Never has the collection of data been more widespread than it is now. The extraction of information from massive, often complex and messy, datasets brings many challenges to fields such as statistics, mathematics and computing.

Develop the skills and understanding to apply modern statistical and data-science tools to gain insight from contemporary data sets. By addressing challenges from a variety of applications, such as social science, public health, industry and environmental science, you will learn how to perform and present an exploratory data analysis, deploy statistical approaches to analyse data and draw conclusions, as well as developing judgement to critically evaluate the appropriateness of chosen methods for real-world challenges.

Optimisation is one of the primary techniques associated with management science and operational research. Linear programming models are used routinely in many industries, including petroleum refining and the food industry. Integer linear programming models are increasingly utilised for complex scheduling problems, such as those in the airline industry, where they have resulted in substantial cost savings.

Skills in formulating and solving applied optimisation problems are valuable for anybody interested in a career in operational research, business modelling, or consultancy. Simulation is ultimately about building computer models to study and quantify uncertainty in a system and using this to make informed decisions, which can provide the edge when making a business decision. We will focus on building and analysing discrete-event simulation models, which can be applied across a wide range of sectors, from healthcare to manufacturing and supply chains.

Supporting decision-making in organisations is at the heart of business analytics, and is important for planning. For example, it helps with predicting customer demand or understanding customer behaviour.

You will be introduced to various techniques in forecasting and predictive analytics. You will learn how to predict future demand using statistical and machine learning techniques. Additionally, you will utilise data to gain a better understanding of customers by implementing machine learning techniques.

The aim is to give you the skills necessary to develop a validated quantitative set of predictions using both statistical and machine learning methods. These skills will enable you to support specific decisions related to inventory, revenue, or marketing management, by providing accurate predictions.

This module will also enhance your programming skills in R. It is designed to improve your quantitative abilities, increase your statistical literacy, and enable you to apply forecasting and predictive analytics to real-world business challenges.

This module provides you with a rigorous understanding of microeconomic principles that underpin sophisticated economic analysis and prepares you for further studies in economics. You will explore key microeconomic concepts including utility maximisation, profit maximisation, cost minimisation, market structures, externalities, information economics, public goods, general equilibrium theory, and welfare economics.

To succeed in this module, you will need problem-solving skills and to be proficient in algebra, elementary calculus and logical reasoning.

Statistics allows us to estimate trends and patterns in data and gives a principled way to quantify uncertainty in these estimates. The findings can lead to new insights and support decision-making in fields as diverse as cyber security, human behaviour, finance and economics, medicine, epidemiology, environmental sustainability and many more.

Dive into the behaviour of multivariate random variables and asymptotic probability theory, both of which are central to statistical inference. You will then be equipped to explore one of the most fundamental statistical models, the linear regression model, and learn how to apply general statistical inference techniques to multi-parameter statistical models. Statistical computing is embedded in the module, allowing you to investigate multivariate probability distributions, simulate random data, and implement statistical methods.

This module is designed to enhance your strategic thinking skills. You will learn how to use games to model real-world strategic situations, and how to analyse and solve these games in scenarios where players are intelligent and rational.

The module covers:

• normal form games
• extensive form games
• Bayesian games
• games with correlation devices
• repetitive games
• behavioural games

Additionally, you will have opportunities to play these games with your instructor and classmates. A basic understanding of algebra, calculus and economics is necessary for this module.

In this module you will extend the knowledge of macroeconomics that you developed in your first year. Although the primary focus of the module is on macroeconomic theory, this is taught within the context of current events in the international macroeconomic environment. The topics covered include classical and Keynesian views, unemployment, the government budget constraint, monetary and fiscal policy, intertemporal macroeconomics, economic policy in the open economy, unemployment and inflation, adaptive and rational expectations, policy effectiveness under rational expectations, the economics of independent central banks, and growth theory.

To succeed in this module, you will need to apply algebra, basic calculus, logical thinking, and problem-solving skills.

You will spend this year working in a graduate-level placement role. This is an ideal opportunity to gain experience in an industry or sector that you might be considering working in once you graduate.

Although it's up to you to find your placement we'll support you all the way. Our Careers Service will provide guidance on CVs, applications, interview techniques and creating a digital profile.

Building on the statistical techniques explored so far, you will gain an understanding of both the theoretical underpinnings and practical application of frequentist statistical inference. You will then be introduced to an alternative paradigm: Bayesian statistics.

The frequentist perspective views all probabilities in terms of the proportions of outcomes over repeated experimentation and has been the foundation of hypothesis testing and experimental design in years of data-driven science and research. Meanwhile, the increasingly popular Bayesian approach arises directly from Bayes theorem, avoiding hypothetical repeated sampling. As a result, Bayesian statistics is often more intuitive and easier to communicate and naturally takes all forms of uncertainty into account.

With this in mind, you will compare and contrast these two perspectives and their associated tools. You will learn to select and justify an appropriate methodology for inference and model selection, and to reason about the uncertainty in your findings within each paradigm.

Machine learning (ML) is critical in the process of extracting knowledge (that is patterns, relationships and insights) from complex real-world datasets. As a result, ML plays a key role in the transition from intuition-based to evidence-based decision-making.

You will be introduced to fundamental methods and tools from statistical and machine learning, covering the entire data analysis process. The module begins with setting project objectives and understanding the available data, and continues through the development and assessment of machine learning and statistical models. We will expand on concepts of statistical modelling, such as linear regression, and introduce established and cutting-edge methodologies in machine learning and artificial intelligence.

This module builds on the skills acquired in the earlier optimisation and simulation module. It will cover more advanced optimisation and simulation techniques and their application in more complex problems. The aim is to further enhance the decision-making toolkits of a business analyst for real-world applications.

The optimisation part will introduce new methods for different types of problem, while the simulation section will expand upon existing knowledge to allow more insightful analysis from simulation models.

The following topics will be covered:

• multi-objective optimisation
• goal programming
• sequential decision making
• In-depth exploration of simulation modelling
• advanced analysis of simulation models.

You will work in a scientific programming language, such as Python, developing the programming skills necessary for implementing optimisation and simulation models and conducting analysis.

This module will introduce you to the field of behavioural and experimental economics. It will equip you with the necessary skills to study how the standard rationality assumptions can be relaxed to account for psychological and cognitive biases as well as social preferences. Additionally, you will be introduced to the tool of experimentation in economics as a means of collecting data to test the various economic theories.

Some of the topics covered will include:

• behavioural finance
• rational emotions
• nudging
• choice under risk
• time preferences
• social preferences
• behavioural game theory

Models of dynamical systems are fundamental to our understanding of the physical and natural world.

Explore a new class of model for the time evolution of a dynamical system and investigate Markov jump process models for real-world systems, such as the evolution of species populations in the wild and the spread of infectious diseases. Using these processes, you will learn how to simulate and study methods understanding their properties and behaviours. Unlike deterministic differential equation models, Markov jump processes are random, allowing for different behaviour every time they are simulated. You will discover how it is often possible to associate a jump process with a related differential equation approximation and that this can provide important insights into the behaviour of the jump process and the original real-world system.

Data science is transforming the role of information technology in society and in many sectors in which economists work. Machine learning and big data methods have gained popularity as tools in academic, government, industry, and beyond.

You will be introduced to big data and machine learning techniques with a focus on economic applications. These techniques are already significantly impacting the field of economics for modelling economic relationship, drawing causal inferences, and making predictions. They will soon become a standard toolbox for economists.

An introduction to a variety of methods that are useful for analysing environmental data, such as air temperatures, rainfall or wildfire locations. Spatial dependence is a key feature of many environmental datasets, and the Gaussian process will be introduced as a model for continuous spatial processes. You will learn about the properties of the Gaussian process and implement this model for spatial data analysis, before investigating methods for point-reference data, such as earthquake or wildfire locations.

You will also dip into natural hazard risk management, which seeks to mitigate the effects of events, such as flooding or storms, in a manner that is proportionate to the risk. You will learn basic concepts from extreme value theory, including the appropriate distributions for extremes, and how to use these as statistical models for estimating the probability of events more extreme than those in the dataset.

This module will introduce you to current economic research. Experts from a variety of fields will teach you in-depth about how economic research is conducted and its real-world applications. By the end of this module, you will be able to:

• Apply advanced economic methods and concepts to evaluate contemporary economic problems.
• Critically think about the issues raised by economic research, including their applications and limitations.
• Present the methods and conclusions of advanced economic research using a wide variety of presentational tools.
• Consolidate the core knowledge, methods, and analytical skills you have developed throughout your degree.

These skills will prepare you for a professional career in economics and related fields, as well as for further academic study.

This module builds on the foundations of monetary and fiscal policy analysis by placing policy decisions in a global context. The first part of the module emphasises the interpretation and analysis of macroeconomic data. You will learn how to apply empirical methods to understand fluctuations in output, employment, inflation, and trade balances.

The second part focuses on the design and coordination of monetary and fiscal policy in an interconnected world. Special attention will be given to the challenges that central banks and governments face in managing global shocks. The topics covered will include international policy spillovers, exchange rate regimes, capital flows, and the evolving role of institutions such as the IMF and the Bank of England. We will explore real-world applications and current policy debates throughout the module.

This module provides a comprehensive exploration of international trade and global business dynamics, connecting theoretical models with practical policy implications. You will examine core trade theories including the Ricardian model, Heckscher-Ohlin model, and heterogeneous firm models. The module also offers in-depth analyses of international factor mobility, trade policies, and globalisation trends.

On the international business side, you will study key topics such as global value chains, multinational firm strategies, international competitive advantage, and the economic impacts of outsourcing and offshoring. The module focuses on real-world applications, exploring how theoretical frameworks inform understanding of contemporary economic phenomena, including labour productivity, attitudes towards trade, the effects of immigration, and the evolving landscape of global economic interactions.

Lay the mathematical foundations necessary to model certain transactions in the world of finance. You will study some stochastic models for financial markets and investigate the pricing of European and American options and other financial products.

You will consider two discrete models, the binomial model and finite market model, and one continuous model. In particular, you’ll deduce the Black Scholes formula following an introduction to some probabilistic terminology, such as sigma algebras and martingales, and some financial terminology such as arbitrage opportunities and self-financing trading strategies. You will also gain a brief overview of Brownian motion.

Statistical methods play a crucial role in health research. This module introduces you to the key study designs used in health investigations, such as randomised controlled trials and various types of observational study.

Issues of study design will be covered from both a practical and theoretical perspective, aiming to identify the most efficient design which adheres to ethical principles and can be carried out in a feasible amount of time, or using a feasible number of patients. Various approaches to controlling for confounding will be discussed, including both design and analysis-based methods. You will also explore different types of response data, including introducing time-to-event data and the resulting challenges presented by censoring.

Real-world studies and published articles will be used to illustrate the concepts, and reference will be made to the ICH guidelines for pharmaceutical research and STROBE guidelines for epidemiological studies.

This experiential learning simulation focuses on a negotiation scenario designed to enhance your professional skills. Based on the Crossbay Contracting Game created by Adam Hindle, three health service organisations are involved in a contract negotiation, and you will act as part of the management team for one of these organisations. The main aim is to reach an agreement that is satisfactory to all three parties.

Public policy analysis is the study of government’s role in the economy. It involves examining both its normative and positive aspects. To gain a comprehensive understanding, we look at a combination of theories, empirical findings, and real-world examples.

We begin by focusing on public goods such as water, transportation, and other infrastructure that the government can provide directly or in collaboration with the private sector. This includes looking at the practice of regulators, as well as cost-benefit analysis. We evaluate the trade-off between efficiency and fairness, then examine state financing, including theories of optimal taxation and recent research on tax evasion and avoidance. Finally, we delve into the internal structure of government, exploring political economy and fiscal federalism.

Stochastic processes are fundamental to probability theory and statistics and appear in many places in both theory and practice. For example, they are used in finance to model stock prices and interest rates, in biology to model population dynamics and the spread of disease, and in physics to describe the motion of particles.

During this module, you will focus on the most basic stochastic processes and how they can be analysed, starting with the simple random walk. Based on a model of how a gambler's fortune changes over time, it questioned whether there are betting strategies that gamblers can use to guarantee a win. We will focus on Markov processes, which are natural generalisations of the simple random walk, and the most important class of stochastic processes. You will discover how to analyse Markov processes and how they are used to model queues and populations.

Statistics and machine learning share the goal of extracting patterns or trends from very large and complex datasets. These patterns are used to forecast or predict future behaviour or interpolate missing information. Learn about the similarities and differences between statistical inference and machine learning algorithms for supervised learning.

You will explore the class of generalised linear models, which is one of the most frequently used classes of supervised learning model. You will learn how to implement these models, how to interpret their output and how to check whether the model is an accurate representation of your dataset. Lastly, you will have the opportunity to see how these models can be extended to the case of the ‘large p, small n’ question. This phrase refers to the situation in which there are many more variables than there are samples, something which is now commonplace.