Report

Delivery Mode: On Campus, Online

Students will explore the contemporary tools and technologies utilised in a digital forensic. Interpreting evidence collected from a simulated cyber-crime, students employ critical-thinking and problem-solving skills to develop a hypothesis in consideration of legal obligations. Additionally, students will present the outcome/s of a collaborative investigation with consideration to ethical and professional practice. Acquiring this specialised knowledge will prepare students to rationalise and recommend future security protocols.

Pre-requisite: EPT232 Ethical Hacking and Penetration Testing

Assessment Tasks: Report, presentation

Delivery Mode: On Campus, Online, Intensive

This subject is designed to provide students with the experience in a professional environment in an area related to their specialisation. The aim of providing industry-specific opportunities is to enable students to develop skills to enhance their prospects of gaining meaningful employment and building their career as software engineers in the future. Much of the benefit of work integrated learning comes from observation, practicing under supervision and reflection. Work Integrated Learning is an excellent way to broaden the student's learning environment while they are studying. It allows them to see first-hand how what they are learning in their degree, translates into practice, as well as how 'real world' practice relates to what they are learning at university. This subject is designed to develop work ready skills to boost students' employability while they are studying.

There are two work integrated learning options available to students:

Option 2: Industry Project
This subject requires students to respond to criteria set within the context of an Industry Project. An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation will be explored. Students will also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject is delivered from a cross specialisation perspective and draws on both specialised and common software engineering practices.
Students are required to work both independently or as part of a collaborative team in order to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an Industry Project.

Assessment Tasks: Report, presentation

Delivery Mode: On Campus, Online

Credit: This is a 30-credit point subject

This subject introduces students to fundamental statistical machine learning models for regression and classification. Key topics of classification algorithms and regression algorithms in machine learning are covered including the practical use of both methods, how to evaluate the proposed models, and how to choose between the different available methods.

Main concepts are followed by examples of practical applications of different classification and regression problems to assist with prediction using machine learning algorithms.

Pre-requisite: PST107 Probabilities & Statistics

Assessment Tasks: Report, Source Code, Analysis of Source Code

Delivery Mode: On Campus, Online

This subject builds on the skills and knowledge students acquired from Concepts of Artificial Intelligence (AI). The subject explores different topics of AI (e.g. Expert Systems, Planning and scheduling, Natural Language Processing (NLP) and Speech Recognition, Machine Learning, and Computer Vision) and their current applications. Students investigate case studies focusing on the most commonly used applications of AI and Machine Learning. This subject also covers the AI for Good movement and how AI is being used to address economic and socially relevant problems. 

Pre-requisite: CAI104 Concepts of Artificial Intelligence

Assessment Tasks: Report

Delivery Mode: On Campus, Online

This subject introduces students to the fundamentals of user experience design by introducing research methods to identify and explore user needs in contemporary digital applications. Students will first define the user experience problem and hypothesise on solutions before analysing and addressing audience and content requirements. Students will work through concept development, prototyping, validation and testing phases to create and refine user-centred design solutions for interactive media.

Pre-requisite: DIG103 Interaction Design

Assessment Tasks: Web Based UI Prototype, Report, Video

Delivery Mode: On Campus, Online

In this subject, you will collaboratively take an idea and create it into a potential business opportunity.  You will assess, research, develop and create a proposed opportunity. This opportunity must currently  not be on the market and could be a new product, service, process or operational improvement. By  the end of this subject, you will enhance your skills in creating a venture-ready business plan and a presentation pitch to persuade relevant stakeholders.

Assessment Tasks: Report, Group Venture Plan, Presentation

Delivery Mode: On Campus, Online, Intensive 

This subject introduces students to data mining techniques for both structured and unstructured data. The technologies to analyse moderate-to-large sized datasets, data preparation, handling missing data, modelling, prediction and classification will be explored. Effective visualisation techniques will be applied to communicate complex information based on data analytics.

Assessment Tasks: Report, Source Code, Data Visualisation

Delivery Mode: On Campus, Online

This subject builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on Deep Learning. Deep learning is a subset of machine learning, and machine learning is a subset of Artificial Intelligence (AI). Students are introduced to foundations of neural networks, their applications to sequence modelling, computer vision, generative models and reinforcement learning. They will learn how to model and train deep neural networks to implement a variety of computer vision applications. Students will be presented with practical examples on how to develop applications using Deep Learning. Knowledge in programming and understanding of machine learning concepts is required in this subject.

Pre-requisite: MLP301 Machine Learning Principle

Assessment Tasks: Online Test, Report, Presentation

Delivery Mode: On Campus, Online

Social Enterprise is an exciting theoretically-based subject that is driven by the desire to create positive change through entrepreneurial activities. These activities harness design thinking and problem-solving processes in the realisation of pragmatic, viable project proposals from initiation to client presentation. By providing students with a framework to understand business model generation and the skills to source, evaluate, and measure opportunities through systematic research and competitor analysis, Social Enterprise empowers students to conceptualise, develop and propose new ventures and products that focus primarily upon social change for good. In addition, this subject will help students understand and address the practical challenges of working within this environment; to analyse different entrepreneurial business strategies, to explore diverse funding strategies, as well as incorporate theoretical discussions on major trends and issues in the social economy. Social Enterprise enables students to appreciate the power of creativity in problem-solving and the importance of the designer’s role in making a difference and precipitating change.

Assessment Tasks: Report, Presentation

Delivery Mode: On Campus, Online, Intensive

This subject introduces students to advanced UX applications and methodologies that reflect professional UX employment opportunities. Through a series of design sprints this subject explores the scoping, planning, designing and delivery of a complex feature of a UX design project following user-centred and agile design processes. A range of UX methodologies such as accessibility, information architecture, functionality, user psychology and behaviour, and project management are examined. Students extend their knowledge of research, design, prototyping, and validation methodologies through the development of a single innovative complex feature of a mobile app or web service.

Pre-requisite: UXF200 UX Fundamentals

Assessment Tasks: Report, Interactive Prototypes, Video, Video Presentation

Delivery Mode: On Campus, Online