The ability to understand and mathematically formulate decision problems is a fundamental skill for managers in any organisation. This unit focuses on basic management science modelling techniques used in capacity planning, production management, and resource allocation. Students will learn to approach complex real life problems, formulate appropriate models and offer solution procedures to ensure an optimal use of resources. Methods include linear programming, integer programming, quadratic programming, and dynamic programming.

This introductory unit on decision analysis addresses the formal methods of decision making. These methods include measuring risk by subjective probabilities; growing decision trees; performing sensitivity analysis; using theoretical probability distributions; simulation of uncertain events; modelling risk attitudes; estimating the value of information; and combining quantitative and qualitative considerations. The main goal of the course is to show how to build models of real business situations that allow the decision maker to better understand the structure of decisions and to automate the decision process by using computer decision tools.

This unit is about the fundamentals of operations management, an exciting area that has a profound effect on the productivity of both manufacturing and services. The techniques of operations management apply throughout the world to virtually all productive enterprises. It does not matter if the application is in an office, a hospital, a restaurant, a department store, or a factory - the production of goods and services requires operations management. As a graduate working in the business sector you will certainly be exposed to operations issues - this unit will equip you to approach these issues intelligently, whether or not your role is within the operations function. The efficient production of goods and services requires effective application of the concepts, tools, and techniques that we introduce in this unit. These include: quality management, capacity planning, location and layout strategies, supply chain management and inventory control.

Project management provides business organisations with a powerful set of tools that improve their ability to plan, implement, and manage activities to accomplish specific organisational objectives. But project management is more than just a set of tools; it is a results-oriented management style that places a premium on building collaborations among a diverse cast of characteristics. This unit introduces students to the planning and management of projects by focusing on a variety of practical topics including project network, PERT, resource scheduling, learning curves, cost and time management in projects, and the use of project management support systems. It also discusses the organisational, leadership, cultural, technological challenges that project managers might face.

Statistical analysis of quantitative data is a fundamental aspect of modern business. The pervasiveness of information technology in all aspects of business means that managers are able to use very large and rich data sets. This unit covers a range of methods to model and analyse the relationships in such data, extending the introductory methods in BUSS1020. The methods are useful for detecting, analysing and making inferences about patterns and relationships within the data so as to support business decisions. This unit offers an insight into the main statistical methodologies for modelling the relationships in both discrete and continuous business data. This provides the information requirements for a range of specific tasks that are required, e.g. in financial asset valuation and risk measurement, market research, demand and sales forecasting and financial analysis, among others. Emphasis will be given to real empirical applications in business, finance, accounting and marketing, using modern software tools.

Predictive analytics are a set of tools to enable managers to exploit the patterns found in transactional and historical data. For example major retailers will invest in predictive analytics to understand, not just consumers' decisions and preferences, but also their personal habits, so as to more efficiently market to them. This unit introduces different techniques of data analysis and modelling that can be applied to traditional and non-traditional problems in a wide range of areas including stock forecasting, fund analysis, asset allocation, equity and fixed income option pricing, consumer products, as well as consumer behaviour modelling (credit, fraud, marketing). The forecasting techniques covered in this unit are useful for preparing individual business forecasts and long-range plans. The unit has a practical approach with many up-to-date datasets used for demonstration in class and in the assignments.

The unit covers a range of statistical models and methods for analysing quantitative actuarial data in general insurance. Both maximum likelihood estimation and Bayesian estimation methods are adopted for statistical inferences with the use of modern software tools such as the R and OpenBUGS packages. Topics covered include probability distributions for actuarial modelling, claim size modelling, claim frequency modelling, loss reserve forecasting, pure premium calculation, premium rates reviewing and revising (credibility theory), linear and generalised linear models, Poisson process and Markov process in actuarial modelling. Upon the completion of this unit and other relevant business analytics units, students may undertake professional examinations for actuaries or may get exemptions in some professional examination papers.

This unit gives guidelines for the formulation of management science models to provide practical assistance for managerial decision making. Optimisation methods are developed, and the complexity and limitations of different types of optimisation model are discussed, so that they can be accounted for in model selection and in the interpretation of results. Linear programming methods are developed and extended to cover variations in the management context to logistics, networks, and strategic planning. Other topics may include decision analysis, stochastic modelling and game theory. The unit covers a variety of case studies incorporating the decision problems faced by managers in business.

Everyone working in business needs to understand and manage risk. This unit will provide the basic knowledge and tools needed to do this. It includes material on the risk management strategies that every business needs, as well as specific quantitative and statistical techniques for evaluating risk. By taking this unit students will learn how different aspects of risk management fit together (like Value-at-Risk (VaR) and tail-VaR calculations, Monte-Carlo simulation, extreme value theory, individual and collective risk models, credibility theory and credit scoring).

The advances in information technology have made available very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. The course offers an insight into the main statistical methodologies for the visualisation and the analysis of business and market data, providing the information requirements for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis will be given to empirical applications using modern software tools.

This unit is designed to equip students with advanced tools for estimation and testing in relevant business statistical models. In particular, the unit covers maximum likelihood, Bayesian estimation and inference, and hypothesis testing. The unit acknowledges the importance of learning computing skills as helpful for job applications and special emphasis is made throughout the unit to learn numerical methods such as Monte Carlo simulations and Bootstrapping. Special topics in advanced statistical modelling, such as nonlinear estimators and time series regression, are also covered. The materials taught are essential as preparation for honours in Quantitative Business Analysis.