Data Analytics MSc (Distance Learning)

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You'll get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project reports and making presentations.

Statistical methodology and statistical practice are very central for data analysis. In official statistics, in hypothesis testing, in distributional properties of data, in very many application domains that support decision making, and so on, such as case studies where both statistical practice is important and the underlying and underpinning statistical methodology that is at issue. Statistical methods and statistical implementation are also very complementary to machine learning and data mining, covering supervised and unsupervised methods. Quite major developments in mathematics, in the past few hundred years, were brought about by statistical methods and their implementation. Real world applications are addressed by modelling and implementing applications encountered in business, e.g. Customer analytics, Credit scoring, Financial forecasting), in Health and Medical research (e.g. Automatic diagnosing, genetic data mining and Bioinformatics, mining online medical publications libraries), in structured and unstructured data analysis, etc. Students are exposed to current core research topics in data mining, machine learning, and interdisciplinary research in which data analysis plays an essential role.

With ever-increasing advancements in Internet-of-Things, Cyber-Physical Systems, and social media applications huge volume of complex and multi-dimensional datasets are being generated every day. Visually analysing these datasets facilitates the transformation of raw data into valuable knowledge and information. The biggest challenge is to articulate suitable solutions of complex analytical problems by visually interacting with the designed artefacts without going into underlying complexities. Tremendous endeavours have been devoted to streamline innovative solutions, novel methods, tools, processes and methodologies to address underlying challenges. This module aims to provide students with core knowledge and deep understanding of advanced theories underpinning data visualisation, best practices in using visualisation artefacts effectively and practical skills in implementing the theoretical knowledge into certain application domains. Students will be engaged in practical utilisation of state-of-the-art visualisation tools and methods to understand real-world big data problems, and to rectify complex issues with visual analysis. Topics that will be covered in this module include exploratory data visualisation; data visualisation theories, existing and emerging interactive 2D and 3D visualisation toolkits, and application of visualisation skillset in application specific domains.

The data needs of modern Enterprises and organisations require a more flexible approach to data management than that offered by traditional relational database management systems (RDBMS). With organizations increasingly looking to Big Data to provide valuable business insights, it has become clear that new approaches are required to handle these new data requirements. Primarily focusing on non-relational data models, this module introduces students to alternative approaches to modelling the data needs of an organization. It also provides students with an opportunity to use non-relational databases and database technologies to build robust and effective organizational information systems. The aim of this module is to introduce the student to the fundamental concepts, core principles, formalism, and practical skills that underpin modern data system where students will develop a practical understanding of methods, techniques and architectures required to build big data systems in order to extract information from large heterogeneous data sets.

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. This module looks at different data mining techniques and gives students the chance to use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. Topics studied include looking at the value of data; approaches to preparing data for exploration; supervised and un-supervised approaches to data mining; exploring unstructured data; social impact of data mining. Current application areas and research topics in data mining will also be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with data mining.

The ever-increasing advancements in sensing technologies, network infrastructure, storage and social media have enabled us to acquire an unprecedented volume of data at an explosive rate. As a result, the ability to efficiently and accurately derive human-understandable knowledge from these datasets has become increasingly critical to our digitally-driven society and economy. Under this Big Data phenomenon, tremendous endeavours have been devoted to tackle its underlying challenges through both novel solutions and the evolution of existing methodology. The module aims to provide students with the knowledge and critical understanding of contemporary challenges posed by the big data. The topics covered here include the fundamental characteristics and operations associated with big data; existing and emerging architectures and processing techniques; domain applications of big data in practice. Through this module, students will develop an informed understanding of the principles and practice of big data analytics in both general and application specific contexts.

The purpose of this module is to enable students to appreciate the historical, current and future application areas of AI and DA in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research & the investigative work of the students within the module.

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where the learning system is often integrated to other reasoning systems.

This module enables the student to work independently on a project related to a self-selected problem. A key feature in this final stage of the MSc is that students will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on the student to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

You will be taught through a series of lectures, tutorials, practical's in computer labs and independent study. Assessments will be based on your choice of modules; this can include presentations, essays, technical documents and peer review. The project is based on your choice of specialism.

Your module specification/course handbook will provide full details of the assessment criteria applying to your course. Assessment will include coursework and peer review and reflect the emphasis of the course on the ability to apply knowledge and skills.

Feedback (either written and/or verbal) is normally provided on all coursework submissions within three term time weeks – unless the submission was made towards the end of the session in which case feedback would be available on request after the formal publication of results. Feedback on final coursework is available on request after the publication of results

Further information

The teaching year normally starts in September with breaks at Christmas and Easter, finishing with a main examination/assessment period around May/June. Timetables are normally available one month before registration.