Big Data Technologies MSc

The module discusses the advances that big data era has brought to the way data is used by organisations, it also considers issues related to data quality and effective data governance. You will learn about the impact the volume, velocity and variety of today’s enterprise data had to the way data is stored, managed and used, the technologies utilised in big data projects, the use of SQL and noSQL databases, Hadoop, MapReduce, Hive, etc.

A hands-on module with highly technical content, Data Repositories Principles and Tools covers the underlying technologies and approaches used in capturing, maintaining and modelling persistent data; it addresses practical issues related to data modelling and database design; finally, it provides practical/hands on skills by introducing the features and constructs of SQL.

Business Intelligence, Data Mining and Analytics are a set of methods and technologies that transform raw data into meaningful and useful information. A Data Warehouse is the architecture or structure that supports these activities. This module teaches students how to build Data Warehouses by understanding their structures and the concept of multi-dimensional modelling. The focus is on Data Warehouse design, multi-dimensional modelling, the integration of multi-source data and analysis, cloud-based data warehousing, NOSQL OLAP, aiming to support better business decision making.

The module consolidates and extends the knowledge students acquired in the taught part course, encourages and rewards individual inventiveness and application of effort. You’ll be required to carry out and bring to fruition a comprehensive piece of individual work on an approved topic (relevant to your course of studies) that involves research, planning, critical evaluation, reflection activities. To provide appropriate foundational knowledge to support the project development, the module includes a series of four blended learning workshops.

This module will provide an overview of modern techniques in Machine Learning and Data Mining that are particularly customised for Data Science applications. Students will be introduced to a range of toolkits, such as R and Python and they will explore the features and strengths of different machine learning and data mining methodologies using selected data sets related to specific public sector or businesses application domains.

This module covers the theoretical and practical aspects of data visualisation including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’. Tools such as R and Tableau are used. The aim is to prepare students for becoming a data visualisation specialist.

This module explores the use of modelling to analyse and measure both online presence and impact using web and social media data. You will learn how to listen to social media conversations taking place, how such data can be transformed into actionable insight for a brand or organisation. You will also study ways in which the effectiveness of modern websites is often judged and how online web metrics can be used to drive performance. The overriding aim of the module is to equip students with the necessary technical skills and industrial knowledge for a career in the area of web or social media marketing.

The module will explore how physical objects can be connected to the internet and being able to identify themselves to other devices and the role of these technology in solving a business or social problem. The module will consider the core technologies as well as the interfaces to support these systems which focuses on identifying, designing, prototyping, and presenting an IoT solution.

Cyber security threats and countermeasures at physical and digital level focusing on behaviour of employees, home users, software developers. Developments in automated threats and countermeasures.

This module equips students with foundational knowledge in statistics, optimisation modelling, and operational research to enable data-driven business decision making. Students gain essential concepts in descriptive, predictive, and prescriptive analytics, applying these techniques to real-world business problems. Students analyse risk and uncertainty, evaluate analytical methods, interpret results, and communicate insights through effective writing and visualisation.