MSc in
Business Intelligence and Analytics

The module explores the principles, architectures, and technologies that underpin modern Big Data ecosystems. Students examine how to manage the volume, velocity, and variety of data using SQL and NoSQL databases, distributed computing frameworks such as Hadoop and Spark, and cloud-based analytics platforms. The module also addresses data governance, quality management, and the legal and ethical dimensions of data persistence and protection. Through practical workshops and applied case studies, students develop the skills to collect, process, and analyse large-scale datasets to support informed business decision-making.

Assessment: Assessment is designed to evaluate both theoretical understanding and applied technical competence. Students complete a group Big Data Analysis Report and group presentation, followed by an individual coursework assignment that requires the practical application of Big Data tools and frameworks to a real-world case study.

This is a module in applied statistics and operational research (OR) for decision making that lays the foundations for more advanced modules in data mining, optimisation and simulation modelling. It covers the essentials of descriptive, predictive, and prescriptive analytics in an application-driven manner and uses appropriate software tools, such as Excel (including add-ins) and R, to derive meaningful solutions.

Assessment: The module will be assessed by a closed-book in-class test and coursework. The coursework will require students to demonstrate an understanding of various aspects of business analytics (descriptive, predictive, and prescriptive) and the ability to combine various techniques to solve problems. The closed-book in-class test (ICT) will be a time-constrained assessment designed to assess students’ understanding of theoretical issues and their critical, integrated understanding of the techniques taught, their application, and their limitations.

The module covers theoretical and practical issues related to the technologies used for persistent data storage. It evaluates underlying technologies and approaches used in capturing, maintaining, and modelling persistent data; reviews the evolution of DBMSs and their components and functionality, along with some of the predominant and emerging data models; addresses practical issues related to conceptual data modelling, practical and current trends in database design; it also discusses in detail the features and constructs of SQL.

Assessment: The first coursework is a group coursework that will assess students’ ability to conceptualise and evaluate information contained in a given case study, to construct a fit-for-purpose data model, and verify their design using alternative approaches to data modelling; it may also require students to manage data from heterogeneous sources. The second assessment will be a Phase Test on SQL that will assess students’ retention, understanding, insight, and ability to use SQL constructs effectively under time constraints.

The module strengthens students’ skills for the research and industry needs in their studies, their final project, and their professional development. The project consolidates and extends the knowledge students acquired in the taught part of the course and encourages and rewards individual initiative and effort. Students need to carry out and bring to fruition a comprehensive piece of individual work that involves activities (related to a single theme relevant to their studies) that include research, planning, critical evaluation, and reflection. The scope of the project is not only to complete a well-defined piece of work in a professional manner within the time the module runs, but also to place the work into the context of the current state of the art of the subject area.

Assessment: The project is assessed using a dissertation (project report) that students are required to submit and a viva voce.

The module provides advanced knowledge of operational research (OR) methods used to solve complex business optimisation problems. Students explore linear and integer programming, network models, dynamic and multi-criteria optimisation, as well as modern heuristic and meta-heuristic approaches such as Genetic Algorithms and Simulated Annealing. The module focuses on developing computer-based optimisation models and applying quantitative techniques to real-world decision-making scenarios across business contexts.

Assessment: Assessment consists of a coursework assignment, where students model and solve a real business optimisation problem using appropriate OR techniques and software tools, and a time-constrained in-class test assessing analytical reasoning, modelling skills, and the ability to interpret and evaluate optimisation results for effective decision-making.

The module focuses on selecting and applying appropriate simulation modelling approaches to address real–world problems, developing solutions using powerful simulation software, and explaining the business and industrial implications. Relevant applications to issues such as stock control, reliability, project management, and service redesign will be considered in domains such as healthcare, supply chain, and transport.

Assessment: Assessment consists of a group presentation and an individual simulation project report.

The module provides an in-depth exploration of modern data mining and machine learning techniques for real-world Data Science applications. Students develop practical expertise in problem formulation, data pre-processing, algorithm selection, implementation (using Python and industry-standard toolkits), and performance evaluation. The module covers predictive and descriptive modelling, pattern mining, dimensionality reduction, neural networks, and SVMs. Emphasis is placed on critical evaluation of algorithms, model bias, performance metrics, and the interpretation of results for business and public-sector contexts.

Assessment: Assessment consists of an individual project report and an in-class test. The project requires students to analyse a large real-world dataset, implement appropriate machine learning or data mining algorithms, evaluate model performance, and produce an analytical report. The in-class test assesses conceptual understanding, algorithmic knowledge, model evaluation skills, and the ability to critically justify methodological choices under timed conditions.

Business Intelligence, Data Mining, and Analytics are 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, multidimensional modelling, multi-source data integration and analysis, cloud-based data warehousing, and NoSQL OLAP, aiming to support better business decision-making.

Assessment: Assessment is based on two components: a coursework report and a closed-book examination in Data Warehousing and OLAP. Formative assessment is embedded throughout the module to help identify strengths and areas for improvement at an early stage.

The module covers data visualisation's theoretical and practical aspects, including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’. Tools such as Power BI, Python and Tableau are used. The aim is to prepare students to become data visualisation specialists.

Assessment: The module uses a combination of an in-class test and an individual coursework project to evaluate both theoretical understanding and practical application of data visualisation principles. The assessments are designed to measure students' ability to comprehend, apply, and critically evaluate data visualisation concepts, techniques, and tools in realistic scenarios.

The module explores the use of modelling to analyse and measure both online presence and the impact of using web and social media data. During the module students will learn how to listen to social media conversations and how such data can be transformed into actionable insight for a brand or organisation. Furthermore, students will study how 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 web or social media marketing.

Assessment: The coursework will assess students’ ability to conceive and implement a semantic model that provides insight into an individual or organisation's current social media performance. Each student will be assigned a project brief detailing the name of the company or individual that they will analyse. The in-class test will be a time-constrained assessment that assesses students’ retention, understanding, insight, and ability to reflect on the models and techniques covered throughout the module.