Full time 12 Month(s)
This practically-focused MSc programme is ideal if you’re keen to use your strong quantitative skills in a role involving detailed analysis of marketing data. Such roles include market analysts, business consultants and business analysts, database managers, marketing researchers, credit risk modellers and related occupations. Our strong connections to industry and government ensure that what you learn on this MSc has a highly practical focus.
With expertise both in the theoretical aspects of the subject and their use in practice, you will be taught by world-class academics and will learn the latest methods in areas such as forecasting, data mining, marketing analytics and marketing research methods.
You will study a range of modules as part of your course, some examples of which are listed below.
At the end of the taught modules on all three Masters programmes in Management Science you will complete a three-four month project, which will be organisation-based (with organisations from both public and private sectors), organisation-driven or research-based. Eligibility for organisation-based projects will be competitive and based on merit and marks achieved.
These projects are an essential part of the learning on each of the MSc programmes and give you a chance to use the analytical methods and professional skills developed on the taught modules. Completion of a project will be an asset on your CV and some students go on to work for the organisation that sponsors their project. Recent examples of projects include simulation, product launch, optimisation and risk analysis at Jaguar Land Rover, Lego and Barclaycard.
Students will be introduced to the latest version of Microsoft spreadsheet: Excel 2016.
The module will cover the basics of ‘dynamic’ model building, including skills such as data visualization, handling, filtering and analysis, using a variety of key functions and charting tools. In addition there are advanced techniques such as optimisation, simulation, and the use of Visual Basic for Applications (VBA) to automate models and construct decision support models. The course will also introduce the basics of simulation programming, using VBA. The course will make extensive use of case-studies and workshop-orientated learning tasks.
At the heart of many management problems lies data that need to be described, analysed and interpreted. An operational researcher/management scientist needs to be able to do this soundly and efficiently. The overall objective of this module is to develop the ability to describe, analyse and interpret data soundly, making effective use of computer software. This module assumes some prior knowledge of statistics (although we know that some of you have plenty!) and some familiarity with simple algebra and calculus is assumed. This module emphasises the practical application of statistical methods, and makes extensive use of SPSS.
This module introduces you to the basic principles of marketing and places particular emphasis on the role of analytical modelling in supporting marketing decisions in organisations.
It is hoped that you will be in a position at the end of this course to fully appreciate how analytical models enable managers to make better marketing decisions instead of relying on their intuition or gut feel. We also provide a brief overview of some of the key topics on other modules that you will cover in more detail in the second term.
The main concern of this module is the process of conducting analytical studies for clients. It runs alongside the technical and applications courses of your MSc and should be regarded as the core 'professional' element. There are lectures, case exercises and team work, and we particularly stress the need to develop sensible approaches to problem structuring and model formulation. Students will develop an understanding of the ways in which modern management science methods can be effective in supporting decision making and analysis in contemporary organisations. In addition, you will gain an appreciation of theories of decision making and choice and some understanding of the ways in which data produced by accounting systems can be used in analytical consultancy practice.
Marketing research enables an organisation to better understand the market environment.
The main objective of conducting marketing research is to enable decision makers to make better decisions than if the latter were based only on intuition and guesses. At the heart of marketing research is information which facilitates the decision making process.
In this module we focus on the collection and use of information in marketing research but pay particular attention to the context of the problem. You will also have the opportunity to learn some transferable skills that relate to gathering and using information for business intelligence and decision making.
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
This module will consider a number of Operational Research (OR) techniques which have in the main been devised to tackle problems in which it is important to model the effects of stochastic influences.
The aim will be to provide sufficient information about the techniques to give an understanding of their potential and limitations, and of the steps likely to be involved in using them. The techniques will be selected from: Markov processes; stochastic simulation; queuing models; risk analysis and replacement, inspection and maintenance models. The first two techniques are generally applicable in many problem areas, whereas the latter three refer to models specifically developed for particular sorts of problems.
The aim of this module is to teach the skills required to apply simulation successfully to help improve the running of an organisation, whether in the public or private sectors, manufacturing or services.
Modern simulation packages are a valuable aid in building a simulation model and this module will employ a widely used and up-to-date discrete event simulation package. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each of the tasks required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
Successfully managing its operations and its supply chain activities is central to any organisation’s ability to compete for revenues and resources.
The aim of this module is to introduce a series of innovations and capabilities in operations and supply chain management that have been associated with excellence and outstanding performance.
These include strategic procurement management, lean production principles, supply chain contract design and negotiation, supply chain financing and risk management, and managing business process flows.
The module will look at these subjects in both manufacturing and service contexts.
This module will develop an understanding of the strategic role that sourcing decisions can play in supply chain management in order to gain sustainable competitive advantage in a global environment. It will look at alternative ways in which the upstream supply chain can be successfully configured and coordinated in different contexts, including the strategic role of IT in supporting this.
SAS is one of the leading providers of business intelligence and predictive analytics software.
There is increasing demand in the graduate labour market for analysts with SAS programming skills.
This module is primarily a 'support' course on the various postgraduate programmes and courses. This module will provide an introduction to SAS programming whilst giving the participants experience of using an advanced data base and a versatile statistical programming language. SAS, through our relationship with them, are supporting this course.
This module examines supply networks as extended operations that deliver products and services, and draws on concepts from operations management and from other disciplines. The emphasis is very much upon inter-organisational networks, rather than those between organisations and consumers.The central theme of the module is that e-business practices and technologies and supply network concepts and practices are inextricably linked: each facilitates the other.The module develops your ability to understand and critically evaluate the key principles of supply chain management, and helps you to appreciate and assess the important interrelationship between ICT and supply chain management practices and strategies. You will also develop skills in identifying and proposing solutions to specific network problems.
This module develops modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning.
The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour.
The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.
This module builds on the concepts learnt in this first term and covers topics such as visualising and understanding multidimensional problems; markets and competitors, market share modelling, marketing forecasting, retailing, promotional modelling and game theory for marketing.
This module will help students to apply the concepts of marketing modelling in its application to complex consumer and business markets. Students will also appreciate how such type of modelling work can help managers make better technical marketing-related decisions such as store location or the amount and type of promotional activity to promote brands.
The purpose of this module is to understand and use in somewhat simple contexts, some of the basic models from logistics.
Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics which are covered are: facility location, network design, warehousing and inventory control, vehicle routing and scheduling, and environmental considerations in transportation and distribution operations.
This module enables you to develop an appreciation of the dynamics of the international marketplace and the complex and challenging forces shaping this environment. It uses the principles and frameworks underpinning international marketing to assess how key marketing and consumer behaviour theories, models and concepts apply in an increasingly globalised and internationally competitive marketplace.
Python is a simple, yet very powerful, high level computer programming language that becomes immensely popular in our days. It is widely used in many scientific areas for data exploration and at the same time it is the preferred programming language among a wide range of modern organisations.
This course is an introduction to computer programming using Python for students without any prior programming experience. It introduces the basic principles of computer programming but is doing it with an emphasis on examples from the areas of business analytics and finance.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
Designed for: Graduates interested in careers in marketing analytics, marketing research, consumer data science, CRM and consultancy
Duration: 12 months full-time.
Entry requirements: A degree in any discipline that includes some Mathematics and/or Statistics, including, for example, Science, Engineering, Business Studies, Economics and Computer Science
If you have studied outside of the UK, you can check your qualifications at International Qualifications:
English language: IELTS: Overall score of at least 7.0, with no individual element below 6.0 We consider tests from other providers, which can be found at English language requirements
If your score is below our requirements we may consider you for one of our pre-sessional English language programmes:
10 week- Overall score of at least 6.0, with no individual element below 5.5 For details of eligibility see: Pre sessional programmes 4 week- Overall score of at least 6.5, with no individual element below 6.0 Further information is available at English for Academic Purposes
Funding: All applicants should consult our information on fees and funding
Further information: Please see our website
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