Advanced Computer Science with Advanced Practice MSc
2 Years Full-Time | September or January Start

In this module you will enhance your Software Engineering skills using an object oriented methods to develop applications. The module has a strong focus on current professional best practice, and is written to support the needs for professional agile development. You will learn about programming design patterns which can be applied to the Presentation, Domain and Data Access layers of an application and technologies such as Object Relational Mapping that allow the domain layer to remain object orientated while storage uses a relational model. Automated testing is also covered as it supports agile development. The module will also cover application level security and discuss design and implementation issues in relation to access control and data security.

The module will cover the following topics:

1. Basic anatomy of the programming language
2. Abstract Data type, Immutable objects and Encapsulation
3. Presentation Design Patterns
4. Design Principles (e.g., Open-Closed, Dependency Inversion, Single Responsibility etc.)
5. Creational Design Patterns and their Implementation
6. Behavioural Design Patterns and their Implementation
7. Structural Design Patterns and their Implementation
8. Traditional Data Access including data access layer patters.
9. Object Relational Mapping
10. Testing using unit testing frameworks.
11. Data Security

In this module you will learn about research and the processes involved in carrying out research and project management, and you will apply them to develop a master’s project proposal. This will include research approaches and methods of research, including literature searching, evaluation and review and risk management within project management tools and techniques. You will also consider relevant legal, ethical and social issues and good professional practice.

By the end of this module you will have constructed a project proposal which can be executed in a master’s project. This will contain a brief literature review justifying a research question, establish aims and objectives, carry out a risk evaluation and provide a plan of execution, using tools and techniques in project management, including an outline of deliverables (both artefacts and products).

This module is designed to be suitable for a variety of networking professional roles including those wishing to gain a deeper understanding of 802.11 protocols, security and mobile networks and their security protocols, their weaknesses and how to protect against known attacks.. Additionally, it is suitable for wireless network administrators and support or design staff requiring a greater understanding of the new technologies and applications of modern converged networks and delegates seeking Certified Wireless Security Professional certification. Below are the learning outcomes of the module:
Critical appraisal of the global nature of WLAN standards and design
To gain critical understanding of the core concepts in wireless and mobile communication technologies, modelling of wireless channel, security threats and vulnerabilities associated with wireless networks.
Critically analyse security protocols associated with wireless local area networks (WLANs) as well as apply specialist knowledge to design and evaluate a secure wireless network.
Critically evaluate and apply suitable security measures to protect wireless networks against known vulnerabilities.
Demonstrate ability to work in culturally diverse groups and teams and make appropriate and personal contributions to team effectiveness

In this module you will develop knowledge and skills that will enable you to tackle a realistic big data problem, using some of the principal machine learning techniques and statistical approaches used in big data analysis. Furthermore, you will learn how to implement your solution using an industry leading Cloud computing provider together with appropriate distributed processing environments.

You will learn how to host multi-terabyte sized big datasets using a cloud service provider. This will includes provisioning a commercial cloud provider, and then mastering appropriate distributed operating systems, such as Hadoop. You will then learn approaches to processing and analysing big data, based on advanced statistical processing, supervised and unsupervised machine learning algorithms and other state of the art big data analytic methods. Such techniques include clustering algorithms, pattern based information extraction, linear and non-linear regression, and feature based models. Inevitably, much work on big data analysis is statistical, so you will therefore develop some relevant statistical understanding. As data visualization is frequently critical in helping to develop hypotheses about the data, you will also cover and apply problem relevant 2D and 3D visualization methods where appropriate to the particular datasets.

In this module, you will learn about the entire data life cycle (from creation to disposal) and will gain a deep understanding of classical database development processes and approaches to modelling, design and management of databases. You will be able to learn and employ data warehousing techniques to integrate and consolidate data from different sources, which can then be used for business reporting, exploratory data analysis and advanced data analytics. In addition, you will realise the responsibilities of database designers with respect to professional, legal, security and ethical issues as well as undertaking risk management and evaluation of commercial risk in relation to data management. Moreover, you get an appreciation of non-traditional data types, systems and applications (e.g., NoSQL Databases), data standards and data quality. The module will covers topics such as:

• An overview of the entire data life cycle (e.g., creation, modelling, representation, usage, maintenance, disposal, etc)
• Classical data engineering processes and approaches (modelling, design, implementation and management and access of databases)
• Data warehousing
• Non-traditional data management technologies (e.g., NoSQL databases)
• Data analytics
• Data standards and data quality

Academic skills when studying away from your home institution can differ due to cultural and language differences in teaching and assessment practices. This module is designed to support your transition in the use and practice of technical language and subject specific skills around assessments and teaching provision in your chosen subject area in the Department of Architecture and Built Environment. The overall aim of this module is to develop your abilities to read and study effectively for academic purposes; to develop your skills in analysing and using source material in seminars and academic writing and to develop your use and application of language and communications skills to a higher level.

The topics you will cover on the module include:

• Understanding assignment briefs and exam questions.
• Developing academic writing skills, including citation, paraphrasing, and summarising.
• Practising ‘critical reading’ and ‘critical writing’.
• Planning and structuring academic assignments (e.g. essays, reports and presentations).
• Avoiding academic misconduct and gaining credit by using academic sources and referencing effectively.
• Listening skills for lectures.
• Speaking in seminar presentations.
• Giving discipline-related academic presentations, experiencing peer observation, and receiving formative feedback.
• Speed reading techniques.
• Discussing ethical issues in research, and analysing results.
• Describing bias and limitations of research.
• Developing self-reflection skills.

Human-Computer Interaction (HCI) is a field of study focusing on the interaction between humans and computers. The way that social life is organised influences the computer interfaces that we design – HCI traditionally concerns itself with satisfying user needs and requirements based on our social interactions. But, technologies also change (sometimes fundamentally) the things that we do and how we do them in the course of our everyday lives. Sometimes this is deliberate and driven by opposing value systems, as in the case of the use of interactive technologies in supporting revolution, activism, citizen science for advocacy e.g. for social change. Sometimes new technologies enable and bring about new forms of living, working, or participation in civic society that disrupt existing ones, as with Uber, AMT, and social networking platforms like Twitter.

In this module you will specifically explore how to design, develop and evaluate technologies for social change, from a human-centred perspective.

Indicative topics that we will cover include (but are not limited to):

Principles of Human-Centred Design for interactive technologies
Understanding people, context, and social life: Theories, Social media, Instrumented environments
Mock-ups, Prototyping and User Interface tools and toolkits
Participatory design
Designing to provoke: Adversarial design, Critical design
Designing for the future: Speculative design, Design fiction
Interaction qualities and experiences: Slow technology, Designing for Non-use, Counterfunctional design
Evaluation techniques: Action research, Field studies, Usability labs (eye tracking)
Specific Application Areas: HCI and environmental sustainability, ICT for Development (ICT4D), The future of: Health, Work, Money, Digital Civics

In this module you will develop knowledge and skills that will enable you to tackle a realistic machine learning problem, using some of the principal advanced machine learning techniques. You will also learn about recent applications of machine learning. Furthermore, you will learn how to implement machine learning based solutions and evaluate their performance using real world examples. The main topics covered in this module include:

• Mathematical foundations of machine learning
• Supervised, Unsupervised and reinforcement learning
• Feature extraction, feature selection and dimensionality reduction
• Classification and clustering techniques
• Optimisation techniques
• Ensemble techniques
• Autoencoders
• Restricted Boltzmann machines
• Deep Learning
• Data visualisation