The module will explore both the theorical and practical implementation of objects and structures relevant to relational and non-relational database systems. Transaction management, database administration, and query optimisation, will be examined and applied.

1. Develop database objects and structures applicable to relational and non- relational database s .
2. Illustrate t ransaction m anagement t echniques in a DBMS.
3. Examine and apply query optimisation concepts.
4. A pply administration and security concepts relating to database systems.
5. Examine and discuss recent database technologies and trends .

To introduce the learner to network standards and technologies in order to design and modify data networks.

1. Analyse and Evaluate the OSI model and the TCP/IP suite.
2. Evaluate and recognise the role of internetwork ing device technology in a LAN.
3. Configure, test and troubleshoot Switches as part of a Local Area Network.
4. Identify , analyse and evaluate the subnetting of a network.

Object oriented analysis and design is concerned with the preparation and management of the requirements specification for a software system. An industry standard modelling technique supported by CASE technology will then be employed to represent these requirements.

1. Compare alternative approaches to the systems development life cycle.
2. Design data gathering techniques as part o f requirements elicitation.
3. Identify and document requirements.
4. Appraise and evaluate UML.
5. Apply the use of CASE technology to model a system using UML.

The module will further enhance the learner's skills in designing and developing object-oriented programs.

1. Introduce object-oriented design and the concepts of encapsulation, abstraction, inheritance, and polymorphism.
2. Design, implement, test, and debug simple programs using object-oriented concepts
3. Compare and contrast the notions of overloading and overriding methods in an object-oriented language.
4. Develop code that responds to exception conditions raised during execution.
5. Implement simple programs involving interfaces and abstract classes.
6. Explain, use, and modify source code which implements introductory sort and search algorithms.

This subject will introduce the learners scripting. Learners will design and implement object oriented programs for use in the administration of server systems.

1. Create scripts implementing data structures as appropriate to the business use case
2. Create objects which implement object relationships with reference to efficient code .
3. Apply basic error handling and debugging techniques
4. Evaluate and implement best practices for scripted programs.

This module will examine data warehousing strategies and methods and the importance of data and business analytics to organisation decision making. It will introduce the area of dimensional data modelling and the Extract Transform and Load(ETL) process whereby the student will gain insight and practical skills in building a data warehouse. At the end of this module, the student should be able to efficiently design, construct, populate and query a data warehouse.

1. Discuss the relationship between business analytics and data in supporting organisation decision making.
2. Analyse the needs of a given business sector in the selection of the appropriate Key Process Indicators (KPIs) for application in data analytics.
3. Appraise dimensional modelling techniques to design and build a data warehouse to meet specified user requirements.
4. Identify and apply relevant techniques to extract, transform and load data.
5. Examine and develop structures and techniques to query data for analysis and reporting.

This module introduces students to the core elements of a Machine Learning Operations(MLOps) pipeline. It will explore theoretically and practically the architecture of Big Data distributed systems. Students will gain hands-on experience with various technologies which enable efficient data accessibility for an organisation. Additionally, the module explores a range of techniques and tools designed to deploy machine learning models into production in a cost-effective and efficient manner.

1. Examine the components of data pipeline workflows.
2. Evaluate technologies available for specific data workflow components.
3. Examine and select data processing tools & techniques to satisfy a given set of user requirements.
4. Produce a data architecture design and configuration which shows a coherent structure pertaining to the ingestion, processing and storage of data.
5. Demonstrate a knowledge of data governance and security policies for a given design.
6. Evaluate future trends in the field of data architecture designs.

This module aims to develop an understanding of algorithms and data structures commonly required by computer applications.

1. Describe, analyse, and apply stack, queue, list, tree, hash, and graph data structures.
2. Use big O to determine relative efficiencies of algorithms and construct empirical tests to verify and tabulate efficiencies.
3. Explain, implement, and apply recursive algorithms.
4. Choose and apply appropriate data structures to solve particular application problems.

This module develops a strong foundation in machine learning techniques and their application to specific problems including regression, classification, recommendation and optimisation. The focus is on understanding machine learning algorithms and their practical implementation and use.

1. Examine the performance of multiple machine learning techniques.
2. Compare the main approaches to machine learning and show similarities and differences between multiple approaches.
3. Discuss appropriate machine learning classification, optimisation and regression problems.
4. Implement and apply various machine learning approaches in a range of real-world applications.
5. Evaluate the state of the art in applied Machine Learning.
6. Implement Machine Learning on multiple datasets to create meaningful output suited to a particular use case.
7. Critically analyse the performance of machine learning algorithms.

The increasing number of communication technologies– including the internet of things (IoT), wearables, ubiquitous sensing, social sharing platforms, and other artificial intelligence-driven systems—are contributing a tremendous amount of data at the individual company, and society level. These technologies offer enormous benefits but also pose enormous risks to individual privacy and ethical concerns. Further, easy access to data collected from online sources, analysed, and inferences drawn about individual users raise a wide range of ethical questions about these technologies, their sources, and their users.

In this course, students will evaluate major ethical questions raised by big data and related technologies. Students will learn about ethical inquiry strategies and decision points and consider how ethical frameworks can and should be applied to big data. They will work through case studies from real-world scenarios to implement legal frameworks, like the GDPR. They will also apply and examine various strategies to implement data governance to ensure clear milestones and trigger points for key stakeholders.

1. Justify and explain basic ethical and policy-based frameworks for working with big data and apply these frameworks to real-world cases.
2. Discuss differences between shared values across data, ethics, and society.
3. Articulate the framework for ethical inquiry strategies and decision points.
4. Implement legal frameworks, like the GDPR, in the context of big data applications in business and government
5. Apply data governance, ownership, data confidence, and data fit to make data governance work for a specific organization.
6. Examine various ways to implement data governance to ensure clear milestones and trigger points for key stakeholders.

This module is designed to teach the essential skills students need to make effective contributions and to have an immediate impact on the accomplishment of projects in which they are involved. Students will learn techniques required to plan, manage and control projects

1. Compare and contrast different approaches to project management.
2. Apply risk management techniques.
3. Appraise techniques used to esti mate project cost and duration .
4. Prepare graphical representations to plan and control a project.
5. Compare and contrast team organisations and select a suitable organisation based on project characteristics .

This module aims to give students operational experience of design and implementation of information systems. Students should also be exposed to a professional working environment with the objective of maximising their employability and future career prospects whilst providing employers with highly capable staff who can demonstrate and apply their technical skills to real-world situation.

1. Work as part of a team in the design and development of information systems and related projects in commercial industry.
2. Identify, explore and make informed judgements relating to the work tasks/project on which they work.
3. Prepare a log book of their daily activities reflecting on personal professional practices appropriate in an industry setting.
4. Prepare a report on the work undertaken during the placement.
5. Apply reflective learning as part of their personal and professional practice
6. Apply research, information gathering, critical analysis, design and implementation techniques appropriately and effectively.
7. Communicate effectively in order to enhance the understanding and engagement of a professional audience

This module will examine business reporting and visualisation by introducing the student to fundamental design principles and techniques for creating meaningful visualisations of quantitative and qualitative data to facilitate business decision making. At the end of this module, the student should be able to collect and process data, create interactive visualisations and demonstrate how it provides insight into managerial decision making.

1. Define the role of business reporting and data visualisation and discuss its evolution, value and importance.
2. Critique existing visualizations based on data visuali s ation theory and principles.
3. Design and create data visualisation s.
4. Apply data transformations such as aggregation and calculations for visuali s ation.
5. Examine the capabi lities and limitations of dashb 0ards.
6. Develop competency in the use of an industry standard tool to prepare and present effective visualisations for various user needs.

The objective of this module is to provide the student writer with a detailed exposure to the general process of academic writing and to then relate this general process to the specific process of writing an academic report in the computing domain.

1. Develop a research specification which includes a research question and corresponding thesis,
2. Gather, evaluate and synthesise high quality evidence to support their thesis,
3. Plan and implement a writing strategy which is logically and rhetorically correct for the writing task
4. Critically review and revise initial drafts of the document against the research specification, againstthe conventions of academic writing and against the usual standards of grammar and style.

Software engineering is an engineering discipline which is concerned with all aspects of software production, it is concerned with theories, methods and tools for professional software development . This module builds on what learners have already covered in previous modules and teaches the discipline of software engineering. Learners will evaluate the engineering methods, processes, techniques and measurements which are part of software engineering.

1. Compare and contrast software process models and select a suitable software process model for use on a software project.
2. Evaluate quality in the software process and process improvement initiatives.
3. Appraise software quality models.
4. Establish a software measurement programme.
5. Manage software configuration for a software project.

Natural Language Processing (NLP) is a rapidly evolving area of research in Computer Science and Artificial Intelligence (AI) concerned with processing natural languages such as English so that computers can understand language as humans do. NLP generally involves translating natural language into data that a computer can use to communicate with humans and to learn about its environment.

This module will provide the learner with the knowledge and skills required to understand the components of a modern NLP system, to critique and construct a sentiment analysis system, and to understand the concept of deep learning within the context of NLP.

1. Examine the various processes required for NLP text normalisation.
2. Discuss, analyse and examine key components required to successfully build a n NLP system .
3. Critically analyse and apply the concepts of a text classification system.
4. Critique, evaluate and implement the key NLP processes needed to build a sentiment analysis system.
5. Critically analyse and critique relevant Neural Networks and Deep Learning suited to NLP implementation.
6. Design and develop a chatbot using relevant NLP techniques.

This module will give students practice in academic research with the freedom to express their personal interests in the field of computing. Students will have an opportunity to critically analyse and synthesise pertinent literature regarding their area of exploration. Students will gain an appreciation of iterative development, critique and an ability to devise a plan for practical development.

1. Appraise and evaluate emerging trends from information sources and present findings.
2. Develop and document detailed aims and objectives from preliminary research.
3. Critically analyse and evaluate pertinent literature succinctly.
4. Analyse advancements in technology with regard to their impact on research and industry.
5. Develop and document a design solution using appropriate methodologies for the field of study.
6. Develop and present a prototype of high academic and technical standard.

This module will examine data analysis strategies and methods and the importance of data and business intelligence to an organisation. It will introduce the use of a statistical programming language and the evaluation of methods required to pre-process, condition and extract data prior to evaluation. Furthermore, the learner will develop an understanding of how to select and implement data analytic techniques, generate insight and interpret data visualisations.

1. Identify suitable data sources for an assigned use case and evaluate pertinence of captured data.
2. Manage datasets and evaluate content for pre-processing, conditioning and extraction.
3. Apply data analytic techniques to derive insight and value from datasets.
4. Understand the importance of data analytics in deriving insight for business intelligence.
5. Critically appraise data analytics technologies and techniques across a broad range of use cases.
6. Apply research, information gathering, critical analysis, design and implementation techniques appropriately and effectively

Students should become sensitized to the ethical and social implications of the growing use of computers and will develop an understanding of the legal issues inherent in the discipline of computing.

1. Evaluate the utility of philosophical ethical theories in solving computer related ethical issues.
2. Examine what it means to be professional at work, in conduct and practice.
3. Discuss the consequences of pertinent legal and social issues on software developers and the role of relevant enforcement organisations.
4. Evaluate current computer-based threats.

The module will develop the learners knowledge of Computer Vision, Image Processing and Video Processing. In addition, learners will evaluate multiple Computer Vision methods, processes, techniques and measurements to gain an understanding of when each should be applied. Deep Learning frameworks such as GoogleLeNet (a pre-trained Convolutional Neural Network) will also be explored and applied to Computer Vision problems.

1. Differentiate between Image Processing and Video Processing techniques.
2. Critically analyse the various elements of a Computer Vision system.
3. Critique and evaluate key components required to successfully perform Object Detection.
4. Design, develop and implement algorithms to perform Object Tracking.
5. Critically examine case studies to identify a Computer Vision problem and design a suitable solution.
6. Implement a Computer Vision system to create meaningful output suited to a particular use case.

This module will offer the student the opportunity to present the synthesis of their computing skills through a personally chosen and independently developed software artefact and supplementary document. Project Development will encourage independent investigation, design skills, revision and reflection on a specific area of computer science relevant to the student's course of study. The software artefact should be a direct response to a research investigation carried out in the Research in Computing with Emerging Technologies module.

In the submission of an accompanying document, it is expected the student will evidence the employment of an appropriate software design methodology, critical thinking and problem solving with regard to issues raised during development. The excellent student will synthesise material from modules they have taken over their course of study and bring this knowledge to bear on their chosen topic.

1. Evidence independent technical investigation .
2. Choose and apply an appropriate software design methodology for the development of a software artefact.
3. Evidence good software development skills such as efficient code, code commenting, code revision, extensibility, bug testing and deployment.
4. Explain development activities in a project report adhering to good academic standards.
5. Deliver a comprehensive presentation on the conception, research, design and development phases which led to their software artefact.

This module will expand upon the knowledge gained from the Data Analytics module. It will provide the learner with the skills required to gain an appreciation of the complexity of predictive model design and the steps required for the development of a data prediction model. Students will also gain an appreciation of the importance of data visualisation for improved decision making and information interpretation.

1. Identify and evaluate logistic regression models and their suitability for an assigned use case.
2. Design a time series forecast model using previously identified datasets aligned with an assigned use case.
3. Develop and document the procedures required to build a time series model.
4. Plan and implement a time series model and evaluate its effectiveness and fit through relevant metrics.
5. Develop and present data forecasts using appropriate visualisation tools.