BSc Hons Mathematics, Statistics & Economics

You'll study the basic concepts and standard methods of mathematical notation and proof, polynomial equations and inequalities, sequences and series, functions, limits and continuity, differentiation and integration.

The fundamental concepts of calculus (differentiation and integration) presented in Applications of Calculus will be examined in more detail, extended to a larger class of functions by means of more sophisticated methods, including an introduction to complex numbers and variables, all demonstrated in application to practical problems including solving basic first and second-order differential equations.  

This class will introduce you to vectors and matrices, along with the idea of mathematical modelling through their application to real-world problems.

Some basic ideas and techniques of statistics will be presented while introducing some essential study skills, allowing you to develop and practice personal and technical skills eg self-study, teamwork, analysing data, writing reports and making presentations.

The module will provide you with a balanced introduction to economics which will be based on a programme of systematic directed reading, supplemented by experiments and exercises undertaken in tutorials. 

The module uses the innovative COREecon resources, which provides a complete introduction to economics and the economy. COREecon teaches about the economy and economics by starting from a question or a problem about the economy - why the advent of capitalism is associated with a sharp increase in average living standards, for example - and then teach the tools of economics that contribute to an answer. This innovative approach ensures that students understand how the tools of economics can help us understand the modern economy. 

The second half of the module is the study of how analytical thinking, scientific method and associated tools can be used to help decision making. This Business Analysis element of the module will provide an overview of where methods and tools are widely used across a large range of industries including the manufacturing, retail, healthcare, financial services, travel, and electronics industries, as well as in local and national government. 

Examples of where Business Analysis is put into practice are: 

• the management of new building projects
• the design of efficient transport systems and plant layouts
• personnel scheduling
• allocation of resources and financial modelling and forecasting

This area of expertise can help to reduce costs, increase revenues, improve customer service, increase efficiency and can even save lives. 

This class will introduce you to the basic ideas of linear algebra, such as matrices and determinants, vector spaces, bases, eigenvalues and eigenvectors. You'll study various standard methods for solving ordinary differential equations and understand their relevance.

Basic ideas, techniques and results for calculus of two and three variables, along with differentiation and integration over curves, surfaces and volumes of both scalar and vector fields will be presented.

Presentation of the basic concepts of probability theory and statistical inference will be covered to provide you with the tools to appropriately analyse a given data set and effectively communicate the results of such analysis.

This class will introduce you to the R computing environment. It'll enable you to use R to import data and perform statistical tests, allow you to understand the concept of an algorithm and what makes a good algorithm and will equip you for implementing simple algorithms in R.

This is the core microeconomics class in second year. It aims to develop students’ understanding of: the concepts of consumer choice; the motives of the firm and profit maximisation; the market and its role in achieving equilibrium prices and quantities; and the implications of market power. It will introduce to students to mathematical techniques commonplace in economics, giving them the ability to apply these in a wider economic context.

The class builds upon the macroeconomic foundations established in the first year Economics class and both extends and deepens analysis. In particular this class will develop students’ ability to use key models used to analyse the determination of output in the short-run and in the medium-run. We will also cover some topics in the analysis of economic data including correlation and simple regression.

This class builds on the conceptual framework of Microeconomics 2 by introducing students to market imperfections through the lens of game theory. Game theory is an analytical toolbox that allows us to understand decision making in strategic environments and the class will provide students with a foundation in game theoretic reasoning.

Thereafter, this class will study how market imperfections create a role for Government in markets. In doing so, we will investigate topics including taxation, education, and crime and punishment. This class will also look to explore some of these ideas empirically using cross section data. These empirical methods will open up a new route for students to understand the key taught economic ideas in a real world context.

The overarching aims of this class are: to extend students' knowledge and understanding of core macroeconomic models and of the econometric methods used to analyse macroeconomic data; and to develop their ability to apply these models and methods to address some key questions in macroeconomics. The class covers: economic growth; expectations, demand and policy; Europe: past, present, and future; and policy rules versus discretion. In addition, econometric methods covered include: regression; unit root testing; modelling volatility; cointegration and error correction.

We'll introduce you to analytical methods for solving ordinary and partial differential equations so you'll develop an understanding along with technical skills in this area.

This class will:

• review the concepts of probability distributions and how to work with these
• present approaches to parameter estimation, focusing on maximum likelihood estimation, bootstrap estimation, and properties of estimators
• present hypothesis testing procedures, including classical likelihood ratio tests and computer-based methods for testing parameter values, and goodness-of-fit tests.
• introduce and provide understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures
• present use of R functions for regression and interpretation of R output

This class will introduce functions of a complex variable, define concepts such as continuity, differentiability, analyticity, line integration, singular points, etc. It'll examine some important properties of such functions, and consider some applications of them, eg conformal mappings, and the evaluation of real integrals using the Residue Theorem. It'll also introduce you to Fourier and Laplace transform methods for solving linear ordinary differential equations and convolution type integral equations.

Here we'll introduce basic algebraic structures, with particular emphasis on those pertaining to finite dimensional linear spaces and deepen your understanding of linear mappings. We'll also provide an introduction to inner product spaces and bilinear forms.

This module will motivate the need for numerical algorithms to approximate the solution of problems that can't be solved with pen and paper. It'll develop your skills in performing detailed analysis of the performance of numerical methods and will continue to develop your skills in the implementation of numerical algorithms using R.

You'll be introduced to the basic concepts of random phenomena evolving in time, from two complementary points of view: probabilistic modelling and data-driven analysis. Presentation of underlying ideas of simple stochastic processes, time series models, and the associated probability theory and statistical techniques will be covered. In addition to applications of the methods to financial and economic systems, including modelling, data analysis, and forecasting.

This class provides you with experience of the skills required to undertake project work, and to communicate the findings in written and oral form using a variety of sources, such as books, journals and the internet.

This class will provide you with a range of applied statistical techniques that can be used in professional life.

You'll be introduced to a range of modern statistical methods and practices used in industry, commerce and research, and will develop skills in your application and presentation.

This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

Topics covered will include:

• survival analysis
• analysing categorical data using hypothesis tests
• experimental Design and sampling
• clinical measurement

An introduction to ideas in mathematics and statistics that can be used to model real systems, with an emphasis on the valuation of financial derivatives. The class places equal emphasis on deterministic analysis (calculus, differential equations) and stochastic analysis (Brownian motion, birth and death processes).  In both cases, in additional to theoretical analysis, appropriate computational algorithms are introduced.  The first half of the class introduces general modelling and simulation tools, and the second half focuses on the specific application of valuing financial derivatives, including the celebrated Black-Scholes theory.

In the third year econometrics class the student will have learned about regression in both a cross-sectional data and time series data context. This class extends that knowledge in three ways.

First, for cross-sectional data, the class deals with regression techniques where the dependent variable may be restricted or limited in some way. In such cases, the regression model as taught in the third year class is not appropriate; EC413 develops models which are similar in spirit to the standard regression model, but can handle all of these cases.

A second purpose of Applied Econometrics is to develop regression methods which can be used when you have panel data - consisting of both cross-sectional and time-series dimensions.

Third, the class will build on the introduction to the econometrics of time series data developed in a previous class by developing two classes of models. One class consists of models that can accommodate multiple long run equilibrium relationships among a set of possibly non-stationary variables. A second class consists of methods for modelling volatility.

Risk and uncertainty are ubiquitous in everyday life and they affect the way in which economic agents make their choices. This course will start analysing how modern economic theory deals with uncertainty and the way in which individuals makes their decisions under uncertainty. Attitude towards risk, decision-making under uncertainty, the role of insurance and financial markets will be covered in the first part of the course.

The second part will present the foundation of the Economics of Information. Building on the first part of this course and on the game theoretic framework covered in Microeconomics 3, the study of Economics of Information will provide tools for the analysis of economic situations and contracting issues when either monitoring is imperfect or information is asymmetric.

This class is intended to show how economic theory can be used to analyse individual behavior when information is not complete and decisions are risky. In particular, it will be applied to economic and business problems but also social interaction issues. In addition, the relevance of incentives and information in many economic environments will be highlighted, with particular emphasis on situations in which moral hazard and adverse selection problems arise.

This course is useful in enhancing students’ understanding of other modules and will be of particular use in students’ dissertations. Moreover, students will gain key skills in strategic thinking and the analysis of information asymmetries that will be useful both in further study and employment.

This class aims to provide students with the required tools to understand current macroeconomic issues, such as: the interactions between the banking sector and monetary policy; or the policy responses to the global financial crisis. Throughout the class the analytical usefulness of the theoretical models taught is illustrated with real-world examples.

This course will examine fundamental topics in Labour Economics. The primary goal is for you to learn how economists view the world of work and pay. The focus is on applications rather than theory. Topics include human capital and signaling, discrimination, the effect of technology on the wage structure, and income inequality. Along the way, you will gain insights into data analysis and empirical methods. These are highly marketable skills that tend to be entry requirements for consulting jobs, government work, and many other sectors.

Here you'll get an introduction to ideas in mathematics and statistics that can be used to model real systems, with an emphasis on the valuation of financial derivatives. This module places equal emphasis on deterministic analysis (calculus, differential equations) and stochastic analysis (Brownian motion, birth and death processes). In both cases, in addition to theoretical analysis, appropriate computational algorithms are introduced. The first half of the class introduces general modelling and simulation tools, and the second half focuses on the specific application of valuing financial derivatives, including the celebrated Black-Scholes theory.

This class will present the main results in Functional Analysis, give an introduction to linear operators on Banach and Hilbert spaces and study applications to integral and differential equations.

You'll be introduced to the theory of Newtonian fluids and its application to flow problems and the dynamics of waves on water and in other contexts.

You'll be presented with the basic theory and practice of finite element methods and polynomial and piecewise polynomial approximation theory.

Here you'll learn the application of mathematical models to a variety of problems in biology, medicine, and ecology. It'll show the application of ordinary differential equations to simple biological and medical problems, the use of mathematical modelling in biochemical reactions, the application of partial differential equations in describing spatial processes such as cancer growth and pattern formation in embryonic development, and the use of delay-differential equations in physiological processes. The marine population modelling element will introduce the use of difference models to represent population processes through applications to fisheries, and the use of coupled ODE system to represent ecosystems. Practical work will include example class case studies that will explore a real-world application of an ecosystem model.

This class will demonstrate the central role network theory plays in mathematical modelling. It'll also show the intimate connection between linear algebra and graph theory and how to use this connection to develop a sound theoretical understanding of network theory. Finally, it'll apply this theory as a tool for revealing structure in networks.

This course will investigate the actions firms can take in markets with the aim of maintaining their market power by deterring entry of additional firms. A modern approach recognises that each firm faces competing firms who all have similar motives.

After briefly reviewing industry market structures to provide the basis of the analysis, issues of entry deterrence will be considered, including the role of advertising, issues associated with research and development, anti-trust and both vertical integration and horizontal mergers. The course will conclude with an investigation of regulatory measures that can be put in place to limit anti-competitive strategies, and consider the role of competition policy.