Postgraduate Diploma in Information Technology - October 2022 Intake only

The Postgraduate Diploma in Information Technology is a 120 credit programme, consisting of two x 20 credit compulsory courses, one x 20 credit elective course, and one x 60 credit Capstone final project.

A full-time student is expected to complete this programme in up to one year (36 weeks excluding study breaks). A part-time student is expected to complete this programme in up to two years (72 weeks excluding study breaks).

This programme has full-time and part-time study options available. If you are interested in studying this programme part-time, please speak to our admissions team. For further information email: admissions@whitecliffe.ac.nz

Programme Structure

Compulsory Courses (40 credits total)

• IT8801 Research Methods and Skills (20 credits)
• IT8802 Technology Management (20 credits)

Elective: Choose 1 of 4 courses (20 credits)

• IT8803 Cyber Security (20 credits)
• IT8804 Data Analysis (20 credits)
• IT8805 Ubiquitous Computing and Intelligent Systems (20 credits)
• IT8806 Machine Learning (20 credits)

Compulsory Capstone Final Project (60 credits)

• IT8807 Applied Project (60 credits)

Research Methods and Skills (20 credits)

Students will be equipped with the knowledge and skills to gain an understanding of quantitive and qualitative methods of conducting meaningful enquiry and research. This course will help with the development of skills to recognise and reflect on the strengths and limitations of different research methodologies in the existing literature. Students will also learn the skill of developing a hypothesis through critical assessment of research and consider ethical and practical aspects.

Students will learn how to formulate research problems, related questions, key steps, and how to frame research problems with the correct research methodologies. They will learn a step-by-step approach to understand how the hypothesis can be tested to prove its validity. This course also covers design thinking and statistical techniques used for analysis.

Technology Management (20 credits)

This course is intended for experienced professionals to establish themselves in a leadership position, or for those who are embarking on their career and need deep knowledge of the major functions of Information Technology to help assist them to choose a specialism.

It covers Information Technology from the commercial stance of operational execution for overall business success. It adopts a "funnel" approach, from high-level strategy into more targeted execution, delivery, and risk management challenges.

Assessments in this course include a high-level business case report analysing the key risks, opportunities, regulatory exposure, or other stimuli for change, in the context of the chosen organisation. The project plan details the implementation of change management in alignment with business and technology strategies in the context of the chosen organisation.

As part of the programme structure, you are required to choose one elective out of the four options: Cyber Security, Data Analysis, Ubiquitous Computing and Intelligent Systems, or Machine Learning.

Cyber Security (20 credits)

Cyber Security is a growing area in the IT industry. This course will follow the approach as defined by the New Zealand Protective Security Requirement Framework (PSR) to manage security in a hierarchical manner. It covers cyber security from governance to technical understanding and highlights security frameworks and best practices.

This course provides an understanding of both theoretical and practical paradigms. It develops skills to identify, assess, prevent, plan, and respond to cyber security threats and issues. This course equips students with an understanding of cyber security concepts, technical abilities, and hands-on knowledge to manage information and systems. It provides knowledge and skills to perform risk assessments, mitigation and management, security threat identification, presentation, and detection of vulnerabilities.

By completing this course, students learn the following industry skills:

• Understanding the New Zealand Protective Security Requirement Framework and its hierarchical approach
• Understanding of IT security knowledge to create and assess risk register, risk assessment, and mitigation
• Learn and develop skills for assessment, planning, and implementing cyber security best practices and frameworks for business needs
• Recognition, classification, and identification of security-related incidents
• Develop skills to apply knowledge through industry best practices, teamwork, and innovation to solve complex security problems
• Understand legal, organisational, and industry regulatory requirements, use professional and ethical practices and advise decision-makers on cyber security implications and related organisational obligations

Assessments in this course include a risk assessment report in terms of threats, vulnerabilities, impact, likelihood, and risk control recommendations for a chosen organisation. It also includes a detailed evaluative report on the use of public or private cloud in terms of security, affordability, and scalability. This detailed report will compare security features and best practices available and will include a secure and reliable solution as an incident response plan for the chosen organisation.

As part of the programme structure, you are required to choose one elective out of the four options: Cyber Security, Data Analysis, Ubiquitous Computing and Intelligent Systems, or Machine Learning.

Data Analysis (20 credits)

The course goal is to develop analytical skills and a data mindset for learners. Therefore, the course will embrace the critical and logical thinking that a data analyst should have, as well as the models and technology that support the process of data analysis. Students will have opportunities to practice applying various machine learning tools and the most well-known web analytical tool - Google Analytics - to solve real-life problems. This will give students a comprehensive understanding of data analysis not just in theory but also in practice.

Before starting to analyse data, students are enabled to understand what the data is about to detect the type of data, and choose the appropriate techniques and models for analysis. Understanding the data includes recognising the trend, and the outliers, and discovering the relationships between variables. For deeper and thorough thinking, students will be challenged to create hypotheses for the future of data or the whole population and then evaluate the hypotheses.

The main job of a data analyst is to analyse data and that majorly is statistical modelling. This is what makes the analyst different from other positions. By modelling the data, students would be able to deeply understand and interpret what the data is implying. In the first step of understanding data, students would be able to categorise what type the data is. This will be helpful to choose the appropriate models that run best on the data. Modelling not only helps with analysing the data but also with foreseeing the data, giving solutions to the problem, and evaluating the solutions.

Data analysis would not mean much to businesses and organisations if the analyst is not able to interpret data in a simple basic language and visualise that to a non-statistics user. Therefore, visualisation tools are necessary for this course. The two most common and comprehensive tools for data scientists are Excel Visual Basic for Applications and Tableau. These tools are designed in different ways whereas both can do similar works such as wrangling, filtering, and creating charts. Knowing how to use these two tools would be very helpful for students when entering the industry.

Following the evolution of data science and the technology industry is the development of a web analytical tool that is becoming more popular nowadays. This machine learning tool saves a lot of time and costs in the analysing process. The ability to use this tool is also a common requirement in a data analysis job. Therefore, this course will provide the opportunity to be familiar with and practice this tool.

After the course, students will have reasonable experience with the analytical skills and mindset of a data analyst.

Assessments in this course include an analytical report using data analysis tools and an analytical report using web analytics. Analytical reports using data analysis tools shall include the application of regression and classification models for data analysis, visualisation summary of the data, and any recommendations. Analytical reports using web analytics include the application of features of web analytics tools such as data collections, management, analytics intelligence, reporting, data analysis along with the visualisation summary of data.

As part of the programme structure, you are required to choose one elective out of the four options: Cyber Security, Data Analysis, Ubiquitous Computing + Intelligent Systems, or Machine Learning.

Ubiquitous Computing + Intelligent Systems (20 credits)

This course is designed for someone who wants to have specialised knowledge of how computer systems can be infused into the physical world and human and social environments. Contents covered in this course are concerned with developing situated and pervasive technology that can be used seamlessly by humans considering the harmony of human and social environments. It will enable the learner to keep abreast of the latest developments and industry practices. This course will not only enable the learner to achieve a cross-disciplinary exchange of ideas with specialised knowledge but will also develop the key skill of design by covering mobile services, sensor networks, context-aware computing, intelligent systems, and the Internet of Things.

It initially provides the vision of Ubiquitous computing by illustrating real-world applications and scenarios and then covers the holistic framework (Smart DEI) which is linked with the core internal properties, external interactions, and architectural design of Ubiquitous computing systems. It then covers the service provision lifecycle (service announcement, discovery, selection, and configuration).

As with recent developments in electronic components, they are getting smaller, faster, and cheaper. They can be deployed pervasively at a massive scale for many industrial applications driven by a need to enrich humans to be better informed about any human-computer interactions. The next selection in this course covers tagging, sensing, and controlling smart objects to model context-aware systems.

It is important to understand that Autonomy is one of the core properties of Ubiquitous computing systems that enable systems to operate independently without external interventions and this is a requirement in almost every computing field nowadays that will eventually make IT an invisible component. This course covers different architectures of Intelligent Systems such as the Reactive Intelligent System models, Environment Model-based Intelligent Systems, Goal-based Intelligent Systems, Utility-based Intelligent Systems, Learning-based Intelligent Systems, and Hybrid Intelligent Systems.

Finally, it covers different communication protocols and an Operating System for small, low-powered devices to connect them to the Internet.

Assessments in this course are designed to measure the achievement of learning outcomes by the students. These assessments gauge the ability of students to model the key Ubiquitous Computing properties of a system in any given scenario. It also measures the ability of students to utilise and evaluate different interaction design models to meet real-world requirements along with the skills of designing a blueprint for the development of actual systems. In the research and requirements assessment, functional and non-functional requirements analysis is performed that may include physical, virtual ICT, or human environment. The evaluative design solution report includes a Ubiquitous Computing System architecture based upon the requirement analysis. It covers the design issues, design constraints and choices, and a description of how a part of this solution can be implemented using some mobile device software API. Note this does not mean implementing the application but relating the application design model to specific device APIs, to explain how a main part of the design could be implemented using the APIs/programming.

As part of the programme structure, you are required to choose one elective out of the four options: Cyber Security, Data Analysis, Ubiquitous Computing + Intelligent Systems, or Machine Learning.

Machine Learning (20 credits)

This course is designed to enable a learner to acquire skills in machine learning for solving real-world problems. Contents covered in this course open the learner to the idea of real-world scenarios where machine learning can be implemented for solving problems efficiently and optimally. This course will introduce the learner to the mathematical/statistical background of the models. This knowledge will enable a learner in the selection of an appropriate machine learning model for a given problem. How to implement an algorithm for solving a given problem and then how to interpret the performance of the algorithm. Also, the learner should be able to conclude from the algorithm results and convert the algorithm or model into an application.

The course initially broadens the horizon of the learner by introducing a holistic view of the domains where machine learning is helping humans make a better-informed decision. A general introduction of machine learning and its two sub-domains of supervised and unsupervised learning are discussed.

It then covers some of the most deployed models of supervised learning like linear regression, logistic regression, and the concept of model generalisation. The learner is then exposed to Neural Networks and how multi-class classification can be achieved. Back-propagation and the issues of random initialisation are discussed.

The second major knowledge area covered in this course is related to unsupervised learning. For this, the support vector machine is introduced to the learner and a detailed discussion regarding optimisation and kernels entails. To reduce the time complexity of algorithm implementation, concepts of dimensionality reduction algorithms like PCA and info gain are also discussed. Gaussian Distribution and Anomaly Detection systems complete the discussion for unsupervised learning.

The utility of machine learning increased as it can help us in making a better-informed decision which can be achieved by developing Recommender Systems. Content-based recommendation systems, as well as collaborative filtering-like topics, will be covered for developing an understanding of Recommender Systems.

In the end, it covers various large dataset-related machine learning algorithms for enabling the learner to work with larger datasets.

Assessments in this course include an evaluative report of machine learning-based solutions to real-world scenarios. This report will cover the description of the real-world problem, the model proposed, and the justification for the model being able to help solve the real-world problem. It should also cover an analysis of how to convert the model trained into a usable application. The second assessment in this course is a solution recommendation presentation to cover the rationale behind the model selection, to understand the statistical relationships, and a conclusion on the model performance.

Capstone/Applied Project (60 credits)

The Postgraduate Diploma in Information Technology culminates in a 60-credit capstone project. The applied project is a core course, and it is informed by the student's research/applied interests. The course enables students to propose, research, and present a novel situation utilising advanced information technology principles and techniques. Students manage the project and associated risks from inception to implementation, appraising, justifying, and mitigating decision-making.

The applied project provides an opportunity for critical analysis and application of learning informed by current research to an industry issue or scenario. Students must secure agreement from a collaborating organisation, identify an issue, select and apply research, prepare a project plan, analyse and apply critical thinking to organisational problems to deliver evidence-based strategic recommendations. Students will report findings to selected stakeholders. Throughout the process, students will reflect on the learning experience, including performance and capability for example to, think critically and strategically, manage, problem solve, design strategy, and present findings. The learning within the applied project course is strongly influenced by current business practices.

This course focuses on a supervised evidence-based project under the supervision of an academic supervisor. The topic of the project will be agreed upon between the student and the academic supervisor. A comprehensive literature analysis will be performed before the presentation and approval of the project proposal. This project will result in a scholarly project report to reflect the project outcome(s).

In order to achieve the learning outcomes, this course not only covers the aspects of detailed literature analysis and investigating the solution of a real-life applied problem with the appropriate research methods and techniques but will also help students to develop the skill of engaging in a process of independent critical enquiry around a specific issue. This course will help in developing the skills of being a Tech Lead by managing the technical aspects of the applied research project. Students will reflect on the project findings in the evidence-based project and/or prototype.

In the applied project, students will be managing project timelines, goals, and milestones using project management methodologies such as agile or similar methods. They will actively be assessing and managing risk, managing resources, and researching and identifying opportunities for solutions in the IT sector. This will mean collaborating and communicating with industry partners, target audiences, and other stakeholders. They will learn development and improvements that will require managing change in the lifecycle of an IT project when designing solutions.

Stakeholder Benefits

The applied project course is designed to contribute positively to all stakeholders. For organisations, work-integrated courses provide a pool of qualified and experienced personnel as future staff or contractors, as well as support personnel during the course. The professionalism and skill base of the industry is enhanced. For the students, work-integrated courses provide relevant industry experience and professional networks and contacts. The applied project provides another avenue to maintain industry connections and track students and graduates against graduate capabilities.

Assessments in this course include an evidence-based project proposal to cover the scoping of the applied project, a project report to cover the detailed analysis and design of the solution along with a proof of concept evidence, and a reflective oral presentation. The presentation will project the opportunities for implementation in the future industry applications.

NZ Government key information link for students, that provides more information to support your decision making for this programme

Domestic Student Entry Requirements:

• Completed application form
• Applicant must have one of the following:
  • A recognised Bachelor's degree in Information Technology or a related discipline with a B- or higher average, OR
  • A Bachelor's degree (Honours) in Information Technology or a related discipline, OR
  • Graduate Certificate or Graduate Diploma in Information Technology or related field
• Special consideration may be offered for admission by the Deputy Chief Executive Academic based on evidence of academic capability and work experience

International Student Entry Requirements:

• Completed application form
• Passport copy
• Statement of purpose
• IELTS Academic overall score of 6.5 with no band less than 6.0 or equivalent
• Applicant must have one of the following:
  • A recognised Bachelor's degree in Information Technology or a related discipline with a B- or higher average, OR
  • A Bachelor's degree (Honours) in Information Technology or a related discipline, OR
  • Graduate Certificate or Graduate Diploma in Information Technology or related field
• Special consideration may be offered for admission by the Deputy Chief Executive Academic based on evidence of academic capability and work experience

BYOD (Bring Your Own Device) Requirements:

This programme has Bring Your Own Device (BYOD) requirements, we recommend you follow the specifications listed as these will support you to be successful in your studies.

Required IT Specifications:

• The latest Windows is the recommended operating system, with regular updates across the programme of study.

Recommended IT Specifications:

Minimum Hardware requirements 

• Intel® or AMD processor with 64-bit support; 2 GHz or faster processor with SSE 4.2 or later 
  
• 16 GB RAM 
• 500 GB SSD or higher hard drive with 10GB free space minimum    
• Wireless capability 802.11n dual-band    
• Up-to-date antivirus software    

Minimum Operating System     

• Windows 10 (64-bit) version 1809 or later; LTSC versions are not supported (a must for IT Students) 
  OR     
• Intel i5 or equivalent or above
• AMD Ryzen5 or equivalent processor 

• Internet and unlimited data plan recommended.    

Not Supported:    

• Chromebooks    
• Windows X or Windows S OS    
• Tablets (except Windows Surface Pro or iPad Pro)