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Business Intelligence and Analytics (MSc)

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 strengths of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the datamining 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 the nature and practices adopted within the business intelligence and analytics environments, 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 data 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 coursework, class presentations, group research work, and the use of a range of industry standard software such as SAS, SPSS, iThink, Simul8, MS SQL Server 2005 Analysis Services, and Oracle Data Mining Suite. Taught modules may be assessed entirely through coursework, or may include a two-hour exam at the end of the year.

Core modules

The module provides you with an in-depth analysis of the most practical topics in data mining and knowledge discovery, such as decision tree and other classification methods, association analysis, clustering and statistical mining.

The project module plays a unifying role and it aims to encourage and reward your 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.

You will strengthen your skills for the research and industry needs of the course, the final project, and for your future career and study. The module guides your personal development plan towards the professional requirements of the discipline, and covers methods of critical evaluation, gathering and analysing information, and preparing and defending a project proposal.

This is a self-contained module in applied statistics and operational research that lays the foundations for more advanced modules in data mining and analytics. You will cover topics such as hypothesis testing, regression, forecasting, linear programming and network modelling, and use software such as EXCEL Solver, SPSS, R, SAS, and AIMMS.

Option modules

This module provides you with an in-depth analysis of advance topics in operational research, such as discrete optimisation, multiple criteria optimisation and modern heuristic approaches.

You will cover topics in computing considered essential for business and industry. These will include the spreadsheet as a tool for developing decision support applications; event-driven and object-oriented programming and GUI generation (eg VBA); and the construction of databases, with emphasis placed on integrating MS Access and MS SQL Server with other applications to create decision support applications.

You will cover theoretical and practical issues related to technologies employed for the persistent storage of data. The module discusses and evaluates the underlying technologies used in capturing, maintaining and modelling persistent data. Pursuing this, you will examine the evolution of database management systems, their components and functionality, along with some of the predominant and emerging data models.

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 module focuses and addresses recent technological developments in integrating and analysing large amounts of business data that today's transactional/ operational enterprise systems are capable of collecting. You will explore multidimensional modelling, the integration of multi-source data and analysis, aiming to support better business decision making. Most of the topics covered in lectures will be associated with a number of supervised, Oracle-supported computer laboratory/ workshop sessions. The exercises and study materials used in these sessions will utilise material and courseware drawn from Oracle documents and Oracle university courses.

Through this module you will discuss in detail the features and constructs of the SQL, the defacto database language for the definition and manipulation of relational-data constructs. The module also covers procedural aspects of the language and issues related to the efficient use of and client/server programming constructs. The module also covers procedural aspects of the language and issues related to the efficient use of and client/server programming constructs. The module is a hands-on skills module; the exercises and materials used in the delivery of the module are based on Oracle University materials, and you will have access to Oracle courseware that can help you with your preparation for Oracle Certification exams.

You will examine the role of the project manager, together with the techniques used for project planning, scheduling, monitoring, and controlling projects throughout the project life cycle. The PRINCE2 project management method is sued as a framework for understanding the key issues, providing you with the practical experience is using a project management software tool for project scheduling.

This module focuses on the choice and use of appropriate simulation models to treat real-world problems, developing solution(s) using powerful Monte Carlo and discrete-event simulation software such as @RISK and SIMUL8, and explaining the business and industrial implications thereof. It will also give you concepts of analytical methods if and when appropriate, such as influence diagrams and queuing theory.

This module introduces techniques sued to analyse, integrate and interpret web and social data for purposed of understanding and optimising web site usage. The aim of the module is to prepare for an analyst career in the area of web or social media marketing. You will learn how web and social media data can be utilised to determine a website's effectiveness to conveying information to its users; about the different sources of web and social media data (e.g. Twitter, Facebook, Web Logs) and how such data can be used to learn about and target a specific web audience; and you will develop practical experience in using several different types of online analytical tools (e.g. Google analytical, Bing Webmaster Tools and AWstats.

Associated careers

Graduates can expect to find employment as consultants, decision modelling or advanced data analyst, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (such as GlaxoSmithKline, Prudential, Santander and Unilever), public sector organisations (such as the NHS and primarily care trusts), retail head offices, the BBC, the Civil Service and the host of banks, brokers and regulators that makeup the city, along with all the specialist support consultancies in IT and market research and forecasting, all of the whom us data for the full range of decision making.

Professional recognition

This course is accredited by the British Computer society for partial fulfilment of the academic requirement for Chartered IT professional.


At Westminster, we have always believed that your University experience should be designed to enhance your professional life. Today’s organisations need graduates with both good degrees and employability skills, and we are committed to enhancing your graduate employability by ensuring that career development skills are embedded in all courses.

Opportunities for part-time work, placements and work-related learning activities are widely available, and can provide you with extra cash and help you to demonstrate that you have the skills employers are looking for. In London there is a plentiful supply of part-time work – most students at the University of Westminster work part time (or full time during vacations) to help support their studies.

We continue to widen and strengthen our links with employers, involving them in curriculum design and encouraging their participation in other aspects of career education and guidance. Staff take into account the latest data on labour market trends and employers’ requirements to continually improve the service delivered to students.

Visit the Business Intelligence and Analytics (MSc) page on the University of Westminster website for more details!

Entry Requirements

You are expected to already have quantitative skills, with an interest in developing these further to support postgraduate activity in analysing, evaluating and reporting on a range of real-world data-intensive problems. You will have a suitable Honours degree from a UK university (or equivalent qualification) in a scientific or engineering discipline with some exposure to the use of IT, or in an area of computer science or IT with a strong interest in quantitative analysis.

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