All graduates from the BCom need to be able to use quantitative techniques to analyse business problems. This ability is important in all business disciplines since all disciplines deal with increasing amounts of data, and there are increasing expectations of quantitative skills. This unit shows how to interpret data involving uncertainty and variability; how to model and analyse the relationships within business data; and how to make correct inferences from the data (and recognise incorrect inferences). The unit will include instruction in the use of software tools (primarily spreadsheets) to analyse and present quantitative data.

This unit provides students with the necessary foundations and skills to undertake second year units in business analytics and successfully complete the Business Analytics major. Theoretical models discussed are motivated by real life business applications and decision problems. The unit provides a grounding in linear algebra (matrix properties) and calculus and applies these methods to regression models with multiple variables. Topics covered include logistic regression, interaction and nonlinear effects. The unit also introduces the key ideas of optimization (particularly for quadratic problems) and shows how optimisation models can be used to make statistical estimates. At the same time as building understanding of the mathematical foundations needed in business analytics, the unit helps students to build programming skills to solve practical problems from the business area. The unit makes use of the modern programming languages such as Python.

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 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.

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. The unit emphasises 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 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 takes a practical approach with many up-to-date datasets used for demonstration in class and in the assignments.

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.

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 primary goal of the unit is to demonstrate 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 covers 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 (i.e. offices, hospitals, restaurants, department stores and factories) - the production of goods and services requires operations management. The efficient production of goods and services requires effective application of the concepts, tools, and techniques introduced in this unit. These include: quality management, capacity planning, location and layout strategies, supply chain management and inventory control.

Project management provides organisations with a powerful set of tools to improves their ability to plan, implement, and manage activities to accomplish specific organisational objectives. 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.

Advances in information technology have made available 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. This unit 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 is 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.

How do business analysts model firm or consumer behaviour quantitatively? How do analysts model brand choice in marketing, or travel mode choice in transport? These questions are answered by modelling choices with statistical tools designed for qualitative or discrete data, such as logistic regressions, rather than the standard linear regression models. This unit investigates various quantitative modelling techniques relevant for choice modelling through business cases in marketing, transport research, strategy, economics and other relevant business fields. This unit also explores models that pool observations on a cross-section of households, countries, firms, etc. over several time periods. This is known as panel data models which are increasingly relevant in all areas of Business with the growing availability of new sources of data.

Time series and dynamic modelling is a fundamental component of modern business practice. Further, forecasting is a required component of business decision making. This unit provides an introduction to the time series models used for the analysis of data arising in different business areas including finance, accounting, marketing, economics and many other disciplines. It then considers methods for point and interval forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of time-series data; Seasonal Exponential smoothing and ARIMA models; Vector Autoregressions; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting risk measures such as Value at Risk and Expected Shortfall; dynamic factor models. Emphasis is placed on applications involving the analysis of many real business datasets. Students are encouraged to undertake hands-on analysis using appropriate software.

This capstone unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills developed across the Business Analytics major. The problem-based approach to learning in this unit offers vital tools and techniques for business decision makers in the big data era through the use of very large and rich data sources. The unit casts the knowledge of statistical learning in modern machine learning context and exposes business students to a range of state-of-the-art machine learning topics with the emphasis on applications involving the analysis of business data. Machine Learning is a fundamental aspect of business analytics that automates analytical modelling and decision making. Students ensure their career-readiness by demonstrating their ability to apply concepts, theories, methodologies, and programming skills to authentic problems and challenges faced in the field of business analytics.

This unit is administrative only and serves as a consolidation for all marks to represent a single final mark for students undertaking Honours. Marks will be calculated as follows: BUSS4001 (20%); BUSS4X12 (10%); BUSS4X13 (10%); BUSS4104 (60%).

This unit is an introduction to research methods used in business disciplines. The unit provides students with an understanding of the range of methods that may be used to answer research questions, their strengths and weakness and underlying philosophical assumptions. Key elements of the research process are addressed, including the purpose of the research; devising the research questions and hypotheses; selecting a research strategy; methods and procedures for data collection and analysis; and interpreting and reporting the results. Students learn important research terminology, how to write a research proposal and ethical considerations in conducting research. The first component of this unit is delivered to the whole Business School Honours cohort and covers obligations as a researcher. The second component of the unit splits into Disciplinary areas and covers issues related to research design.

This unit covers advanced research-integrated coursework topics in optimisation and stochastic processes, such as convex optimisation, duality, approximation, statistical estimation, random walks and Markov chains, and Poisson and other stochastic processes.

This unit aims to provide advanced knowledge on linear models and methods for economic and financial time-series analysis and panel data models. The unit focuses on estimation and inference. It covers some of the basics of time series model including stationary processes, AR, MA and ARMA processes, spectral analysis, structural change, nonstationarity, VAR and VECM, state-space models and Kalman filter.

This unit comprises the research and writing of a supervised thesis on an approved topic in business. A written Honours Thesis and presentation of the research work is undertaken.