Master of Information Systems

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  • Mode of deliveryOn Campus

  • AQF Level9

  • Duration2 Years
    Full-Time

  • LocationSydney NSW
    Melbourne VIC

  • Course Fees See Fees

  • Upcoming Intake Date 6 May 2024, 15 July 2024

Course Description

The proposed Master of Information Systems (MIS) program is designed to meet the Australia Computer Society (ACS).

The course aims to produce Information Systems (IS) graduates who will be eligible to gain employment in a range of different and modern areas of information technology and information systems.

The course is built on providing students with foundational knowledge of core and key areas of information technology and systems. Specialisation units in the second year provide opportunities for study in current important IS areas including cyber security, data analytics and artificial intelligence.

The course is characterised by an end of program capstone project comprising two connected units. These capstone units provide students with the opportunity to work in a team to solve a complex real-world business information systems problem, drawing from the knowledge and skills acquired over the whole program of study.

Learning Outcomes

Graduates of the Master of Information Systems will have demonstrated achievement of the following Course Learning Outcomes and be able to:

  • CLO 1: Apply advanced and contemporary information systems knowledge and skills to business and organisational problems.

  • CLO 2: Critically evaluate opportunities and challenges in the development of information systems in organizations and society.

  • CLO 3: Create justifiable and creative solutions to complex information systems problems through research and use of industry standard methodologies.

  • CLO 4: Communicate complex information system concepts and solutions effectively to both technical and non-technical stakeholders.

  • CLO 5: Apply and integrate ethics, sustainability, security and privacy principles into information system solutions.

Career Opportunities

The MIS aims to provide graduates to the Professional, Scientific and Technical Services industry, particularly Computer Systems Designs and Related Services.

In Australia, these are associated with the ANZSCO code minor groups of:

  • 261 Business and Systems Analysts, and Programmers

  • 262 Database and Systems Administrators, and ICT Security Specialists

  • 224 – Information and Organisation Professionals

The MIS has been designed to initially focus on the modern areas with information systems of Cyber Security, Data Analytics and the emerging area of Artificial Intelligence (AI).

For those opting to undertake the Data Analysis specialisation, this has been specifically designed to meet the needs of those entering this growing field. Recently, Australia formally recognised this area as an occupational area (ANZSCO Code: 224999 – Information and Organisation Professionals nec (Data Scientist)). The Cyber Security specialisation focuses on the skills and knowledge needed from those entering the field of cyber security (ANZSCO code 262112: ICT Security Specialist).

Master of Information Systems graduates may seek employment in a number of information systems and information technology related areas including:

Career outcomes for Apex Master of Information Systems graduates include:

  • Cyber Security Analyst
  • Data Analyst
  • IT Business Analyst and Consultant
  • Information System Manager
  • IS Project Manager
  • Software Tester
  • Systems Analyst
  • Technical Support Officer
  • Machine Learning Engineer
  • Artificial Intelligence developer/ engineer/architect

Course Duration

Full Time

The course is 2 years full-time comprising of 16 units (160 Credit Points) where a full-time student undertakes 4 units of study per study period.

International students on a student visa are required to study full time (i.e. must complete a minimum of 1.0 Equivalent Full Time Study Load (EFTSL) of study per year). At APEX, 1.0 EFTSL is defined as 80 credit points a year (8 units where each unit is 10 credit points).

Part Time

The course can be undertaken part-time for domestic students only. The course is 4 years part-time comprising of 16 units (160 Credit Points).

A study period means the intake semester (or intake Summer School) and each subsequent semester.

Units and Study Load

The course comprises of 16 units each of 10 credit points. Students will study 10 core units, 4 specialisation units and 2 unrestricted electives.

Full-time students undertake 4 units of study during a study period. Each unit involves 12 hours of study per week, comprising of 3 hours face-to-face teaching and 9 hours of independent learning per unit.

Full-time students undertaking 4 units per study period will have 12 hours face-to-face teaching and 36 hours of independent study each week. A study period means the intake semester (or intake Summer School) and each subsequent semester.

First Study Period

Unit Description

This unit introduces students to the key concepts and technologies of information systems and computer networks that drive efficiency and effectiveness in modern organisations. Students will gain a clear understanding of modern information system componentry and how computer networks are constructed and operate. They will also consider both the benefits and inherit risks of cloud computing, ubiquitous computing, the Internet of Things, and artificial intelligence. Students will gain an understanding of privacy, ethical, and security issues associated with modern information systems. Finally, students will develop their research and critical analysis skills and be able to apply these skills to critique information sources and report their findings in an academically sound manner.

Unit Description

In this unit students are equipped with a strong foundation in programming concepts and techniques using Python. Throughout the unit, students will develop skills in designing, coding, and testing of both procedural and object-oriented programs to solve problems. In addition, students will learn to use and differentiate both procedural and object-oriented programming paradigms. Students will learn to apply appropriate data structures and algorithms to solve programming tasks and use Python packages in data analysis to create well-structured and documented Python programs. Finally, students will learn how to apply appropriate testing and exception handling techniques to ensure their programs are robust and efficient.

Unit Description

In this unit students will learn how to utilise the industry standard traditional and agile approaches in project management to effectively manage information systems (IS) projects. Students will analyse and evaluate different project management methodologies, and design and develop a project plan and schedule for an IS project. Students will learn how to apply IS project management risk management principles, and evaluate/select appropriate IS project management tools and technologies. Students will also learn the importance and appropriate strategies of effective communication with stakeholders.

Unit Description

Digital information systems produce vast amounts of data, and appropriate management of this data is essential for decision making and value-adding. This unit introduces the fundamental concepts in database design and development, covering the conceptual level and physical level of database management systems (DBMS). In this unit students will learn how to model business data using standard data modelling methodologies and apply this for the conceptual, logical, and physical design of relational databases. Students will also learn to apply industry standard languages and approaches to create, query and manipulate databases. The practical challenges involved with deploying database management systems such as database performance tuning, transaction management, Internet connectivity, and security are also covered. Contemporary approaches and technologies for the storage and retrieval of semi-structured and unstructured data are examined.

Second Study Period

Unit Description

Digital information systems produce vast amounts of data, and appropriate management of this data is essential for decision making and value-adding. This unit introduces the fundamental concepts in database design and development, covering the conceptual level and physical level of database management systems (DBMS). In this unit students will learn how to model business data using standard data modelling methodologies and apply this for the conceptual, logical, and physical design of relational databases. Students will also learn to apply industry standard languages and approaches to create, query and manipulate databases. The practical challenges involved with deploying database management systems such as database performance tuning, transaction management, Internet connectivity, and security are also covered. Contemporary approaches and technologies for the storage and retrieval of semi-structured and unstructured data are examined.

Unit Description

Information Systems operations and service management is critical for every business, especially those concerned with delivering value to customers. This unit introduces students to the principles, tools, quantitative models, and strategies used in the IS Operations and Service and examines key issues facing both service and manufacturing organizations. In this unit, students develop an understanding of product and service design, technology-enabled innovations, process design, operations planning and control, quality, performance, and IT service delivery. Students are equipped with the basic skills and techniques to analyse operations in a digitalized world and methodology and approaches to improve these. In addition, this unit explores the concepts of information technology enabled innovations and IT services and equips students with an ability to evaluate, implement and manage enabling technologies in business operations. Students will also be introduced to important and emerging standards and practices, such as ITIL and cloud-based tools for operations and service management.

Prerequisites

MIS5101 Information Systems and Networks

Unit Description

This unit provides students with a comprehensive understanding of the key concepts and principles of cyber security. The course covers a range of topics, including the fundamentals of network security, cryptography, cyber threats and attacks, risk management, and incident response. Students will learn about the latest technologies and techniques used to protect computer networks and systems from cyberattacks, as well as the legal and ethical issues related to cyber security. Students will gain key insights into identifying and mitigating security risks, approaches to designing secure systems, and implementing best practices in cyber security. By the end of the unit, students will have a solid foundation in cyber security that can be applied to a range of industries and roles, from IT professionals to business managers and executives.

Prerequisites

MIS5104 Database Systems

Unit Description

This unit provides students with an overview of the complex area of data analytics. Students will learn to apply key statistical approaches and draw inferences from sample data. They will also learn the underpinning approaches used in modern data analytics systems and be able to use and critically assess a range of data analytics and visualisation tools to interpret data sets. Students will learn about potential bias in data sets and be able to assess the quality and integrity of data sources, as well as the social and ethics dimension of large-scale collection and use of personal and behavioural data.

Third Study Period

Prerequisites

60 credit points inclduing MIS5105 Systems Analysis and Design

Unit Description

In MIS5203 Applied IS Project – Part A, combined with the ensuing unit MIS5320 Applied IS Project – Part B, students will demonstrate application of the knowledge and skills gained throughout the course by tackling the development of a solution to a complex business IS problem.

In this unit students will address a real-world complex business IS problem. By applying industry standard and modern IS iterative and incremental Project development methodology such as UP (Unified Process) Model. Students will elucidate the business problem and associated imperatives/drivers. The students will develop a set of justifiable system requirements and specifications that define the system to be built. Students will assess various technologies and/or approaches to determine a feasible approach for the creation of a solution. Finally, the students will create a prototype demonstrating the concept of the proposed solution and a system design for the Minimum Viable Product (MVP). Students also will consider and address any ethical, sustainability, security and/or privacy impacts of potential solutions.

Specialisation Unit 1 (Intermediate or Advanced Level)
Specialisation Unit 2 (Advanced Level ie. MIS53XX)
Postgraduate Elective Unit 1 (Foundational, Intermediate or Advanced Level)

Fourth Study Period

Specialisation Unit 3 (Advanced Level ie. MIS53XX)
Specialisation Unit 4 (Advanced Level ie. MIS53XX)
Postgraduate Elective Unit 2 (Foundational, Intermediate or Advanced Level)
Prerequisites

100 credit points including MIS5103 Project Management for IS, MIS5106 IS Operations and Service Management, MIS5203 Applied IS Project – Part A, and 1 specialisation unit

Unit Description

This unit follows from MIS5203 Applied IS Project – Part A and together these demonstrate the ability of a student to develop a solution to a complex business IS problem.

In this unit the students start by reviewing the set of system requirements and specification together with a design for a Minimum Viable Product (MVP) that was previously developed in the preceding unit. Students have an opportunity to refine the specifications and apply the Unified Process (UP) Construction & Transition phases of the iterative and incremental project development Methodology to test their design based on further considerations and stakeholder feedback. The design of the MVP is refined and a project plan for the implementation of the MVP is created during the Transition phase to ensure that the functional requirements have been addressed and documented properly. The MVP is then implemented with feedback from stakeholders prior to implementation of the full system. Students will document and present the system along with a detailed project report to explain the operation of the system and how it addresses the business problem. Students will justify choices made and approaches taken, as well as discuss the methods/techniques adopted in the development of the IS solution.

Cyber Security Specialisation Units

Prerequisites

MIS5201 Cyber Security

Unit Description

This unit follows from MIS5203 Applied IS Project – Part A and together these demonstrate the ability of a student to develop a solution to a complex business IS problem.

In this unit the students start by reviewing the set of system requirements and specification together with a design for a Minimum Viable Product (MVP) that was previously developed in the preceding unit. Students have an opportunity to refine the specifications and apply the Unified Process (UP) Construction & Transition phases of the iterative and incremental project development Methodology to test their design based on further considerations and stakeholder feedback. The design of the MVP is refined and a project plan for the implementation of the MVP is created during the Transition phase to ensure that the functional requirements have been addressed and documented properly. The MVP is then implemented with feedback from stakeholders prior to implementation of the full system. Students will document and present the system along with a detailed project report to explain the operation of the system and how it addresses the business problem. Students will justify choices made and approaches taken, as well as discuss the methods/techniques adopted in the development of the IS solution.

Prerequisites

MIS5201 Cyber Security

Unit Description

This unit centres on the cyber security incident response process and is designed to equip students with the skills and knowledge to effectively manage cyber security incidents. The course will cover the entire incident response process, including identifying, containing, analysing, eradicating, and recovering from cyber security incidents. Students will learn to propose and justify appropriate response strategies towards different types of cyber security incidents, such as malware infections, network intrusions, data breaches, and denial-of-service attacks.

The unit will also focus on evaluating the role of incident response teams and stakeholders, including legal and regulatory bodies, law enforcement agencies, and third-party service providers, in managing cyber security incidents. Students will learn how to select and apply appropriate cyber security incident response tools and technologies, such as forensic tools, network analysis tools, and incident response platforms. Additionally, they will develop effective communication and leadership strategies to manage cyber security incidents, including the development of incident response and stakeholder communication plans.

Prerequisites

MIS5201 Cyber Security

Unit Description

This unit focuses on software security and is designed to provide students with the skills and knowledge to identify and mitigate security risks and vulnerabilities associated with software development. The course will cover various security risks and vulnerabilities associated with software development, including code-level vulnerabilities, design flaws, and configuration errors. Students will learn to develop strategies to address potential security risks and vulnerabilities in software development projects.

The unit will also focus on selecting and justifying a range of secure software development practices, including threat modelling, secure coding, security testing, and vulnerability management in different real-world scenarios. Students will learn to apply security tools and technologies such as static and dynamic analysis tools, penetration testing tools and security libraries, and to identify and mitigate security risks during the software development lifecycle.

Prerequisites

MIS5201 Cyber Security

Unit Description

This unit focuses on software security and is designed to provide students with the skills and knowledge to identify and mitigate security risks and vulnerabilities associated with software development. The course will cover various security risks and vulnerabilities associated with software development, including code-level vulnerabilities, design flaws, and configuration errors. Students will learn to develop strategies to address potential security risks and vulnerabilities in software development projects.

The unit will also focus on selecting and justifying a range of secure software development practices, including threat modelling, secure coding, security testing, and vulnerability management in different real-world scenarios. Students will learn to apply security tools and technologies such as static and dynamic analysis tools, penetration testing tools and security libraries, and to identify and mitigate security risks during the software development lifecycle.

Data Analytics Specialisation Units

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit is designed to provide students with a comprehensive understanding of data mining techniques and their applications in real-world scenarios. The unit covers the fundamental concepts and techniques of data mining, including data pre-processing, pattern recognition and predictive modelling. Students will learn how to apply data mining techniques and tools to store and analyse complex datasets, including relational and non-relational approaches for structured and unstructured data. They will also evaluate the strengths and limitations of different data mining algorithms and methodologies and choose the most appropriate approach for a given problem based on data characteristics, domain knowledge, and performance metrics.

The unit will equip students with the ability to develop effective data mining strategies and plans for different types of business applications such as customer segmentation, market basket analysis, churn prediction, fraud detection and social media analysis. Additionally, students will learn how to assess a range of data mining approaches to generate meaningful insights and actionable recommendations for decision-making using cloud and big data technologies. By the end of the unit, students will have the necessary skills and knowledge to apply data mining techniques to real-world problems and generate valuable insights that can inform decision-making and drive business growth.

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit is designed to provide students with a comprehensive understanding of data mining techniques and their applications in real-world scenarios. The unit covers the fundamental concepts and techniques of data mining, including data pre-processing, pattern recognition and predictive modelling. Students will learn how to apply data mining techniques and tools to store and analyse complex datasets, including relational and non-relational approaches for structured and unstructured data. They will also evaluate the strengths and limitations of different data mining algorithms and methodologies and choose the most appropriate approach for a given problem based on data characteristics, domain knowledge, and performance metrics.

The unit will equip students with the ability to develop effective data mining strategies and plans for different types of business applications such as customer segmentation, market basket analysis, churn prediction, fraud detection and social media analysis. Additionally, students will learn how to assess a range of data mining approaches to generate meaningful insights and actionable recommendations for decision-making using cloud and big data technologies. By the end of the unit, students will have the necessary skills and knowledge to apply data mining techniques to real-world problems and generate valuable insights that can inform decision-making and drive business growth.

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit centres on developing advanced skills in predictive analytics and data storytelling, providing students with the ability to predict future states of complex datasets, communicate insights through data visualisation techniques, and understand ethical and legal considerations in predictive analytics. Students will study mathematical and statistical models using regression analysis and time series forecasting, enabling them to predict future states of complex, real-world datasets. Students will also learn to compare and apply a range of predictive analytics and visualisation approaches to explore and solve diverse real-world problems. They will develop skills in formulating communication strategies for data insights using effective data visualisation techniques to assist decision-makers in understanding complex data.

Furthermore, this unit will explore the capacity of data storytelling techniques to communicate complex data insights to a non-technical audience. Students will develop an understanding of ethical and legal considerations in predictive analytics. They will learn to develop strategies for ethical data use and management, including the mitigation of data privacy concerns and algorithmic bias. Graduates of this unit will have the skills and knowledge to apply advanced analytics techniques to solve real-world problems and communicate insights to both technical and non-technical stakeholders.

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit will equip students with the skills and knowledge required to effectively use data analysis techniques to understand and influence customer behaviour in the context of social media and websites. Through the unit, students will develop an understanding of how to create useful insights for marketing, reputation management and customer service purposes using sentiment analysis techniques. They will also learn how to analyse social network data using large graphs including network graphs and centrality measures, and how network analysis can be used for influencer marketing, viral marketing, and brand awareness.

Students will also gain expertise in designing and implementing A/B tests for website optimisation using appropriate analytics tools. The unit will cover machine learning techniques such as clustering and classification that can be used to predict social/web customer behaviour. Additionally, students will learn how to assess the data requirements to undertake web scraping and associated large-scale data collection and pre-processing approaches while considering the ethical and legal considerations of web scraping. Overall, this unit will provide students with a deep understanding of the data analysis techniques required to effectively influence customer behaviour in today’s data-driven business world.

Artificial Intelligence Units

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit is designed to provide students with a comprehensive understanding of data mining techniques and their applications in real-world scenarios. The unit covers the fundamental concepts and techniques of data mining, including data pre-processing, pattern recognition and predictive modelling. Students will learn how to apply data mining techniques and tools to store and analyse complex datasets, including relational and non-relational approaches for structured and unstructured data. They will also evaluate the strengths and limitations of different data mining algorithms and methodologies and choose the most appropriate approach for a given problem based on data characteristics, domain knowledge, and performance metrics.

The unit will equip students with the ability to develop effective data mining strategies and plans for different types of business applications such as customer segmentation, market basket analysis, churn prediction, fraud detection and social media analysis. Additionally, students will learn how to assess a range of data mining approaches to generate meaningful insights and actionable recommendations for decision-making using cloud and big data technologies. By the end of the unit, students will have the necessary skills and knowledge to apply data mining techniques to real-world problems and generate valuable insights that can inform decision-making and drive business growth.

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit covers a range of advanced Natural Language Processing (NLP) topics, including text pre-processing, deep learning for NLP, and ethical considerations in NLP practices. In this unit, students will gain a strong understanding of the fundamental NLP concepts and techniques such as tokenisation, stemming, and lemmatisation and how to apply them to various NLP tasks. They will also explore the latest developments in deep learning for NLP and evaluate the performance and accuracy of models for tasks such as text classification, named entity recognition, and machine translation.

In addition, students will learn how to analyse text data using NLP techniques such as sentiment analysis, topic modelling and text summarisation, and understand how NLP can be used for social media analytics, customer feedback analysis, and content generation. They will also evaluate the ethical considerations involved in NLP practices, such as bias, privacy, validation, and explainability. By the end of the unit, students will have gained practical experience in applying large language models to solve NLP tasks and will understand how they can improve AI outcomes in a range of contexts.

Prerequisites

MIS5202 Data Analysis

Unit Description

The unit emphasises the impact of intelligent systems on information systems architectures, designs, and implementations. Students will examine the fundamental concepts of intelligent systems, software bots and robotic process automation and explore how they can be integrated into various information systems. The unit will cover a range of topics including intelligent decision-making, automation of routine tasks and user experience enhancement. Students will also investigate the potential benefits of intelligent systems in enhancing productivity, reducing costs and expanding intelligent system functionality in areas such as computer vision.

Students will evaluate the techniques that intelligent agents and chatbots use to interact with users in natural language and assist them in performing various tasks such as customer service, sales support, and knowledge management. They will also develop policies and guidelines that can ensure the responsible and ethical use of intelligent systems technologies in business environments. The unit will provide students with the necessary knowledge and skills to analyse the impact of robotics and intelligent systems on future information systems architectures, designs, and implementations. Upon completion of the unit, students will be able to apply these concepts to real-world situations and make informed decisions about the use of intelligent systems technologies in their organisations.

Prerequisites

MIS5202 Data Analysis

Unit Description

This unit aims to provide students with a thorough understanding of fundamental concepts in autonomous systems, including how computer vision and deep learning models can be applied to image and video analysis subtasks. Students will learn about image formation, feature extraction, object recognition and motion analysis, and how these concepts can be used for various image and video analysis tasks in developing autonomous systems. They will gain experience in interpreting these concepts and applying them to practical problems in image and video analysis such as face detection, image restoration and video summarisation. Students will also learn how computer vision techniques can be used for applications such as surveillance, medical imaging and autonomous driving.

Students will assess the performance and accuracy of a range of deep learning models for autonomous systems subtasks such as object detection, semantic segmentation, and action recognition. They will gain practical experience in analysing image and video data using computer vision techniques and applying deep learning models to solve complex problems. Additionally, students will learn about the ethical considerations involved in image and video analysis such as privacy, bias, and explainability and how to create strategies for ethical image and video analysis practices. Finally, students will recommend real-time image and video analysis approaches appropriate to a range of situations that require real-time data processing, including video surveillance and augmented reality.

Admission Criteria

Specific Course Admission Criteria for Master of Information Systems: None

General Academic Admission Criteria applies. You are required to read the Admission Criteria and ensure you satisfy the requirements before you apply.

Admission Criteria

Course Fees

Standard Fees

Tuition Fee: AUD $48,000

Semester Indicative Fee: AUD $12,000*

Enrolment Fee:AUD $250

* Indicative fees based on 4 units per semester.

Note: Scholarships may apply.

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