Business Intelligence and Analytics MSc

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  • Course description
    This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.

    You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.

    Course content:

    Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.

    Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.

    Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.


    The following modules are indicative of what you will study on this course. For more details on course structure and modules, and how you will be taught and assessed, see the full course document.

    Core modules:


    The module discusses how to manage the volume, velocity and variety of Big Data, SQL and noSQL databases, and it touches on issues related to data governance and data quality.


    This is a self–contained 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 essential of descriptive, predictive, and prescriptive analytics in an application driven manner and makes use of appropriate software tools such as EXCEL and R to derive meaningful solutions.


    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 work with select data sets, related to the specific public sector or businesses application domains. Students will work through exercises that provide opportunities to explore the features and strengths of different machine learning and data mining methodologies. A range of toolkits will be introduced, such as R and Python.


    The module strengthens student’s skills for the research and industry needs in the area of their studies, their final project, and their professional development. It guides the students’ personal development plan towards the professional requirements of the discipline, and covers methods of critical evaluation, gathering and analysing information, and preparing and planning a project proposal.


    The project module plays a unifying role and it aims to encourage and reward individual inventiveness and application of effort. The scope of the project is not only to complete a well-defined piece of work in a professional manner, but also to place the work into the context of the current state of the art in business intelligence and/or analytics.

    Option modules:


    The module teaches students how to use Big Data Analytics in enterprises considering both the latest research achievements and technology trends. It gives an overview of the underlying concepts and technologies of Big Data Analytics, such as Hadoop, MapReduce, Hive, etc. It covers the whole data lifecycle from creating to processing data and from publishing and to preserving data.


    The module provides an in-depth analysis of advanced topics in operational research such as discrete optimisation, multiple criteria optimisation and modern heuristic approaches.


    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 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.


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


    The module focuses on the choice and use of appropriate simulation modelling approaches to treat real–world problems, developing solution(s) using powerful simulation software and explaining the business and industrial implications thereof. Relevant applications to problems such as stock control, reliability, project management, and service redesign will be considered in domains such as healthcare, supply-chain, and transport.


    This module explores the use of modelling to analyse and measure both online presence and impact using web and social media data. During the module students will learn how to listen to social media conversations taking place and how such data can be transformed into actionable insight for a brand or organisation. Furthermore, we will study ways in which the effectiveness of modern websites are 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.

    You may take instead another postgraduate module in the Department, at the course leader’s discretion.

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