BSc (Hons) Economics and Mathematics Bachelor's degree at the University of Sheffield

This module introduces you to the fundamental principles of microeconomic theory, focusing on the behaviour and decision-making processes of individuals, households, and firms. You will gain a solid understanding of key microeconomic concepts, such as choice theory, market structures, consumer behavior, and firm decision-making. The module explores the practical application of microeconomic analysis to contemporary policy issues. This module provides you with the foundations in microeconomics re

This module introduces you to the foundations of macroeconomic theory and analysis. You will explore how economies operate at the aggregate level and develop the analytical tools needed to understand key economic indicators such as interest rates, national output, inflation, unemployment, and exchange rates. A central focus will be on understanding how these variables interact and influence one another over the short and long run. Through real-world examples and policy applications, you will lea

This core module is designed to consolidate A-level material and explore topics in mathematics that you'll use throughout your degree. You'll also be introduced to core skills, such as mathematical literacy, communication and problem-solving. Throughout this module you'll develop a strong foundation in core mathematics. You'll consider techniques for solving equations, special functions, calculus, vectors, complex numbers, and finite and infinite series.

This core module is designed to further develop your knowledge of the core mathematics you'll use across your degree. You'll learn about two-dimensional coordinate geometry, discussing the theory of matrices geometrically and algebraically. You'll also define and evaluate derivatives and integrals for functions that depend on more than one variable, with an emphasis on functions of two variables. Throughout this module you'll continue to develop your employability skills, exploring the career op

Probability theory is branch of mathematics concerned with the study of chance phenomena. Data science involves the handling and analysis of data using a variety of tools: statistical inference, machine learning, and graphical methods. The first part of the module introduces probability theory, providing a foundation for further probability and statistics modules, and for the statistical inference methods taught here. Examples are presented from diverse areas, and case studies involving a variet

This module collects together a number of the most useful topics for future modules in financial mathematics. The first semester will cover some computing, using Python to write simple programmes and LaTeX to write mathematical documents. The second semester is given to analysis, a branch of pure mathematics with applications in financial mathematics. The two semesters are independent of each other.

This module builds on Level 1 modules in microeconomics and mathematical economics, using the mathematical training to allow a more rigorous investigation of the principles of microeconomics. It aims to develop an understanding and ability to undertake economic analysis of models of the behaviour and interaction of economic agents (consumers, firms and government) in a market economy, the functioning of different types of industries, decision making under uncertainty and economic welfare.

The aims of this course are to provide firm grounding in the analytical tools of modern macroeconomics; to use these tools to understand critically the conduct of economic policy nationally and internationally. The course builds on level 1 modules in macroeconomics. The main subject areas covered are: Basic macroeconomic models: consumption/leisure choice, closed economy one period-macro models, models of search and unemployment; Savings, investment and government deficits: consumption/savings c

This module is designed to give you an introduction into the econometric skills you will need for a successful career in applied economic research. Throughout this module, you will build a strong foundation in interpreting econometric outputs, crucial for understanding academic literature and policy reports. You will learn fundamental theoretical knowledge, such as ordinary least squares, supported by economic intuition and mathematical reasoning. You will learn statistical software, gaining an

Linear algebra and calculus are fundamental to most advanced work in pure and applied mathematics and to much of statistics. This module will provide you with basic tools and techniques from linear algebra and calculus. You'll also develop an understanding of the theory underpinning these, enabling you to use these methods in a variety of situations beyond the module. Building on your first year, you'll also continue to develop employability skills throughout this module.

This module will develop your understanding of theory covered in first year modules. You'll explore analysis, which underpins core concepts across the mathematical sciences. You'll examine why familiar tools, like differentiation and integration, actually work, allowing us to prove their formal properties. This rigorous foundation in analysis will enable you to tackle more complex problems in the future. You'll extend your understanding of ideas from calculus to higher dimensions, considering di

This module will build on the theory built in Level 1 'Foundations of Pure Mathematics', focusing on the twin pillars of analysis and algebra. These are not only fundamental for pure mathematics at higher levels, but provide rigorous theory behind core concepts that are used throughout the mathematical sciences. Whilst to some extent you have been doing analysis and algebra since you were at school, here you will be going much deeper. You will examine why familiar tools, like differentiation and

In this project module, you will investigate one or more case studies of using mathematics and statistics for solving empirical (i.e. 'real world') problems. These case studies will illustrate the process of mathematical and statistical modelling, whereby real-world questions are translated to mathematical and/or statistical questions. Students will see how techniques learned earlier in their degree can be used to explore these problems. There will be a mix of individual and group projects to ch

Statistical inference and modelling are at the heart of data science, a field of rapidly-growing importance in the modern word. This module develops methods for analysing data, and provides a foundation for further study of probability and statistics at higher Levels. You will learn about a range of standard probability distributions beyond those met at Level 1, including multivariate distributions. You will learn about sampling theory and summary statistics, and their relation to data analysis.

Many things about life are unpredictable. Consequently, it often makes sense to incorporate some randomness in mathematical models of natural and physical processes. Such models are called 'stochastic models' and are the study object of this module. We will learn about a number of general models for processes where the state of a system is fluctuating randomly over time. Examples might include the length of a queue, the size of a reproducing population, or the quantity of water in a reservoir. W

It is essential for you as an economist to understand and be able to apply econometric techniques because it will allow you to rigorously analyse data to test economic theories, measure real-world effects, and produce credible evidence to inform decisions and policy. In this module you will study a number of advanced topics in econometrics. In the first half of the module you will learn how to implement recent developments in time series econometrics.