Unit of study descriptions

Master of Information Technology / Master of Information Technology Management

Candidates for the degree of Master of Information Technology/ Master of Information Technology Management are required to complete 96 credit points from the units of study listed in the tables below as follows:
1. a total of 96 credit points
2. a minimum of 24 credit points of the Core units of study
3. a minimum of 24 credit points of the Information Technology Specialist units of study
4. a minimum of 24 credit points of the Information Technology Management Specialist units of study
The completion of a major is an optional requirement of this degree. A major requires the completion of all core units of study and at least 18 credit points chosen from the Information Technology Specialist units of study listed in the table for the defined major, as below.
5. A maximum of 12 credit points may be selected from units outside of the School of Computer Science, with the approval of the Program Director.
For the Professional Pathway, after completing 24 credit points of coursework, in addition to the requirements listed above, select:
1. a minimum of 12 credit points of the Professional Pathway Project units of study,
2. a maximum of 12 credit points of the Foundation units of study.
3. no credit points from the research pathway units of study.
Students may gain admission to the Research Pathway after completing 24 credit points of coursework with a Distinction average or above, subject to the approval of the Program Director. For the Research Pathway, in addition to the requirements listed above, select:
1. a minimum of 24 credit points of the Research Pathway units of study,
2. no credit points from the Professional Pathway Project units of study.

Core units

Candidates for the Master of Information Technology/Master of Information Technology Management must complete a minimum of 24 credit points from the listed Core units of study.
COMP5206 Information Technologies and Systems

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assessment: Through semester assessment (50%) Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will provide a comprehensive introduction to the field of information systems from organisational and managerial perspectives. The emergence of the digital firm and its implications will be studied. The critical role of information and knowledge management will be emphasised from both conceptual and practical standpoints.
Key topics covered will include: Basic Information Systems Concepts; Systems Approach and Systems Thinking; E-Business and E-Commerce; IT Strategy and Competitive Advantage; Data and Knowledge Management; Information Systems Development and IS Management; Decision support systems, business intelligence and online analytical processing systems (OLAP); Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, Enterprise Content Management and Supply Chain Management (SCM) systems; Ethical, Legal and Social Aspects of Information technologies.
INFO5990 Professional Practice in IT

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: Students enrolled in INFO5990 are assumed to have previously completed a Bachelors degree in some area of IT, or have completed a Graduate Diploma in some area of IT, or have many years experience as a practising IT professional. Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: The main focus of the subject is to provide students with the necessary tools, basic skills, experience and adequate knowledge so they develop an awareness and an understanding of the responsibilities and issues associated with professional conduct and practice in the information technology sector. This unit is for MIT, MITM, MIT/MITM students only.
This Unit of Study introduces the students to some of the concepts, standards and techniques associated with the current professional practice in information technology in the business environment.
Students will encounter a range of concepts, techniques and professional issues including interpersonal and organisational communication, human resources and conflict resolution, globalisation, professional ethics, social impacts of IT, data security, data quality assurance, system audit, investigative research and project management practice. Practical and real world case studies will be used as part of the learning to enhance the in-class teachings to the needs of industry.
INFO5992 Understanding IT Innovations

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: PMGT5875 Assumed knowledge: INFO5990 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
An essential skill for an IT manager is the ability to keep up-to-date with emerging technologies, and be able to evaluate the significance of these technologies to their organisation's business activities. This unit of study is based around a study of current technologies and the influence of these technologies on business strategies.
Important trends in innovation in IT are identified and their implications for innovation management explored. Major topics include: drivers of innovation; the trend to open information ("open source") rather than protected intellectual property; and distribution of innovation over many independent but collaborating actors.
On completion of this unit, students will be able to identify and analyse an emerging technology and write a detailed evaluation of the impact of this technology on existing business practices.
INFO6007 Project Management in IT

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Workshops, Assignments, Exam Preparation Prohibitions: PMGT5871 Assumed knowledge: Students enrolled in INFO6007 are assumed to have previously completed a Bachelors degree in some area of IT, or have completed a Graduate Diploma in some area of IT, or have three years experience as a practising IT professional. Recent work experience, or recent postgraduate education, in software project management, software process improvement, or software quality assurance is an advantage. Assessment: Through semester assessment (60%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study covers the key components of successfully managing a wide variety of Information Technology projects. The course covers both quantitative and qualitative aspects of project management. Topics include the management of time, scope, budget, risk, quality, and resources through each of the phases of a project.

Information Technology Specialist Units

Candidates for the Master of Information Technology/Master of Information Technology Management must complete a minimum of 24 credit points from the listed Information Technology Specialist units of study.
CISS6022 Cybersecurity

Credit points: 6 Session: Semester 1 Classes: 1x2hr seminar/week Assessment: 1x2hr exam (40%), 1x3000wd analytical Essay (40%), 1x1000wd equivalent lab exercise (10%), 1xSeminar participation (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
The digital revolution has created new frontiers of information that influence almost every aspect of our lives. But does cyberspace also threaten our security? What are the methods and motives for attack? And how can state and non-state actors respond? Drawing on a unique combination of expertise from the Centre for International Security Studies and the School of Information Technologies, this unit introduces students to the technical and political concepts that are necessary to answer these important questions.
COMP5045 Computational Geometry

Credit points: 6 Session: Semester 1 Classes: Project Work Assumed knowledge: Students are assumed to have a basic knowledge of the design and analysis of algorithms and data structures: you should be familiar with big-O notations and simple algorithmic techniques like sorting, binary search, and balanced search trees. Assessment: Through semester assessment (72%) and Final Exam (28%) Mode of delivery: Normal (lecture/lab/tutorial) day
In many areas of computer science- robotics, computer graphics, virtual reality, and geographic information systems are some examples- it is necessary to store, analyse, and create or manipulate spatial data. This course deals with the algorithmic aspects of these tasks: we study techniques and concepts needed for the design and analysis of geometric algorithms and data structures. Each technique and concept will be illustrated on the basis of a problem arising in one of the application areas mentioned above.
COMP5046 Natural Language Processing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Knowledge of an OO programming language Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP).
Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing.
Students will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. Students will annotate data as part of completing a real-world NLP task.
COMP5047 Pervasive Computing

Credit points: 6 Session: Semester 2 Classes: Studio class Assumed knowledge: ELEC1601 AND (COMP2129 OR COMP2017). Background in programming and operating systems that is sufficient for the student to independently learn new programming tools from standard online technical materials. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This is an advanced course on Pervasive Computing, with a focus on the "Internet of Things" (IoT). It introduces the key aspects of the IoT and explores these in terms of the new research towards creating user interfaces that disappear into the environment and are available pervasively, for example in homes, workplaces, cars and carried.
COMP5048 Visual Analytics

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have basic knowledge of data structures, algorithms and programming skills. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.
COMP5216 Mobile Computing

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: COMP5214 OR COMP9103. Software Development in JAVA, or similar introductory software development units. Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Mobile computing is becoming a main stream for many IT applications, due to the availability of more and more powerful and affordable mobile devices with rich sensors such as cameras and GPS, which have already significantly changed many aspects in business, education, social network, health care, and entertainment in our daily life. Therefore it has been critical for students to be equipped with sufficient knowledge of such new computing platform and necessary skills. The unit aims to provide an in-depth overview of existing and emerging mobile computing techniques and applications, the eco-system of the mobile computing platforms, and its key building components. The unit will also train students with hand-on experiences in developing mobile applications in a broad range of areas.
COMP5313 Large Scale Networks

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: Algorithmic skills (as expected from any IT graduate). Basic probability knowledge. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
The growing connected-ness of modern society translates into simplifying global communication and accelerating spread of news, information and epidemics. The focus of this unit is on the key concepts to address the challenges induced by the recent scale shift of complex networks. In particular, the course will present how scalable solutions exploiting graph theory, sociology and probability tackle the problems of communicating (routing, diffusing, aggregating) in dynamic and social networks.
COMP5318 Machine Learning and Data Mining

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.
COMP5328 Advanced Machine Learning

Credit points: 6 Session: Semester 2 Classes: Lectures, tutorials Assumed knowledge: COMP5318 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Machine learning models explain and generalise data. This course introduces some fundamental machine learning concepts, learning problems and algorithms to provide understanding and simple answers to many questions arising from data explanation and generalisation. For example, why do different machine learning models work? How to further improve them? How to adapt them to different purposes?
The fundamental concepts, learning problems and algorithms are carefully selected. Many of them are closely related to practical questions of the day, such as transfer learning, learning with label noise and multi-view learning.
COMP5329 Deep Learning

Credit points: 6 Session: Semester 1 Classes: Tutorials, Lectures Assumed knowledge: COMP5318 Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.
COMP5338 Advanced Data Models

Credit points: 6 Session: Semester 2 Classes: Tutorials, Lectures Assumed knowledge: This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1). Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study gives a comprehensive overview of post-relational data models and of latest developments in data storage technology.
Particular emphasis is put on spatial, temporal, and NoSQL data storage. This unit extensively covers the advanced features of SQL:2003, as well as a few dominant NoSQL storage technologies. Besides in lectures, the advanced topics will be also studied with prescribed readings of database research publications.
COMP5347 Web Application Development

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory, Project Work Prerequisites: INFO1103 or INFO1113 or COMP9103 or COMP9220 or COMP5028 Assumed knowledge: COMP9220 or COMP5028. The course assumes basic knowledge on OO design and proficiency in a programming language Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Nowadays most client facing enterprise applications are running on web or at least with a web interface. The design and implementation of a web application require totally different set of skills to those are required for traditional desktop applications. All web applications are of client/ server architecture. Requests sent to a web application are expected to go through the public Internet, which slows the responsiveness and increases the possible security threat. A typical web application is also expected to handle large number of requests coming from every corner of the Internet and sent by all sorts of client systems. This further complicates the design of such system.
This course aims at providing both conceptual understanding and hand-on experiences for the technologies used in building web applications. We will examine how data/messages are communicated between client and server; how to improve the responsiveness using rich client technology; as well as how to build a secure web application.
At the end of this course, students are expected to have a clear understanding of the structure and technologies of web applications. Students are also expected to have practical knowledge of some major web application environments and to be able to develop and deploy simple web applications. Cloud based platform are increasingly popular as the development and deployment platform. This course will incorporate the cloud aspect of web application development as well.
COMP5348 Enterprise Scale Software Architecture

This unit of study is not available in 2019

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Programming competence in Java or similar OO language. Capacity to master novel technologies (especially to program against novel APIs) using manuals, tutorial examples, etc. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics on software architecture for large-scale enterprises. Computer systems for large-scale enterprises handle critical business processes, interact with computer systems of other organisations, and have to be highly reliable, available and scalable. This class of systems are built up from several application components, incorporating existing "legacy" code and data stores as well as linking these through middleware technologies, such as distributed transaction processing, remote objects, message-queuing, publish-subscribe, and clustering. The choice of middleware can decide whether the system achieves essential non- functional requirements such as performance and availability. The objective of this unit of study is to educate students for their later professional career and it covers Software Architecture topics of the ACM/IEEE Software Engineering curriculum. Objective: The objective of this unit of study is to educate students for their later professional career and it covers topics of the ACM/IEEE Software Engineering curriculum.
COMP5349 Cloud Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The course will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5405 Digital Media Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Prohibitions: COMP5114 OR COMP9419 Assessment: through semester assessment (50%) and final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Digital media data such as audio, image, videos, graphics, and 3D are increasingly becoming indispensable for big data driven computing applications in many domains, such as social media, public security, education, commerce, entertainment, and healthcare. This unit aims to bring students the essential knowledge on digital media, various computing techniques and tools on digital media processing and analysis, and many cutting-edge digital media applications such as VR/AR and Internet of Things (IoT) enabled new media. It will help students build practical computing skills for digital media driven applications and utilise learned knowledge to produce creative and media rich solutions to real world problems.
COMP5415 Multimedia Design and Authoring

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides principles and practicalities of creating interactive and effective multimedia products. It gives an overview of the complete spectrum of different media platforms and current authoring techniques used in multimedia production. Coverage includes the following key topics: enabling multimedia technologies; multimedia design issues; interactive 2D and 3D computer animation; multimedia object modelling and rendering; multimedia scripting programming; post-production and delivery of multimedia applications.
COMP5416 Advanced Network Technologies

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assumed knowledge: ELEC3506 OR ELEC9506 OR ELEC5740 OR COMP5116 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit introduces networking concepts beyond the best effort service of the core TCP/IP protocol suite. Understanding of the fundamental issues in building an integrated multi-service network for global Internet services, taking into account service objectives, application characteristics and needs and network mechanisms will be discussed. Enables students to understand the core issues and be aware of proposed solutions so they can actively follow and participate in the development of the Internet beyond the basic bit transport service.
COMP5424 Information Technology in Biomedicine

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Information technology (IT) has significantly contributed to the research and practice of medicine, biology and health care. The IT field is growing enormously in scope with biomedicine taking a lead role in utilising the evolving applications to its best advantage. The goal of this unit of study is to provide students with the necessary knowledge to understand the information technology in biomedicine. The major emphasis will be on the principles associated with biomedical digital imaging systems and related biomedicine data processing, analysis, visualisation, registration, modelling, retrieval and management. A broad range of practical integrated clinical applications will be also elaborated.
COMP5425 Multimedia Retrieval

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: COMP9007 or COMP5211. Basic Programming skills and data structure knowledge. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The explosive growth of multimedia data, including text, audio, images and video has imposed unprecedented challenges for search engines to meet various information needs of users. This unit provides students with the necessary and updated knowledge of this field in the context of big data, from the information retrieval basics of a search engine, to many advanced techniques towards next generation search engines, such as content based image and video retrieval, large scale visual information retrieval, and social media.
COMP5426 Parallel and Distributed Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is intended to introduce and motivate the study of high performance computer systems. The student will be presented with the foundational concepts pertaining to the different types and classes of high performance computers. The student will be exposed to the description of the technological context of current high performance computer systems. Students will gain skills in evaluating, experimenting with, and optimising the performance of high performance computers. The unit also provides students with the ability to undertake more advanced topics and courses on high performance computing.
COMP5427 Usability Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Usability engineering is the systematic process of designing and evaluating user interfaces so that they are usable. This means that people can readily learn to use them efficiently, can later remember how to use them and find it pleasant to use them. The wide use of computers in many aspects of people's lives means that usability engineering is of the utmost importance.
There is a substantial body of knowledge about how to elicit usability requirements, identify the tasks that a system needs to support, design interfaces and then evaluate them. This makes for systematic ways to go about the creation and evaluation of interfaces to be usable for the target users, where this may include people with special needs. The field is extremely dynamic with the fast emergence of new ways to interact, ranging from conventional WIMP interfaces, to touch and gesture interaction, and involving mobile, portable, embedded and desktop computers.
This unit will enable students to learn the fundamental concepts, methods and techniques of usability engineering. Students will practice these in small classroom activities. They will then draw them together to complete a major usability evaluation assignment in which they will design the usability testing process, recruit participants, conduct the evaluation study, analyse these and report the results
COMP5617 Empirical Security Analysis and Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Project Work - own time Assumed knowledge: Students are expected to have: Good programming skills in Go, Python, or C. UNIX/Linux command-line and tools Technical orientation and foundational networking knowledge Sufficient mathematical skills to understand cryptography Experience working with version control Assessment: through semester assessment (40%) and final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will present the lessons from recent research and from case studies of practice to bring students the skills to assess and improve the security of deployed systems. A particular focus is on data-driven approaches to collect operational data about a system's security. We explore deployment issues at local and global scale, e. g. for X. 509, DNS, and BGP, and also take human factors explicitly into account. As a result, students will learn to put building blocks of security together in a sound way, to arrive at engineering solutions that are empirically verifiable, functional, and secure against realistic threats. As Dan Geer once famously said: "Any security technology whose effectiveness can't be empirically determined is indistinguishable from blind luck."
COMP5618 Applied Cybersecurity

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project work Assumed knowledge: (ELEC5616 OR INFO2315 OR INFO2222) with a grade of Credit or greater Assessment: through semester assessment (60%) and final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Digital technologies permeate every part of our lives. The internet has created a more open society, allowing us to create, share and access information and knowledge freely. As more of the services we rely on are digitised and available to use over the web, the more our identity, productivity, access to information, connectivity, social connections and financial well-being depends on information security. Consequently, a deep understanding of both offensive and defensive security techniques is fast becoming essential knowledge for a career in computing.
This course will provide in-depth knowledge of offensive security that will prepare the student for work in any technical field where they will are responsible for the development or maintenance of sensitive systems. The course begins by introducing the basic tools used by hackers, before highlighting the common weaknesses- and mitigations- for various levels of the technology stack, such as web applications, operating systems and corporate networks. Finally, students are provided practical insights into careers in information security in the areas of attack detection, prevention and defence. Students will develop the skills necessary to both gain access to test computers and to defend test networks from attack.
ELEC5509 Mobile Networks

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: ELEC3505 AND ELEC3506. Basically, students need to know the concepts of data communications and mobile communications, which could be gained in one the following units of study: ELEC3505 Communications, ELEC3506 Data Communications and the Internet, or similar units. If you are not sure, please contact the instructor. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study serves as an introduction to communications network research. The unit relies on a solid understanding of data communications and mobile networks. It introduces some of the currently most debated research topics in mobile networking and presents an overview of different technical solutions. Students are expected to critically evaluate these solutions in their context and produce an objective analysis of the advantages/disadvantages of the different research proposals. The general areas covered are wireless Internet, mobility management, quality of service in mobile and IP networks, ad hoc networks, and cellular network architectures.
The following topics are covered. Introduction to wireless and mobile Internet. Wireless cellular data networks. Cellular mobile networks. Mobile networks of the future. Quality of service in a mobile environment. Traffic modelling for wireless Internet. Traffic management for wireless Internet. Mobility management in mobile networks. Transport protocols for mobile networks. Internet protocols for mobile networks.
ELEC5514 Networked Embedded Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories Prerequisites: ELEC5509 Assumed knowledge: ELEC3305 AND ELEC3506 AND ELEC3607 AND ELEC5508 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aim to teach the fundamentals concepts associated with: Networked Embedded Systems, wireless sensor networks; Wireless channel propagation and radio power consumption; Wireless networks, ZigBee, Bluetooth, etc. ; Sensor principle, data fusion, source detection and identification; Multiple source detection, multiple access communications; Network topology, routing, network information theory; Distributed source channel coding for sensor networks; Power-aware and energy-aware communication protocols; Distributed embedded systems problems such as time synchronization and node localisation; Exposure to several recently developed solutions to address problems in wireless sensor networks and ubiquitous computing giving them a well-rounded view of the state-of the-art in the networked embedded systems field.
Student involvement with projects will expose them to the usage of simulators and/or programming some types of networked embedded systems platforms.
Ability to identify the main issues and trade-offs in networked embedded systems; Understanding of the state-of-the-art solutions in the area; Based on the above understanding, ability to analyse requirements and devise first-order solutions for particular networked embedded systems problems; Familiarisation with a simulator platform and real hardware platforms for network embedded systems through the students involvement in projects.
ELEC5517 Software Defined Networks

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project Work - own time Prerequisites: ELEC3506 OR ELEC9506 Assessment: through semester assessment (60%) and final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study will introduce an emerging networking paradigm- Software Defined Networks (SDNs). By separating the control logics from the physical networks, the software defined networks allow an automated and programmable software program to logically control and manage the network. This unit introduces the basic principles of software defined networks, its architecture, abstraction, SDN programming, programmable control plane and data plane protocols, network update, network virtualisation, traffic management as well as its applications and implementations. Student will learn and practice SDN programming, testing and debugging on SDNs platforms through experiments and group projects. It is assumed that the students have some knowledge on data communications and networks.
ELEC5616 Computer and Network Security

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials, Laboratories, Project Work - own time Assumed knowledge: A programming language, basic maths. Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit examines the basic cryptographic building blocks of security, working through to their applications in authentication, key exchange, secret and public key encryption, digital signatures, protocols and systems. It then considers these applications in the real world, including models for integrity, authentication, electronic cash, viruses, firewalls, electronic voting, risk assessment, secure web browsers and electronic warfare. Practical cryptosystems are analysed with regard to the assumptions with which they were designed, their limitations, failure modes and ultimately why most end up broken.
ELEC5618 Software Quality Engineering

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: Writing programs with multiple functions or methods in multiple files; design of complex data structures and combination in non trivial algorithms; use of an integrated development environment; software version control systems. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will cover software quality planning, validation and verification methods and techniques, risk analysis, software review techniques, software standards and software process improvement and software reliability.
Students who successfully complete this unit will understand the fundamental concepts of software quality engineering and be able to define software quality requirements, assess the quality of a software design, explain specific methods of building software quality, understand software reliability models and metrics, develop a software quality plan, understand quality assurance and control activities and techniques, understand various testing techniques including being able to verify and test a unit of code and comprehend ISO standards, SPICE, CMM and CMMI.
ELEC5619 Object Oriented Application Frameworks

Credit points: 6 Session: Semester 2 Classes: Project Work - in class, Project Work - own time, Presentation, Tutorials Assumed knowledge: Java programming, and some web development experience are essential. Databases strongly recommended Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aims to introduce students to the main issues involved in producing large Internet systems by using and building application frameworks. Frameworks allow great reuse so developers do not have to design and implement applications from scratch, as students have done in ELEC3610 The unit lays down the basic concepts and hands on experience on the design and development of enterprise systems, emphasizing the development of systems using design patterns and application frameworks.
A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. Several development Java frameworks will be used, including Spring, Hibernate, and others. Principles of design patterns will also be studied.
ELEC5620 Model Based Software Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories, Project Work - in class, Project Work - own time Assumed knowledge: A programming language, basic maths. Assessment: Through semester assessment (80%) and Final Exam (20%) Mode of delivery: Normal (lecture/lab/tutorial) day
Model-Based Software Engineering focuses on modern software engineering methods, technologies, and processes used in professional development projects. It covers both the pragmatic engineering elements and the underlying theory of the model-based approach to the analysis, design, implementation, and maintenance of complex software-intensive systems.
Students will participate in a group project, which will entail developing and/or evolving a software system, following a full development cycle from requirements specification through to implementation and testing using up-to-date industrial development tools and processes. At the end of the course they will provide a presentation and demonstration of their project work to the class. There is no formal teaching of a programming language in this unit, although students will be expected to demonstrate through their project work their general software engineering and architectural skills as well as their mastery of model-based methods and technologies.
Students successfully completing this unit will have a strong practical and theoretical understanding of the modern software development cycle as applied in industrial settings. In particular, they will be familiar with the latest model-based software engineering approaches necessary for successfully dealing with today's highly complex and challenging software systems.
The pedagogic grounds for this course and its focus on model-based approaches are to arm new software engineers with skills and perspectives that extend beyond the level of basic programming. Such skills are essential to success in software development nowadays, and are in great demand but very low supply. The dearth of such expertise is one of the key reasons behind the alarmingly high failure rate of industrial software projects (currently estimated at being greater than 40%). Therefore, this unit complements SQE and strengthens a key area in the program.
ELEC5622 Signals, Software and Health

Credit points: 6 Session: Semester 2 Classes: Project Work - in class, Project Work - own time, Presentation, Tutorials, Laboratories Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aims to introduce students to the main issues involved in producing systems that use sensor data, such as those from physiology and activity tracking, often combined with patients self-reports. As sensing devices become ubiquitous, data processing, storage and visualisation techniques are becoming part of all health systems, both institutionalised and individually driven.
The unit is related to, but distinct, to health informatics- an area that focuses on the the use of computing to deliver cost efficient healthcare and the area of bioinformatics, that explores the role of computing in understanding biology at the cellular level (e. g. genome). This unit focuses on the technical and non-technical problems of developing increasingly ubiquitous devices and systems that can be used for personal and clinical monitoring.
HSBH5003 e-Health for Health Professionals

Credit points: 6 Teacher/Coordinator: Professor Tim Shaw, Anna Janssen Session: Semester 1 Classes: online and 3x3-hrs face to face workshops Assessment: eHealth Evaluation (40%), eHealth Innovation Challenge (40%), eHealth reflection task (10%), participation (10%) Mode of delivery: Distance education/intensive on campus
The aim of this unit is to provide future health professionals with a strong foundation in e-Health on which they can make evidence-based decisions. In particular, this unit will provide students with opportunities to examine:
. How technology affects health care in different Australian health contexts
. Ethical issues surrounding e-Health
. Innovations in e-Health
. How emerging technologies affect patient-centred communication between health professionals, and health professionals and their clients/patients
. Strategies for interacting with patients and clients using different technologies
. Strategies for engaging in multi-disciplinary e-Healthcare delivery
. The relationship between technologies, data and the wider information network
Students will develop their skills in a variety of technologies identified as key e-Health skills for clinicians. Students will create an e-Health delivery portfolio to showcase these skills. This unit will also enable students to be lifelong learners by providing them with reflective learning skills. Reflective learning skills are identified as essential for lifelong learning.
IDEA9106 Design Thinking

Credit points: 6 Teacher/Coordinator: Dr Naseem Ahmadpour Session: Semester 1,Semester 2 Classes: Lecture 1 hr/wk, tutorial 2 hrs/wk Assessment: Design assignments (90%), Quizzes (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides an overview of a human-centred approach to the design of products and systems. It introduces students to design thinking and how it can be productively applied to different design situations. The theoretical concepts, methods and tools for the key stages of interaction design are covered including user research, ideation, prototyping and user evaluation. It provides students with the principles, processes and tools for working collaboratively on design projects in studio. Students learn to build empathy with users, identify and reframe the problem space, develop value-driven design concepts and persuasively communicate design proposals with an emphasis on the user experience through visual storytelling. This unit is a foundational core unit in the Master of Interaction Design and Electronic Arts program.
INFO5010 IT Advanced Topic A

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit will cover some topic of active and cutting-edge research within IT; the content of this unit may be varied depending on special opportunities such as a distinguished researcher visiting the University.
INFO5011 IT Advanced Topic B

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit will cover some topic of active and cutting-edge research within IT; the content of this unit may be varied depending on special opportunities such as a distinguished researcher visiting the University.
INFO5060 Data Analytics and Business Intelligence

Credit points: 6 Session: Summer Main Classes: Lectures, Tutorials, Laboratories, Presentation, Project Work - own time Assumed knowledge: The unit is expected to be taken after introductory courses or related units such as COMP5206 Information Technologies and Systems Assessment: Through semester assessment (65%) and Final Exam (35%) Mode of delivery: Block mode
The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.
INFO5301 Information Security Management

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: This unit of study assumes foundational knowledge of Information systems management. Two year IT industry exposure and a breadth of IT experience will be preferable. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study gives a broad view of the management aspects of information security. We emphasise corporate governance for information security, organisational structures within which information security is managed, risk assessment, and control structures. Planning for security, and regulatory issues, are also addressed.
INFO5306 Enterprise Healthcare Information Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories Assumed knowledge: The unit is expected to be taken after introductory courses in related units such as COMP5206 Information Technologies and Systems (or COMP5138/COMP9120 Database Management Systems). Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Healthcare systems intimately coupled to ICT have been at the forefront of many of the medical advances in modern society in the past decade. As is already the case in many other service-driven sectors, it is widely recognised that a key approach to solve some of the healthcare challenges is to harness and further ICT innovations. This unit is designed to help fill a massive technology talent gap where one of the biggest IT challenges in history is in the technology transformation of healthcare.
The unit will consist of weekly lectures, a set of group discussions (tutorials) and practical lab sessions. The contents will offer students the opportunity to develop IT knowledge and skills related to all aspects of Enterprise Healthcare Information Systems.
Key Topics covered include: Health Information System e. g. , Picture Archiving and Communication Systems (PACS) and Radiology IS; Electronic Health Records / Personal Health Records; Health data management; Healthcare Transactions; Health Statistics and Research; Decision Support Systems including Image-based systems; Cost Assessments and Ethics / Privacy; TeleHealth / eHealth; Cases studies with Australian Hospitals.
Guest lecturers from the healthcare industry will be invited. The core of student's assessments will be based on individual research reports (topics related to the current industry IT needs), software / practical assignment and quizzes.
PUBH5010 Epidemiology Methods and Uses

Credit points: 6 Teacher/Coordinator: Dr Erin Mathieu, Professor Tim Driscoll Session: Semester 1 Classes: 1x 1hr lecture and 1x 2hr tutorial per week for 13 weeks - face to face or their equivalent online Prohibitions: BSTA5011 or CEPI5100 Assessment: 1x 6 page assignment (25%), 10 weekly quizzes (5% in total) and 1x 2.5hr supervised open-book exam (70%). For distance students, it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issue. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. In addition to formal classes or their on-line equivalent,it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.
Textbooks
Webb, PW. Bain, CJ. and Pirozzo, SL. Essential Epidemiology: An Introduction for Students and Health Professionals Second Edition: Cambridge University Press 2017.
STAT5003 Computational Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: STAT5002 Assessment: Assignments (40%), quizzes (20%); 2-hour final examination (40%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolment
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Textbooks
(1) An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, (2014), Springer;

Information Technology Management Specialist units

Candidates for the Master of Information Technology/Master of Information Technology Management must complete a minimum of 24 credit points from the listed Information Technology Management Specialist units of study.
INFO5060 Data Analytics and Business Intelligence

Credit points: 6 Session: Summer Main Classes: Lectures, Tutorials, Laboratories, Presentation, Project Work - own time Assumed knowledge: The unit is expected to be taken after introductory courses or related units such as COMP5206 Information Technologies and Systems Assessment: Through semester assessment (65%) and Final Exam (35%) Mode of delivery: Block mode
The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.
INFO5301 Information Security Management

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: This unit of study assumes foundational knowledge of Information systems management. Two year IT industry exposure and a breadth of IT experience will be preferable. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study gives a broad view of the management aspects of information security. We emphasise corporate governance for information security, organisational structures within which information security is managed, risk assessment, and control structures. Planning for security, and regulatory issues, are also addressed.
INFO5991 Services Science Management and Engineering

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Seminars Assumed knowledge: INFO5990. Students are expected to have a degree in computer science, engineering, information technology, information systems or business. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
The service economy plays a dominant and growing role in economic growth and employment in most parts of the world. Increasingly, the improved productivity and competitive performance of firms and nations in services relies on innovative and effective design, engineering, and management of IT-centric services.
This unit offers IT graduates and professionals an understanding of the role of IT-centric services in a social, economic and business context, as well as knowledge of the principles of their design, engineering and management in a service-oriented IT framework. Delivery of the unit is driven by a critical approach to the literature, live case studies presented by industry professionals and writing a Consultants' Report. Its learning outcomes are based on industry needs. Three modules address the range of topics in Services Science, Management and Engineering (SSME).
1. Service fundamentals context and strategy: the service economy and the nature of service systems; the role IT-centric services in a social, economic and business context; IT-centric services optimisation and innovation.
2. Designing and Engineering IT-centric services: service design; service oriented enterprise and IT architecture.
3. Sourcing, governing, and managing IT-centric services: outsourcing IT-centric services (including services in the cloud); IT-centric services governance and management (COBIT and ITIL; service level agreements.
Critical analysis of articles and the persuasive use of evidence in writing are cornerstones of the unit. Students learn how to apply these skills in business consulting processes to a business case drawn from a recent consulting project at a large multinational organisation. The processes include:clarifying the client's situation and problems, researching evidence related to it, analysing the evidence, developing options for solving the problems, presenting recommendations persuasively to the client both orally and in a written Consultants' Report. These steps are scaffolded for the student, with formative assessment, and increasing levels of difficulty.
Students need to be able to read, critically analyse, and report on an article or case study every three weeks. If you are not confident of your skills in these areas, you can enroll in the free courses provided by the University's Learning Centre in Academic Reading and Writing and Oral Communication Skills. Some of these courses are specifically designed for students with a non-English speaking background. Familiarity with using Library reference tools and the ability to locate scholarly resources in the Library's electronic databases is also necessary. See the Library's Research and information skills page for help with this http://www.library.usyd.edu.au/skills/
INFO6010 Advanced Topics in IT Project Management

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials (applied workshop), E-Learning Prerequisites: INFO6007, OR 3-5 years working experience in IT Project Management Assumed knowledge: Students are assumed to understand the role of IT projects. Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will explore the limitations of IT project management and the most promising techniques to overcome project failure. It will start by reviewing case study research showing we have reached the limits of traditional IT project management practice. The theoretical base will be completed by exploring the finding that senior management have more impact on success than traditional approaches.
Participants will be introduced to and learn to apply the most promising tools and techniques needed to govern IT projects. The topics reviewed will include: 1) Strategy; 2) Organisational change; 3) Project sponsorship; 4) Programme management; 5) Performance measurement; 6) Culture; 7) Portfolio management; 8) Relevant Australian and International Standards on IT/Project Governance and new industry methodologies around portfolio, programme and change management will be reviewed.
INFO6012 Information Technology Strategy and Value

Credit points: 6 Session: Semester 1 Classes: Flexible Sessions Prerequisites: COMP5206 Assumed knowledge: COMP5206 Assessment: Through semester assessment (55%) and Final Exam (45%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
The increasingly strategic role of IT in organisations is widely recognised. This unit of study is designed to provide a comprehensive introduction to strategic aspects of IT as they impact on business value. Such a perspective is critical for IT professionals in both IT producer and user organisations from the level of Chief Information Officer to managers as well as technical specialists. Deep understanding of IT strategy formulation and implementation and ensuring its alignment with the organisation's strategic directions is important for successfully managing the major changes that the IT function has undergone in recent years. Topics covered will include assessment of IT impacts, achieving sustainable competitive advantage through IT, relationship between IT strategy and value, IT strategy formulation and implementation, evaluation of strategic investments in IT, IT portfolio management, IT sourcing and open innovation, and dynamics of IT strategy and game theory. It will explore IT-related strategic decision making at the different organisational levels and the concept of strategic congruence. This unit will also provide students with models, tools, and techniques to evaluate an organisation's IT strategic position, and hence to help make appropriate strategic choices.
ISYS5050 Knowledge Management Systems

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: An undergraduate degree in Computer Science or Information Systems. Good grasp of database technologies and the role of information systems in organisations. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
The need to track and facilitate the sharing of the core knowledge resources in contemporary organisations is widely recognised. This course will provide a comprehensive introduction to the area of Knowledge Management (KM) from both technological and organisational perspectives. We will review and discuss a range of published papers, case studies, and other publications that deal with a range of important KM-related topics. One of the key knowledge management technologies, Business Intelligence Systems, will be covered in detail. It will also include hands-on work using the BI (Online Analytical Processing- OLAP) tool, COGNOS.
Some of the main themes to be covered will include: KM- Conceptual Foundations; Taxonomies of organizational knowledge and KM mechanisms; Case/Field Studies of KM Initiatives; Data Warehousing and OLAP/Business Analytics; Data, text, and web mining; Social media,crowdsourcing, and KM; Big data and actionable knowledge.
ISYS5070 Change Management in IT

Credit points: 6 Session: Summer Main Classes: Lectures, Tutorials, Presentation, Project Work - own time Assumed knowledge: The unit is expected to be taken after the following related units INFO6007 Project Management in IT and COMP5206 Information Technologies and Systems. Assessment: Through semester assessment (70%) and Final Exam (30%) Mode of delivery: Block mode
This unit of study presents the leading edge of research and practice in change management and focuses on theories, frameworks and perspectives that can guide your work as a change agent in the IT industries. The unit will cover a range of approaches, methods, interventions and tools that can be used to successfully manage change projects that relate to the implementation of new technologies.
The globalisation of markets and industries, accelerating technological innovations and the need of companies to remain at the forefront of technological developments in an increasingly competitive, globalised industry have resulted in a significant increase in the speed, magnitude, and unpredictability of technological and organisational change over the last decades. Companies who have the competencies required to navigate change and overcome the inevitable obstacles to success gain a much-needed competitive edge in the marketplace. Increased globalization, economic rationalism, environmental dynamics and technological changes mean that companies, more than ever before, need to be highly flexible and adaptable to survive and thrive. Yet, a large percentage of IT projects fail to achieve the intended objectives, go over time or over budget. The capability to successfully manage organisational and technological change has become a core competency for IT professionals, business leaders and project managers.
This unit has been specifically developed for IT professionals, project managers, and senior managers to equip them with the knowledge and tools needed to ensure that IT projects remain on track to achieving the intended objectives on time and on budget. The course presents the key theories, concepts and findings in the context of academic research and change management practice. The objective is to allow participants to critically assess academic theories and methodological practice and devise interventions and actions that allow the successful management of IT initiatives.

Foundation units

Candidates may complete a maximum of 12 credit points from the listed Foundation units.
COMP9007 Algorithms

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: COMP5211 Assumed knowledge: This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structure can also help students to better understand the concepts of Algorithms will be taught in this course. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The study of algorithms is a fundamental aspect of computing. This unit of study covers data structures, algorithms, and gives an overview of the main ways of computational thinking from simple list manipulation and data format conversion, up to shortest paths and cycle detection in graphs. Students will gain essential knowledge in computer science, including basic concepts in data structures, algorithms, and intractability, using paradigms such as dynamic programming, divide and conquer, greed, local search, and randomisation, as well NP-hardness.
COMP9103 Software Development in Java

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lecture, Laboratory Prohibitions: COMP5214 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Programming in a legible, maintainable, reusable way is essential to solve complex problems in the pervasive computing environments. This unit will equip students with foundation of programming concepts that are common to widely used programming languages. Students will be progressively guided in this introductory unit from necessary and important building blocks of programming to the object-oriented approach. Java, one of the most popular programming languages, is used in this unit. It provides interdisciplinary approaches, applications and examples to support students from broad backgrounds such as science, engineering, and mathematics.
COMP9110 System Analysis and Modelling

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: ELEC3610 OR ELEC5743 OR INFO2110 OR INFO5001 OR ISYS2110 Assumed knowledge: Experience with a data model as in COMP9129 or COMP9103 or COMP9220 or COMP9120 or COMP5212 or COMP5214 or COMP5028 or COMP5138 Assessment: Through semester assessment (30%) and Final Exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides a comprehensive introduction to the analysis of complex systems. Key topics are the determination and expression of system requirements (both functional and non-functional), and the representation of structural and behavioural models of the system in UML notations. Students will be expected to evaluate requirements documents and models as well as producing them. This unit covers essential topics from the ACM/IEEE SE2004 curriculum, especially from MAA Software Modelling and Analysis. Note: The lectures of this unit are co-taught with ISYS2110.
COMP9120 Database Management Systems

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials, Project work Prohibitions: INFO2120 OR INFO2820 OR INFO2005 OR INFO2905 OR COMP5138 OR ISYS2120. Students who have previously studied an introductory database subject as part of their undergraduate degree should not enrol in this foundational unit, as it covers the same foundational content. Assumed knowledge: Some exposure to programming and some familiarity with data model concepts Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides a conceptual and practical introduction to the use of common platforms that manage large relational databases. Students will understand the foundations of database management and enhance their theoretical and practical knowledge of the widespread relational database systems, as these are used for both operational (OLTP) and decision-support (OLAP) purposes. The unit covers the main aspects of SQL, the industry-standard database query language. Students will further develop the ability to create robust relational database designs by studying conceptual modelling, relational design and normalization theory. This unit also covers aspects of relational database management systems which are important for database administration. Topics covered include storage structures, indexing and its impact on query plans, transaction management and data warehousing.
In this unit students will develop the ability to: Understand the foundations of database management; Strengthen their theoretical knowledge of database systems in general and relational data model and systems in particular; Create robust relational database designs; Understand the theory and applications of relational query processing and optimisation; Study the critical issues in data and database administration; Explore the key emerging topics in database management.
COMP9121 Design of Networks and Distributed Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Prohibitions: COMP5116 Assessment: Through semester assessment (40%) Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit covers general foundations of communication systems and a detailed walk through of the implementation of the TCP/IP protocol stack, which forms the basis of the Internet. The unit also covers the basic knowledge of how to analyse, design and implement simple communication protocols.
On completion of this unit students will have developed an understanding of the principles and practice of the layered model of communications architecture, the TCP/IP protocol stack and its component protocols, and various common techniques and tools for protocol analysis and design.
COMP9201 Software Construction and Design 1

Credit points: 6 Session: Semester 2 Classes: lectures, laboratories Prohibitions: INFO3220 OR SOFT2201 Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit introduces the foundations of software design and construction. It covers the topics of modelling software (UML, CRC, use cases), software design principles, object-oriented programming theory (inheritance, polymorphism, dynamic subtyping and generics), and simple design patterns. The unit aims to foster a strong technical understanding of the underlying software design and construction theory (delivered in the lecture) but also has a strong emphasis of the practice, where students apply the theory on practical examples.
COMP9601 Computer and Network Organisation

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Prohibitions: COMP5213 Assessment: Through semester assessment (40%) Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides an introduction to computer organisation and network protocols. It covers a broad range of topics including computer hardware, software architecture (operating systems, compilers, etc), and principles of communication network protocols. It is designed to give students an understanding of how software programs operate and run inside the computer hardware, and therefore the knowledge how to use computers most effectively.
INFO9003 IT for Health Professionals

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project Work - own time Prohibitions: INFO5003 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Block mode
Information technologies (IT) and systems have emerged as the primary platform to support communication, collaboration, research, decision making, and problem solving in contemporary health organisations. The essential necessity for students to acquire the fundamental knowledge and skills for applying IT effectively for a wide range of tasks is widely recognised. This is an introductory unit of study which prepares students in the Health discipline to develop the necessary knowledge, skills and abilities to be competent in the use of information technology for solving a variety of problems. The main focus of this unit is on modelling and problem solving through the effective use of using IT. Students will learn how to navigate independently to solve their problems on their own, and to be capable of fully applying the power of IT tools in the service of their goals in their own health domains while not losing sight of the fundamental concepts of computing.
Students are taught core skills related to general purpose computing involving a range of software tools such as spreadsheets, database management systems, internet search engine. Students will undertake practical tasks including scripting languages and building a small scale application for managing information. In addition, the course will address the issues arising from the wide-spread use of information technology in a variety of Health area.
INFO9117 Intro to Software Engineering Practice

This unit of study is not available in 2019

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: Skill as an individual programmer (as expected from any IT graduate, who could be admitted to GCertIT, GDipIT or MIT degree) Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This is an elective for students in the postgraduate IT degrees. It is expected to be taken early in the degree if (and only if) their undergraduate education and subsequent experience have not covered this vital aspect, namely working in groups under a defined process to deliver a software development project. Remark: the Sydney University undergraduate degrees in IT and in SE all cover this material, especially through the unit COMP3615 or INFO3600 and INFO3402; however, not all institutions guarantee this sort of experience for IT graduates. This unit will scaffold such students to do well in future group development projects, in their coursework or in industry, by providing fundamental knowledge of Software Engineering processes and practices.
Much of the student's effort will be directed towards a carefully managed small-group project to deliver a software system to meet a client's needs; they will be working with a client who may be external, or who may be a member of the teaching staff role-playing as an external client. A member of the teaching staff (separate from anyone who is acting as client) will take the role of manager for the group, checking progress and providing feedback frequently. By the end of the unit, the students will understand the processes and practices used in group projects that develop software, and they will be able to follow these processes and practices, so that they can contribute effectively in a small group that is developing software to meet clients needs.
PUBH5018 Introductory Biostatistics

Credit points: 6 Teacher/Coordinator: Dr Kevin McGeechan, Dr Erin Cvejic Session: Semester 1 Classes: 2 x 2hr lecture, 10 x 1hr lectures, 11 x 2hr tutorials, 2 x 1hr and 8 x 0.5hr statistical computing self directed learning tasks over 12 weeks - lectures and tutorials may be completed online Assessment: Weekly quizzes (10%), 1x4 page assignment (20%), 1 x 1hr online test (20%) and 1x1.5hr open-book exam (50%). For distance students it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit aims to provide students with an introduction to statistical concepts, their use and relevance in public health. This unit covers descriptive analyses to summarise and display data; concepts underlying statistical inference; basic statistical methods for the analysis of continuous and binary data; and statistical aspects of study design. Specific topics include: sampling; probability distributions; sampling distribution of the mean; confidence interval and significance tests for one-sample, two paired samples and two independent samples for continuous data and also binary data; correlation and simple linear regression; distribution-free methods for two paired samples, two independent samples and correlation; power and sample size estimation for simple studies; statistical aspects of study design and analysis. Students will be required to perform analyses using a calculator and will also be required to conduct analyses using statistical software (SPSS). It is expected that students spend an additional 2 hours per week preparing for their tutorials. Computing tasks are self-directed.
Textbooks
Course notes are provided.
STAT5002 Introduction to Statistics

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Assumed knowledge: HSC Mathematics Assessment: 2 hour examination (60%), assignments (20%), quizzes (20%) Mode of delivery: Normal (lecture/lab/tutorial) evening
The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.
Textbooks
All of Statistics, Larry Wasserman, Springer (2004)

Elective Units

Candidates may complete a maximum of 12 credit points from the listed Elective units.
COMP5705 Information Technology Short Project

Credit points: 6 Session: Intensive January,Semester 1,Semester 2,Summer Main Classes: Meeting, Project Work Prohibitions: COMP5702 or COMP5703 or COMP5704 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
This is a short 6cp IT project unit of study that can be taken either stand-alone as a short IT project during winter or summer schools, or as an internship-project as part of an industry-based scholarship such as the Faculty's Postgraduate Industry Project Placement Scheme (PIPPS). The focus is on the development of a client-focused solution with proper project management and documentation. For such students who follow the internship model of one day a week over both semester 1 and semester 2, COMP5705 can be combined with COMP5706 IT Industry Placement Project.
CSYS5010 Introduction to Complex Systems

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Laboratories Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
Globalisation, rapid technological advances, the development of integrated and distributed systems, cross-disciplinary technical collaboration, and the emergence of "evolved" (as opposed to designed) systems are some of the reasons why many systems have begun to be described as complex systems in recent times. Complex technological, biological, socio-economic and socio-ecological systems (power grids, communication and transport systems, food webs, megaprojects, and interdependent civil infrastructure) are composed of large numbers of diverse interacting parts and exhibit self-organisation and/or emergent behaviour. This unit will introduce the basic concepts of "complex systems theory", and focus on methods for the quantitative analysis and modelling of collective emergent phenomena, using diverse computational approaches such as agent-based modelling and simulation, cellular automata, bio-inspired algorithms, and game theory. Students will gain theoretical knowledge of complex adaptive systems, coupled with practical skills in computational simulation and forecasting using a range of modern toolkits.
DATA5207 Data Analysis in the Social Sciences

Credit points: 6 Session: Intensive December,Semester 1 Classes: lectures, laboratories Assumed knowledge: COMP5310 Assessment: through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.
ELEC5507 Error Control Coding

Credit points: 6 Session: Semester 1 Classes: Lectures, Project Work - own time, Tutorials Assumed knowledge: Fundamental mathematics including probability theory and linear algebra. Basic knowledge on digital communications. Basic MATLAB programming skills is desired. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit deals with the principles of error control coding techniques and their applications in various communication and data storage systems. Its aim is to present the fundamentals of error control coding techniques and develop theoretical and practical skills in the design of error control encoders/decoders. Successful completion of this unit will facilitate progression to advanced study or to work in the fields of telecommunications and computer engineering. It is assumed that the students have some background in communications principles and probability theory.
The following topics are covered: Introduction to error control coding, Linear algebra, Linear block codes, Cyclic codes, BCH codes, Reed-Solomon codes, Applications of block codes in communications, Convolutional codes, Viterbi algorithm, Applications of convolutional codes in communications, Soft decision decoding of block and convolutional codes, LDPC codes, Turbo codes, MIMO and rateless codes.
ELEC5508 Wireless Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories Assumed knowledge: Basic knowledge in probability and statistics, analog and digital communications, error probability calculation in communications channels, and telecommunications network. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will introduce the key ideas in modern wireless telecommunications networks. It will address both physical layer issues such as propagation and modulation, plus network layer issues such as capacity, radio resource management and mobility management issues.
The following topics are covered. Wireless channel: Multipath fading, frequency selective fading, Doppler spread, statistical models, diversity, GSM, OFDM. Capacity and Interference: Cell types, coverage, frequency reuse, interference management, SIMO, MISO, multiuser diversity, CDMA, OFDMA, beamforming, superposition coding. MIMO: SVD, waterfilling, beamforming, V-BLAST, SIC, MMSE, Power Allocation. LTE/LTE-Advanced: Uplink-downlink channels, control signals, data transmission, spatial multiplexing, CoMP, spectrum reuse, heterogeneous networks, inter-cell interference coordination, carrier aggregation. Queueing theory: basic models, queueing systems, waiting time, delay, queue length, priority queues, wireless network virtualization (WNV) queues.
ELEC5510 Satellite Communication Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Site Visit, Project Work - own time, Tutorials, Laboratories Assumed knowledge: Knowledge of error probabilities, analog and digital modulation techniques and error performance evaluation studied in ELEC3505 Communications and ELEC4505 Digital Communication Systems, is assumed. Assessment: Through semester assessment (30%) and Final Exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Satellite communication systems provide fixed and mobile communication services over very large areas of land, sea and air. This unit presents the fundamental knowledge and skills in the analysis and design of such systems. It introduces students to the broad spectrum of satellite communications and its position in the entire telecommunications network; helps students to develop awareness of the key factors affecting a good satellite communications system and theoretical and practical skills in the design of a satellite communications link.
Topic areas include: satellite communication link design; propagation effects and their impact on satellite performance; satellite antennas; digital modem design, speech codec design; error control for digital satellite links.
ELEC5511 Optical Communication Systems

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: (ELEC3405 OR ELEC9405) AND (ELEC3505 OR ELEC9505). Basic knowledge of communications, electronics and photonics Assessment: Through semester assessment (25%) and Final Exam (75%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: -
Optical telecommunications has revolutionized the way we receive information and communicate with one another. This course will provide an understanding of the fundamental principles of optical fibre communication systems. It commences with a description of optical fibre propagation characteristics and transmission properties. We will then consider light sources and the fundamental principles of laser action in semiconductor and other lasers including quantum well lasers, tunable lasers and fibre lasers, and also the characteristics of optical transmitters based on semiconductor and electro-optic modulation techniques. The characteristics of optical amplifiers will also be discussed. On the receiver side, the principles of photodetection and optical receiver sensitivity will be presented. Other aspects such as fibre devices and multiple wavelength division multiplexing techniques will also be discussed. Finally, the complete optical fibre communication system will be studied to enable the design of data transmission optical systems, local area networks and multi-channel optical systems.
ELEC5512 Optical Networks

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: Knowledge of digital communications, wave propagation, and fundamental optics Assessment: Through semester assessment (30%) and Final Exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit builds upon the fundamentals of optical communication introduced in ELEC3405 (Communications Electronics and Photonics). It focuses on photonic network architectures and protocols, network design, enabling technologies and the drivers for intelligent optical network.
Students will learn how to analyse and design optical networks and optical components.
Introduction, photonic network architectures: point to point, star, ring, mesh; system principles: modulation formats, link budgets, optical signal to noise ratio, dispersion, error rates, optical gain and regeneration; wavelength division multiplexed networks; WDM components: optical filters, gratings, multiplexers, demultiplexers, wavelength routers, optical crossconnects, wavelength converters, WDM transmitters and receivers; Wavelength switched/routed networks, ultra high speed TDM, dispersion managed links, soliton systems; broadcast and distribution networks, multiple access, subcarrier multiplexed lightwave video networks, optical local area and metropolitan area networks; protocols for photonic networks: IP, Gbit Ethernet, SDH/SONET, FDDI, ATM, Fibre Channel.
INFS6012 Enterprise Systems Management

Credit points: 6 Session: Semester 2 Classes: 1x 3hr seminar per week Assessment: individual assignment (25%), group project (25%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) evening
This unit explores the strategic managerial issues that arise from the implementation and use of Enterprise Systems as a means of integrating data and standardising processes. The unit utilises a combination of practical sessions with an Enterprise System, such as SAP, and analyses based on readings of case studies to explore the long-term effects of strategic implementation decisions, and issues with regard to Enterprise System implementation projects. The unit explores the emergence and implications of cloud-based Enterprise Systems, and the part that Enterprise Systems play in an organisation's broader information infrastructure.
INFS6015 Business Process Management

Credit points: 6 Session: Semester 2 Classes: 1x 3hr seminar per week Assessment: individual assignment (25%), group assignment (25%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides an overview of the business process architecture and life cycle from a management perspective. It provides a detailed understanding of the concepts, strategies, tools and technologies required for modelling, analysis, design, improvement, integration, performance measurement and governance of business processes (both intra- and inter-enterprise) in any organisational and/or value chain context and relevant industry standards. The unit also develops practical skills in modelling, redesigning and improving business processes using various business process management software tools/suites.
INFS6016 Technology Enabled Business Innovation

Credit points: 6 Session: Semester 2 Classes: 1 x 3hr seminar per week Assumed knowledge: INFS6004 and; Understanding the major functions of a business and how those business functions interact internally and externally so the company can be competitive in the market is essential in order to critically analyse how and where a business can be innovative. Some knowledge of how technology can be applied in a business is also essential. Experience as a member of a project team is desirable. Assessment: individual project proposal (10%), group project report (45%), group project presentation (5%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit develops knowledge and skills in innovative, technology-enabled business models and strategies from a management perspective. The unit facilitates a better understanding and application of the concepts, strategies, tools and technologies necessary for undertaking business innovation. From basic knowledge of business models and essential business processes, this unit increases awareness and understanding of stakeholders, their capabilities and their limitations in the strategic convergence of technology and business. It provides insights into the technology and infrastructure required to support commerce in the 21st Century and supports development of student capabilities to analyse, develop and evaluate innovative technology-enabled business strategies and models.
INFS6018 Managing Business Intelligence

Credit points: 6 Session: Semester 1 Classes: 1 x 3hr seminar per week Assumed knowledge: Understanding the major functions of a business and how those business functions interact internally and externally so the company can be competitive in a changing market. How information systems can be used and managed in a business. How to critically analyse a business and determine its options for transformation. (ii) Desirable Experience as a member of a project team. Assessment: mid-semester exam (35%); project report (30%); project presentation (10%); reflective summary (25%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Business Intelligence (BI), increasingly known as Business Analytics, is a major source of competitive advantage in the Information Age and is therefore a leading business priority globally. In recent times, this field has evolved from a technology topic to a management priority, creating an unprecedented demand for new management skills. Taking a business rather than technology perspective, this unit covers all aspects of the enterprise BI ecosystem in the context of strategic and operational BI, including all five stages of BI evolution. Topics include assessment and management of organisational data quality, multidimensional data modelling and integration, management of structured and unstructured data (including those created by social media), business aspects of data warehousing, innovation through advanced analytics, BI driven performance management, business process intelligence, active enterprise intelligence, and management of complex BI projects. Access is provided to the largest world-wide community of BI academics and industry practitioners called TUN (www.TeradataUniversityNetwork.com). The hands-on experience in using a commercial BI platform, combined with in-depth analytical skills, will enable students completing the unit to help any organization (regardless of its size and industry domain) to derive more intelligence from its data and compete on analytics. This unit does not require programming experience; it is suitable for both current and aspiring BI practitioners as well as general business practitioners from any functional area interested to learn how to start and lead BI-related initiatives.

Professional Pathway units

24 credits of Core, Specialist or Foundation units must be completed before enrolling in any Project units of study.
The minimum requirement for the Professional Pathway is 12 credit points of Information Technology Capstone Project units. Candidates can complete either COMP5770 and COMP5708 (6 & 6 credit points over two semesters) or COMP5703 (12 credit points in one semester).
COMP5703 Information Technology Capstone Project

Credit points: 12 Session: Semester 1,Semester 2 Classes: Project Work, Meeting Prerequisites: A candidate for the MDS, MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Prohibitions: COMP5702 OR COMP5704 OR COMP5707 OR COMP5708 OR COMP5709 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
The Information Technology Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM) or in the data science domain (MDS). The results will be presented in a final project presentation and report.
It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of IT/DS skills in research or design.
COMP5707 Information Technology Capstone A

Credit points: 6 Session: Semester 1,Semester 2 Classes: Research/Project Work, Meeting Prohibitions: COMP5702 OR COMP5704 OR COMP5703. Eligible students of the IT Capstone Project may choose either COMP5703 or COMP5707/COMP5708. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the MDS, MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Capstone project will be required to complete both COMP5707 (6 CPS) and COMP5708 (6 CPS), totaling 12 CPS.
The Information Technology Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM). The results will be presented in a final project presentation and report.
It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of IT skills in research or design.
COMP5708 Information Technology Capstone B

Credit points: 6 Session: Semester 1,Semester 2 Classes: Research/Project Work, Meeting Corequisites: COMP5707 Prohibitions: COMP5702 OR COMP5704 OR COMP5703. Eligible students of the IT Capstone Project may choose either COMP5703 or COMP5707/COMP5708. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Capstone project will be required to complete both COMP5707 (6 CPS) and COMP5708 (6 CPS), totaling 12 CPS.
The Information Technology Capstone project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to choose a research/development project that demonstrates their prior learning in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM). The results will be presented in a final project presentation and report.
It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and manner that fosters the development of IT skills in research or design.
COMP5709 IT Capstone Project - Individual

Credit points: 12 Session: Semester 1,Semester 2 Classes: meetings Prohibitions: COMP5702 OR COMP5703 OR COMP5704 OR COMP5707 OR COMP5708 Assumed knowledge: A candidate for the MDS, MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study, and has a WAM of 75+ may take this unit Assessment: through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
The Information Technology Capstone project unit provides an opportunity for high-achieving students (WAM of 75+) to carry out an individual defined piece of work with academics of our school. The students will acquire skills including the capacity to define a project, show how it relates to existing work, and carry out the project in a systematic manner. Students will apply their gained knowledge of units of study in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM) or in the data science domain (MDS). The results will be presented in a final project presentation and report. The unit aims to provide students with the opportunity to carry out an advanced project work in a setting and manner that fosters the development of IT/DS skills in research or design.

Research Pathway units

Candidates in the Research Pathway must take all 24 credit points of the Research Pathway units of study in the table below.
COMP5702 IT Research Project A

Credit points: 12 Session: Semester 1,Semester 2 Classes: Research, Meeting Prohibitions: COMP5707 OR COMP5708 OR COMP5703. Students enrolling (and eligible) for the IT Research Project are not eligible to enrol in the IT Capstone Project Units. Assumed knowledge: Students should take INFO5993 - Research Methods in IT either concurrently or prior to undertaking this project unit. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Research project will be required to complete both COMP5702 (12CPS) and COMP5704 (6 CPS), totaling 18 CPS. In addition, students should take INFO5993 - Research Methods in IT either concurrently or prior to undertaking this project unit.
The Information Technology Research Project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to define an original research project that demonstrates their prior learning in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM). The results will be presented in a final project presentation and report.
It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent research work in a setting and manner that fosters the development of IT skills in research.
COMP5704 IT Research Project B

Credit points: 6 Session: Semester 1,Semester 2 Classes: Research, Meeting Prohibitions: COMP5707 OR COMP5708 OR COMP5703. Students enrolling (and eligible) for the IT Research Project are not eligible to enrol in the IT Capstone Project Units. Assumed knowledge: Students should take INFO5993 - Research Methods in IT either concurrently or prior to undertaking this project unit. Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the MIT, MITM or MIT / MITM who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the IT Research project will be required to complete both COMP5702 (12CPS) and COMP5704 (6 CPS), totaling 18 CPS. In addition, students should take INFO5993 - Research Methods in IT either concurrently or prior to undertaking this project unit.
The Information Technology Research Project provides an opportunity for students to carry out a defined piece of independent research or design. These skills include the capacity to define a research or design question, show how it relates to existing knowledge and carry out the research or design in a systematic manner. Students will be expected to define an original research project that demonstrates their prior learning in their advanced IT specialist domain (MIT) or the management of IT (MITM) or both technical and IT management domains (MIT/MITM). The results will be presented in a final project presentation and report.
It is not expected that the project outcomes from this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent research work in a setting and manner that fosters the development of IT skills in research.
INFO5993 IT Research Methods

Credit points: 6 Session: Semester 1,Semester 2 Classes: Seminars Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will provide an overview of the different research methods that are used in IT. Students will learn to find and evaluate research on their topic and to present their own research plan or results for evaluation by others. The unit will develop a better understanding of what research in IT is and how it differs from other projects in IT. Students will learn research ethics. This unit of study is required for students in IT who are enrolled in a research project as part of their Honours or MIT/MITM degree. It is also recommended for students enrolled or planning to do a research degree in IT and Engineering.

Exchange Units

Exchange units require the approval of the Program Director. With approval, up to 24 credit points of Exchange units may be taken in place of other units, towards the requirements of the degree.
INFO5551 Postgraduate IT Exchange A

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is for University of Sydney students in the Exchange program studying at an overseas University.
INFO5552 Postgraduate IT Exchange B

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is for University of Sydney students in the Exchange program studying at an overseas University.
INFO5553 Postgraduate IT Exchange C

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is for University of Sydney students in the Exchange program studying at an overseas University.
INFO5554 Postgraduate IT Exchange D

Credit points: 6 Session: Semester 1,Semester 2 Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is for University of Sydney students in the Exchange program studying at an overseas University.

Majors for the Master of Information Technology/Master of Information Technology Management

The completion of a major is an optional requirement of this degree. The award of a major requires the completion of 18 credit points of Information Technology Specialist units listed under the relevant major.

Biomedical and Health Informatics

COMP5405 Digital Media Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Prohibitions: COMP5114 OR COMP9419 Assessment: through semester assessment (50%) and final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Digital media data such as audio, image, videos, graphics, and 3D are increasingly becoming indispensable for big data driven computing applications in many domains, such as social media, public security, education, commerce, entertainment, and healthcare. This unit aims to bring students the essential knowledge on digital media, various computing techniques and tools on digital media processing and analysis, and many cutting-edge digital media applications such as VR/AR and Internet of Things (IoT) enabled new media. It will help students build practical computing skills for digital media driven applications and utilise learned knowledge to produce creative and media rich solutions to real world problems.
COMP5424 Information Technology in Biomedicine

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Information technology (IT) has significantly contributed to the research and practice of medicine, biology and health care. The IT field is growing enormously in scope with biomedicine taking a lead role in utilising the evolving applications to its best advantage. The goal of this unit of study is to provide students with the necessary knowledge to understand the information technology in biomedicine. The major emphasis will be on the principles associated with biomedical digital imaging systems and related biomedicine data processing, analysis, visualisation, registration, modelling, retrieval and management. A broad range of practical integrated clinical applications will be also elaborated.
ELEC5622 Signals, Software and Health

Credit points: 6 Session: Semester 2 Classes: Project Work - in class, Project Work - own time, Presentation, Tutorials, Laboratories Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aims to introduce students to the main issues involved in producing systems that use sensor data, such as those from physiology and activity tracking, often combined with patients self-reports. As sensing devices become ubiquitous, data processing, storage and visualisation techniques are becoming part of all health systems, both institutionalised and individually driven.
The unit is related to, but distinct, to health informatics- an area that focuses on the the use of computing to deliver cost efficient healthcare and the area of bioinformatics, that explores the role of computing in understanding biology at the cellular level (e. g. genome). This unit focuses on the technical and non-technical problems of developing increasingly ubiquitous devices and systems that can be used for personal and clinical monitoring.
HSBH5003 e-Health for Health Professionals

Credit points: 6 Teacher/Coordinator: Professor Tim Shaw, Anna Janssen Session: Semester 1 Classes: online and 3x3-hrs face to face workshops Assessment: eHealth Evaluation (40%), eHealth Innovation Challenge (40%), eHealth reflection task (10%), participation (10%) Mode of delivery: Distance education/intensive on campus
The aim of this unit is to provide future health professionals with a strong foundation in e-Health on which they can make evidence-based decisions. In particular, this unit will provide students with opportunities to examine:
. How technology affects health care in different Australian health contexts
. Ethical issues surrounding e-Health
. Innovations in e-Health
. How emerging technologies affect patient-centred communication between health professionals, and health professionals and their clients/patients
. Strategies for interacting with patients and clients using different technologies
. Strategies for engaging in multi-disciplinary e-Healthcare delivery
. The relationship between technologies, data and the wider information network
Students will develop their skills in a variety of technologies identified as key e-Health skills for clinicians. Students will create an e-Health delivery portfolio to showcase these skills. This unit will also enable students to be lifelong learners by providing them with reflective learning skills. Reflective learning skills are identified as essential for lifelong learning.
INFO5306 Enterprise Healthcare Information Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories Assumed knowledge: The unit is expected to be taken after introductory courses in related units such as COMP5206 Information Technologies and Systems (or COMP5138/COMP9120 Database Management Systems). Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Healthcare systems intimately coupled to ICT have been at the forefront of many of the medical advances in modern society in the past decade. As is already the case in many other service-driven sectors, it is widely recognised that a key approach to solve some of the healthcare challenges is to harness and further ICT innovations. This unit is designed to help fill a massive technology talent gap where one of the biggest IT challenges in history is in the technology transformation of healthcare.
The unit will consist of weekly lectures, a set of group discussions (tutorials) and practical lab sessions. The contents will offer students the opportunity to develop IT knowledge and skills related to all aspects of Enterprise Healthcare Information Systems.
Key Topics covered include: Health Information System e. g. , Picture Archiving and Communication Systems (PACS) and Radiology IS; Electronic Health Records / Personal Health Records; Health data management; Healthcare Transactions; Health Statistics and Research; Decision Support Systems including Image-based systems; Cost Assessments and Ethics / Privacy; TeleHealth / eHealth; Cases studies with Australian Hospitals.
Guest lecturers from the healthcare industry will be invited. The core of student's assessments will be based on individual research reports (topics related to the current industry IT needs), software / practical assignment and quizzes.
PUBH5010 Epidemiology Methods and Uses

Credit points: 6 Teacher/Coordinator: Dr Erin Mathieu, Professor Tim Driscoll Session: Semester 1 Classes: 1x 1hr lecture and 1x 2hr tutorial per week for 13 weeks - face to face or their equivalent online Prohibitions: BSTA5011 or CEPI5100 Assessment: 1x 6 page assignment (25%), 10 weekly quizzes (5% in total) and 1x 2.5hr supervised open-book exam (70%). For distance students, it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issue. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. In addition to formal classes or their on-line equivalent,it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.
Textbooks
Webb, PW. Bain, CJ. and Pirozzo, SL. Essential Epidemiology: An Introduction for Students and Health Professionals Second Edition: Cambridge University Press 2017.

Data Management and Analytics

COMP5046 Natural Language Processing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Knowledge of an OO programming language Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP).
Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing.
Students will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. Students will annotate data as part of completing a real-world NLP task.
COMP5048 Visual Analytics

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have basic knowledge of data structures, algorithms and programming skills. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.
COMP5318 Machine Learning and Data Mining

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.
COMP5328 Advanced Machine Learning

Credit points: 6 Session: Semester 2 Classes: Lectures, tutorials Assumed knowledge: COMP5318 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Machine learning models explain and generalise data. This course introduces some fundamental machine learning concepts, learning problems and algorithms to provide understanding and simple answers to many questions arising from data explanation and generalisation. For example, why do different machine learning models work? How to further improve them? How to adapt them to different purposes?
The fundamental concepts, learning problems and algorithms are carefully selected. Many of them are closely related to practical questions of the day, such as transfer learning, learning with label noise and multi-view learning.
COMP5329 Deep Learning

Credit points: 6 Session: Semester 1 Classes: Tutorials, Lectures Assumed knowledge: COMP5318 Assessment: through semester assessment (50%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.
COMP5338 Advanced Data Models

Credit points: 6 Session: Semester 2 Classes: Tutorials, Lectures Assumed knowledge: This unit of study assumes foundational knowledge of relational database systems as taught in COMP5138/COMP9120 (Database Management Systems) or INFO2120/INFO2820/ISYS2120 (Database Systems 1). Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study gives a comprehensive overview of post-relational data models and of latest developments in data storage technology.
Particular emphasis is put on spatial, temporal, and NoSQL data storage. This unit extensively covers the advanced features of SQL:2003, as well as a few dominant NoSQL storage technologies. Besides in lectures, the advanced topics will be also studied with prescribed readings of database research publications.
COMP5349 Cloud Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The course will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5425 Multimedia Retrieval

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: COMP9007 or COMP5211. Basic Programming skills and data structure knowledge. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The explosive growth of multimedia data, including text, audio, images and video has imposed unprecedented challenges for search engines to meet various information needs of users. This unit provides students with the necessary and updated knowledge of this field in the context of big data, from the information retrieval basics of a search engine, to many advanced techniques towards next generation search engines, such as content based image and video retrieval, large scale visual information retrieval, and social media.
INFO5060 Data Analytics and Business Intelligence

Credit points: 6 Session: Summer Main Classes: Lectures, Tutorials, Laboratories, Presentation, Project Work - own time Assumed knowledge: The unit is expected to be taken after introductory courses or related units such as COMP5206 Information Technologies and Systems Assessment: Through semester assessment (65%) and Final Exam (35%) Mode of delivery: Block mode
The frontier for using data to make decisions has shifted dramatically. High performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. This course provides an overview of Business Intelligence (BI) concepts, technologies and practices, and then focuses on the application of BI through a team based project simulation that will allow students to have practical experience in building a BI solution based on a real world case study.
STAT5003 Computational Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: STAT5002 Assessment: Assignments (40%), quizzes (20%); 2-hour final examination (40%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolment
The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.
Textbooks
(1) An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, (2014), Springer;

Digital Media Technology

COMP5045 Computational Geometry

Credit points: 6 Session: Semester 1 Classes: Project Work Assumed knowledge: Students are assumed to have a basic knowledge of the design and analysis of algorithms and data structures: you should be familiar with big-O notations and simple algorithmic techniques like sorting, binary search, and balanced search trees. Assessment: Through semester assessment (72%) and Final Exam (28%) Mode of delivery: Normal (lecture/lab/tutorial) day
In many areas of computer science- robotics, computer graphics, virtual reality, and geographic information systems are some examples- it is necessary to store, analyse, and create or manipulate spatial data. This course deals with the algorithmic aspects of these tasks: we study techniques and concepts needed for the design and analysis of geometric algorithms and data structures. Each technique and concept will be illustrated on the basis of a problem arising in one of the application areas mentioned above.
COMP5048 Visual Analytics

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have basic knowledge of data structures, algorithms and programming skills. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.
COMP5405 Digital Media Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Prohibitions: COMP5114 OR COMP9419 Assessment: through semester assessment (50%) and final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Digital media data such as audio, image, videos, graphics, and 3D are increasingly becoming indispensable for big data driven computing applications in many domains, such as social media, public security, education, commerce, entertainment, and healthcare. This unit aims to bring students the essential knowledge on digital media, various computing techniques and tools on digital media processing and analysis, and many cutting-edge digital media applications such as VR/AR and Internet of Things (IoT) enabled new media. It will help students build practical computing skills for digital media driven applications and utilise learned knowledge to produce creative and media rich solutions to real world problems.
COMP5415 Multimedia Design and Authoring

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides principles and practicalities of creating interactive and effective multimedia products. It gives an overview of the complete spectrum of different media platforms and current authoring techniques used in multimedia production. Coverage includes the following key topics: enabling multimedia technologies; multimedia design issues; interactive 2D and 3D computer animation; multimedia object modelling and rendering; multimedia scripting programming; post-production and delivery of multimedia applications.
COMP5425 Multimedia Retrieval

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: COMP9007 or COMP5211. Basic Programming skills and data structure knowledge. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The explosive growth of multimedia data, including text, audio, images and video has imposed unprecedented challenges for search engines to meet various information needs of users. This unit provides students with the necessary and updated knowledge of this field in the context of big data, from the information retrieval basics of a search engine, to many advanced techniques towards next generation search engines, such as content based image and video retrieval, large scale visual information retrieval, and social media.
COMP5427 Usability Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Usability engineering is the systematic process of designing and evaluating user interfaces so that they are usable. This means that people can readily learn to use them efficiently, can later remember how to use them and find it pleasant to use them. The wide use of computers in many aspects of people's lives means that usability engineering is of the utmost importance.
There is a substantial body of knowledge about how to elicit usability requirements, identify the tasks that a system needs to support, design interfaces and then evaluate them. This makes for systematic ways to go about the creation and evaluation of interfaces to be usable for the target users, where this may include people with special needs. The field is extremely dynamic with the fast emergence of new ways to interact, ranging from conventional WIMP interfaces, to touch and gesture interaction, and involving mobile, portable, embedded and desktop computers.
This unit will enable students to learn the fundamental concepts, methods and techniques of usability engineering. Students will practice these in small classroom activities. They will then draw them together to complete a major usability evaluation assignment in which they will design the usability testing process, recruit participants, conduct the evaluation study, analyse these and report the results
IDEA9106 Design Thinking

Credit points: 6 Teacher/Coordinator: Dr Naseem Ahmadpour Session: Semester 1,Semester 2 Classes: Lecture 1 hr/wk, tutorial 2 hrs/wk Assessment: Design assignments (90%), Quizzes (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides an overview of a human-centred approach to the design of products and systems. It introduces students to design thinking and how it can be productively applied to different design situations. The theoretical concepts, methods and tools for the key stages of interaction design are covered including user research, ideation, prototyping and user evaluation. It provides students with the principles, processes and tools for working collaboratively on design projects in studio. Students learn to build empathy with users, identify and reframe the problem space, develop value-driven design concepts and persuasively communicate design proposals with an emphasis on the user experience through visual storytelling. This unit is a foundational core unit in the Master of Interaction Design and Electronic Arts program.

IT Security

CISS6022 Cybersecurity

Credit points: 6 Session: Semester 1 Classes: 1x2hr seminar/week Assessment: 1x2hr exam (40%), 1x3000wd analytical Essay (40%), 1x1000wd equivalent lab exercise (10%), 1xSeminar participation (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
The digital revolution has created new frontiers of information that influence almost every aspect of our lives. But does cyberspace also threaten our security? What are the methods and motives for attack? And how can state and non-state actors respond? Drawing on a unique combination of expertise from the Centre for International Security Studies and the School of Information Technologies, this unit introduces students to the technical and political concepts that are necessary to answer these important questions.
COMP5347 Web Application Development

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory, Project Work Prerequisites: INFO1103 or INFO1113 or COMP9103 or COMP9220 or COMP5028 Assumed knowledge: COMP9220 or COMP5028. The course assumes basic knowledge on OO design and proficiency in a programming language Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Nowadays most client facing enterprise applications are running on web or at least with a web interface. The design and implementation of a web application require totally different set of skills to those are required for traditional desktop applications. All web applications are of client/ server architecture. Requests sent to a web application are expected to go through the public Internet, which slows the responsiveness and increases the possible security threat. A typical web application is also expected to handle large number of requests coming from every corner of the Internet and sent by all sorts of client systems. This further complicates the design of such system.
This course aims at providing both conceptual understanding and hand-on experiences for the technologies used in building web applications. We will examine how data/messages are communicated between client and server; how to improve the responsiveness using rich client technology; as well as how to build a secure web application.
At the end of this course, students are expected to have a clear understanding of the structure and technologies of web applications. Students are also expected to have practical knowledge of some major web application environments and to be able to develop and deploy simple web applications. Cloud based platform are increasingly popular as the development and deployment platform. This course will incorporate the cloud aspect of web application development as well.
COMP5416 Advanced Network Technologies

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assumed knowledge: ELEC3506 OR ELEC9506 OR ELEC5740 OR COMP5116 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit introduces networking concepts beyond the best effort service of the core TCP/IP protocol suite. Understanding of the fundamental issues in building an integrated multi-service network for global Internet services, taking into account service objectives, application characteristics and needs and network mechanisms will be discussed. Enables students to understand the core issues and be aware of proposed solutions so they can actively follow and participate in the development of the Internet beyond the basic bit transport service.
COMP5617 Empirical Security Analysis and Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Project Work - own time Assumed knowledge: Students are expected to have: Good programming skills in Go, Python, or C. UNIX/Linux command-line and tools Technical orientation and foundational networking knowledge Sufficient mathematical skills to understand cryptography Experience working with version control Assessment: through semester assessment (40%) and final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will present the lessons from recent research and from case studies of practice to bring students the skills to assess and improve the security of deployed systems. A particular focus is on data-driven approaches to collect operational data about a system's security. We explore deployment issues at local and global scale, e. g. for X. 509, DNS, and BGP, and also take human factors explicitly into account. As a result, students will learn to put building blocks of security together in a sound way, to arrive at engineering solutions that are empirically verifiable, functional, and secure against realistic threats. As Dan Geer once famously said: "Any security technology whose effectiveness can't be empirically determined is indistinguishable from blind luck."
COMP5618 Applied Cybersecurity

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project work Assumed knowledge: (ELEC5616 OR INFO2315 OR INFO2222) with a grade of Credit or greater Assessment: through semester assessment (60%) and final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Digital technologies permeate every part of our lives. The internet has created a more open society, allowing us to create, share and access information and knowledge freely. As more of the services we rely on are digitised and available to use over the web, the more our identity, productivity, access to information, connectivity, social connections and financial well-being depends on information security. Consequently, a deep understanding of both offensive and defensive security techniques is fast becoming essential knowledge for a career in computing.
This course will provide in-depth knowledge of offensive security that will prepare the student for work in any technical field where they will are responsible for the development or maintenance of sensitive systems. The course begins by introducing the basic tools used by hackers, before highlighting the common weaknesses- and mitigations- for various levels of the technology stack, such as web applications, operating systems and corporate networks. Finally, students are provided practical insights into careers in information security in the areas of attack detection, prevention and defence. Students will develop the skills necessary to both gain access to test computers and to defend test networks from attack.
ELEC5616 Computer and Network Security

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials, Laboratories, Project Work - own time Assumed knowledge: A programming language, basic maths. Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit examines the basic cryptographic building blocks of security, working through to their applications in authentication, key exchange, secret and public key encryption, digital signatures, protocols and systems. It then considers these applications in the real world, including models for integrity, authentication, electronic cash, viruses, firewalls, electronic voting, risk assessment, secure web browsers and electronic warfare. Practical cryptosystems are analysed with regard to the assumptions with which they were designed, their limitations, failure modes and ultimately why most end up broken.
INFO5301 Information Security Management

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: This unit of study assumes foundational knowledge of Information systems management. Two year IT industry exposure and a breadth of IT experience will be preferable. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study gives a broad view of the management aspects of information security. We emphasise corporate governance for information security, organisational structures within which information security is managed, risk assessment, and control structures. Planning for security, and regulatory issues, are also addressed.

Networks and Distributed Systems

COMP5047 Pervasive Computing

Credit points: 6 Session: Semester 2 Classes: Studio class Assumed knowledge: ELEC1601 AND (COMP2129 OR COMP2017). Background in programming and operating systems that is sufficient for the student to independently learn new programming tools from standard online technical materials. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This is an advanced course on Pervasive Computing, with a focus on the "Internet of Things" (IoT). It introduces the key aspects of the IoT and explores these in terms of the new research towards creating user interfaces that disappear into the environment and are available pervasively, for example in homes, workplaces, cars and carried.
COMP5216 Mobile Computing

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: COMP5214 OR COMP9103. Software Development in JAVA, or similar introductory software development units. Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Mobile computing is becoming a main stream for many IT applications, due to the availability of more and more powerful and affordable mobile devices with rich sensors such as cameras and GPS, which have already significantly changed many aspects in business, education, social network, health care, and entertainment in our daily life. Therefore it has been critical for students to be equipped with sufficient knowledge of such new computing platform and necessary skills. The unit aims to provide an in-depth overview of existing and emerging mobile computing techniques and applications, the eco-system of the mobile computing platforms, and its key building components. The unit will also train students with hand-on experiences in developing mobile applications in a broad range of areas.
COMP5313 Large Scale Networks

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: Algorithmic skills (as expected from any IT graduate). Basic probability knowledge. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
The growing connected-ness of modern society translates into simplifying global communication and accelerating spread of news, information and epidemics. The focus of this unit is on the key concepts to address the challenges induced by the recent scale shift of complex networks. In particular, the course will present how scalable solutions exploiting graph theory, sociology and probability tackle the problems of communicating (routing, diffusing, aggregating) in dynamic and social networks.
COMP5349 Cloud Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The course will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5416 Advanced Network Technologies

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assumed knowledge: ELEC3506 OR ELEC9506 OR ELEC5740 OR COMP5116 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit introduces networking concepts beyond the best effort service of the core TCP/IP protocol suite. Understanding of the fundamental issues in building an integrated multi-service network for global Internet services, taking into account service objectives, application characteristics and needs and network mechanisms will be discussed. Enables students to understand the core issues and be aware of proposed solutions so they can actively follow and participate in the development of the Internet beyond the basic bit transport service.
COMP5426 Parallel and Distributed Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is intended to introduce and motivate the study of high performance computer systems. The student will be presented with the foundational concepts pertaining to the different types and classes of high performance computers. The student will be exposed to the description of the technological context of current high performance computer systems. Students will gain skills in evaluating, experimenting with, and optimising the performance of high performance computers. The unit also provides students with the ability to undertake more advanced topics and courses on high performance computing.
ELEC5509 Mobile Networks

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: ELEC3505 AND ELEC3506. Basically, students need to know the concepts of data communications and mobile communications, which could be gained in one the following units of study: ELEC3505 Communications, ELEC3506 Data Communications and the Internet, or similar units. If you are not sure, please contact the instructor. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study serves as an introduction to communications network research. The unit relies on a solid understanding of data communications and mobile networks. It introduces some of the currently most debated research topics in mobile networking and presents an overview of different technical solutions. Students are expected to critically evaluate these solutions in their context and produce an objective analysis of the advantages/disadvantages of the different research proposals. The general areas covered are wireless Internet, mobility management, quality of service in mobile and IP networks, ad hoc networks, and cellular network architectures.
The following topics are covered. Introduction to wireless and mobile Internet. Wireless cellular data networks. Cellular mobile networks. Mobile networks of the future. Quality of service in a mobile environment. Traffic modelling for wireless Internet. Traffic management for wireless Internet. Mobility management in mobile networks. Transport protocols for mobile networks. Internet protocols for mobile networks.
ELEC5514 Networked Embedded Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories Prerequisites: ELEC5509 Assumed knowledge: ELEC3305 AND ELEC3506 AND ELEC3607 AND ELEC5508 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aim to teach the fundamentals concepts associated with: Networked Embedded Systems, wireless sensor networks; Wireless channel propagation and radio power consumption; Wireless networks, ZigBee, Bluetooth, etc. ; Sensor principle, data fusion, source detection and identification; Multiple source detection, multiple access communications; Network topology, routing, network information theory; Distributed source channel coding for sensor networks; Power-aware and energy-aware communication protocols; Distributed embedded systems problems such as time synchronization and node localisation; Exposure to several recently developed solutions to address problems in wireless sensor networks and ubiquitous computing giving them a well-rounded view of the state-of the-art in the networked embedded systems field.
Student involvement with projects will expose them to the usage of simulators and/or programming some types of networked embedded systems platforms.
Ability to identify the main issues and trade-offs in networked embedded systems; Understanding of the state-of-the-art solutions in the area; Based on the above understanding, ability to analyse requirements and devise first-order solutions for particular networked embedded systems problems; Familiarisation with a simulator platform and real hardware platforms for network embedded systems through the students involvement in projects.
ELEC5517 Software Defined Networks

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project Work - own time Prerequisites: ELEC3506 OR ELEC9506 Assessment: through semester assessment (60%) and final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study will introduce an emerging networking paradigm- Software Defined Networks (SDNs). By separating the control logics from the physical networks, the software defined networks allow an automated and programmable software program to logically control and manage the network. This unit introduces the basic principles of software defined networks, its architecture, abstraction, SDN programming, programmable control plane and data plane protocols, network update, network virtualisation, traffic management as well as its applications and implementations. Student will learn and practice SDN programming, testing and debugging on SDNs platforms through experiments and group projects. It is assumed that the students have some knowledge on data communications and networks.

Software Engineering

COMP5216 Mobile Computing

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: COMP5214 OR COMP9103. Software Development in JAVA, or similar introductory software development units. Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Mobile computing is becoming a main stream for many IT applications, due to the availability of more and more powerful and affordable mobile devices with rich sensors such as cameras and GPS, which have already significantly changed many aspects in business, education, social network, health care, and entertainment in our daily life. Therefore it has been critical for students to be equipped with sufficient knowledge of such new computing platform and necessary skills. The unit aims to provide an in-depth overview of existing and emerging mobile computing techniques and applications, the eco-system of the mobile computing platforms, and its key building components. The unit will also train students with hand-on experiences in developing mobile applications in a broad range of areas.
COMP5347 Web Application Development

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory, Project Work Prerequisites: INFO1103 or INFO1113 or COMP9103 or COMP9220 or COMP5028 Assumed knowledge: COMP9220 or COMP5028. The course assumes basic knowledge on OO design and proficiency in a programming language Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Nowadays most client facing enterprise applications are running on web or at least with a web interface. The design and implementation of a web application require totally different set of skills to those are required for traditional desktop applications. All web applications are of client/ server architecture. Requests sent to a web application are expected to go through the public Internet, which slows the responsiveness and increases the possible security threat. A typical web application is also expected to handle large number of requests coming from every corner of the Internet and sent by all sorts of client systems. This further complicates the design of such system.
This course aims at providing both conceptual understanding and hand-on experiences for the technologies used in building web applications. We will examine how data/messages are communicated between client and server; how to improve the responsiveness using rich client technology; as well as how to build a secure web application.
At the end of this course, students are expected to have a clear understanding of the structure and technologies of web applications. Students are also expected to have practical knowledge of some major web application environments and to be able to develop and deploy simple web applications. Cloud based platform are increasingly popular as the development and deployment platform. This course will incorporate the cloud aspect of web application development as well.
COMP5348 Enterprise Scale Software Architecture

This unit of study is not available in 2019

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Programming competence in Java or similar OO language. Capacity to master novel technologies (especially to program against novel APIs) using manuals, tutorial examples, etc. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics on software architecture for large-scale enterprises. Computer systems for large-scale enterprises handle critical business processes, interact with computer systems of other organisations, and have to be highly reliable, available and scalable. This class of systems are built up from several application components, incorporating existing "legacy" code and data stores as well as linking these through middleware technologies, such as distributed transaction processing, remote objects, message-queuing, publish-subscribe, and clustering. The choice of middleware can decide whether the system achieves essential non- functional requirements such as performance and availability. The objective of this unit of study is to educate students for their later professional career and it covers Software Architecture topics of the ACM/IEEE Software Engineering curriculum. Objective: The objective of this unit of study is to educate students for their later professional career and it covers topics of the ACM/IEEE Software Engineering curriculum.
COMP5349 Cloud Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The course will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5427 Usability Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Usability engineering is the systematic process of designing and evaluating user interfaces so that they are usable. This means that people can readily learn to use them efficiently, can later remember how to use them and find it pleasant to use them. The wide use of computers in many aspects of people's lives means that usability engineering is of the utmost importance.
There is a substantial body of knowledge about how to elicit usability requirements, identify the tasks that a system needs to support, design interfaces and then evaluate them. This makes for systematic ways to go about the creation and evaluation of interfaces to be usable for the target users, where this may include people with special needs. The field is extremely dynamic with the fast emergence of new ways to interact, ranging from conventional WIMP interfaces, to touch and gesture interaction, and involving mobile, portable, embedded and desktop computers.
This unit will enable students to learn the fundamental concepts, methods and techniques of usability engineering. Students will practice these in small classroom activities. They will then draw them together to complete a major usability evaluation assignment in which they will design the usability testing process, recruit participants, conduct the evaluation study, analyse these and report the results
ELEC5618 Software Quality Engineering

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: Writing programs with multiple functions or methods in multiple files; design of complex data structures and combination in non trivial algorithms; use of an integrated development environment; software version control systems. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will cover software quality planning, validation and verification methods and techniques, risk analysis, software review techniques, software standards and software process improvement and software reliability.
Students who successfully complete this unit will understand the fundamental concepts of software quality engineering and be able to define software quality requirements, assess the quality of a software design, explain specific methods of building software quality, understand software reliability models and metrics, develop a software quality plan, understand quality assurance and control activities and techniques, understand various testing techniques including being able to verify and test a unit of code and comprehend ISO standards, SPICE, CMM and CMMI.
ELEC5619 Object Oriented Application Frameworks

Credit points: 6 Session: Semester 2 Classes: Project Work - in class, Project Work - own time, Presentation, Tutorials Assumed knowledge: Java programming, and some web development experience are essential. Databases strongly recommended Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aims to introduce students to the main issues involved in producing large Internet systems by using and building application frameworks. Frameworks allow great reuse so developers do not have to design and implement applications from scratch, as students have done in ELEC3610 The unit lays down the basic concepts and hands on experience on the design and development of enterprise systems, emphasizing the development of systems using design patterns and application frameworks.
A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. A project-based approach will introduce the problems often found when building such systems, and will require students to take control of their learning. Several development Java frameworks will be used, including Spring, Hibernate, and others. Principles of design patterns will also be studied.
ELEC5620 Model Based Software Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories, Project Work - in class, Project Work - own time Assumed knowledge: A programming language, basic maths. Assessment: Through semester assessment (80%) and Final Exam (20%) Mode of delivery: Normal (lecture/lab/tutorial) day
Model-Based Software Engineering focuses on modern software engineering methods, technologies, and processes used in professional development projects. It covers both the pragmatic engineering elements and the underlying theory of the model-based approach to the analysis, design, implementation, and maintenance of complex software-intensive systems.
Students will participate in a group project, which will entail developing and/or evolving a software system, following a full development cycle from requirements specification through to implementation and testing using up-to-date industrial development tools and processes. At the end of the course they will provide a presentation and demonstration of their project work to the class. There is no formal teaching of a programming language in this unit, although students will be expected to demonstrate through their project work their general software engineering and architectural skills as well as their mastery of model-based methods and technologies.
Students successfully completing this unit will have a strong practical and theoretical understanding of the modern software development cycle as applied in industrial settings. In particular, they will be familiar with the latest model-based software engineering approaches necessary for successfully dealing with today's highly complex and challenging software systems.
The pedagogic grounds for this course and its focus on model-based approaches are to arm new software engineers with skills and perspectives that extend beyond the level of basic programming. Such skills are essential to success in software development nowadays, and are in great demand but very low supply. The dearth of such expertise is one of the key reasons behind the alarmingly high failure rate of industrial software projects (currently estimated at being greater than 40%). Therefore, this unit complements SQE and strengthens a key area in the program.

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