-
Year 1
-
Learn the underlying mathematics that underpins the rest of your degree. Master coding in the industrial software Python, right from the start and apply it in algorithms to solve real-world problems including public key cryptography. You’ll begin by building on the mathematical skills and topics you learnt at school, studying six core modules including calculus, linear algebra, numerical methods, pure mathematics, and probability. We’ve structured the curriculum so that all of our students acquire a common mathematical expertise, so you’ll also have the flexibility to move between courses as you progress.
Core modules:
- Mathematical reasoning
- Calculus
- Linear algebra
- Analysis and group theory
- Probability
- Numerical methods
Core modules
-
Mathematical Reasoning (MATH1701)
This module will introduce the basic reasoning skills needed for the development and applications of modern mathematics. It also introduces Python as a new tool for exploring and applying mathematics to real world problems. The importance of logical thinking will be investigated in various mathematical topics. This will include fundamental properties of prime numbers, their random generation and use in cryptography.
-
Calculus (MATH1702)
Calculus underpins mathematical modelling in science, finance and industry. This module gives students the ability to calculate accurately and efficiently. Key results are proven and calculus is extended to higher dimensions through partial differentiation and multiple integration. The methods covered in this module will be used by students throughout the rest of their degree.
-
Linear Algebra (MATH1703)
Vectors and matrices are fundamental in mathematics, and central to its applications in statistics, physics, data science, and engineering. This module develops practical skills in handling vectors and matrices, explores the mathematical structure of linear spaces, and elucidates their deep connections with analytic geometry.
-
Analysis and Group Theory (MATH1704)
In this module we explore two fundamental areas of pure mathematics. Analysis provides a rigorous foundation of calculus, while group theory introduces important algebraic structures that are used in many branches of pure mathematics and their applications. A rigorous approach will be taken in both topics, with emphasis on proof. Python will be used to illustrate and investigate cutting edge applications.
-
Probability (MATH1705)
An understanding of uncertainty and random phenomena is becoming increasingly important in daily life and in the modern workplace. The aim of this module is to develop the concept of chance in a mathematical framework. Random variables are introduced, with examples involving some common distributions, and the concepts of expectation, variance and correlation are investigated using mathematical tools.
-
Numerical Methods (MATH1706)
In mathematics, solving most real world problems requires the use of computers. This module introduces computational mathematics and algorithms . Students will use mathematical software interactively and write programs in Python. The numerical methods which underlie industrial, scientific and financial applications will be studied.
-
Year 2
-
In Year 2, you'll expand your rigorous understanding of mathematics, always accompanied by the study of applications. This year also includes topics in artificial intelligence, including evolutionary algorithms and machine learning. Operational research introduces Monte Carlo methods, which rely on randomness and sampling to solve impactful problems.
Core modules:
- Advanced calculus.
- Statistical inference and regression.
- Algebra and transforms.
- Differential equations.
- Operational research.
- Artificial intelligence.
Core modules
-
Stage 2 Mathematics Placement Preparation (BPIE213)
These sessions are designed to help students obtain a year-long placement in the third year of their programme. Students are assisted both in their search for a placement and in their preparation for the placement itself.
-
Artificial Intelligence (COMP2002)
This module provides students with an introduction to the principles of artificial intelligence and the methods used in that field. Topics covered include search and optimisation, knowledge representation and reasoning, and machine learning. Students will gain experience of modelling and simulation, and will apply analytical tools to evaluating results, and will consider the ethical implications of the introduction of AI.
-
Advanced Calculus (MATH2701)
In this module the geometrical and dynamical concepts needed to describe higher-dimensional objects are introduced. This includes vector calculus techniques and new forms of integration, such as line integration. Students also explore the relationships between integration and differentiation in higher dimensions. We apply advanced calculus to problems from areas such as mechanics and electromagnetism.
-
Statistical Inference and Regression (MATH2702)
This module provides a mathematical treatment of statistical methods for learning from the data abounding in the modern world. Confidence intervals and hypothesis testing are studied. Methods of estimation are explored, focusing on the maximum likelihood method. The module demonstrates the underlying theory of the general linear model. Applications are implemented using the professional statistical software, R.
-
Algebra and Transforms (MATH2703)
This module introduces mathematical structures called rings and fields, which capture properties of objects such as integers, real numbers or polynomials. These structures are used to explore error-correcting codes for data transmission. Calculus is used to introduce Laplace and Fourier transforms, and Fourier series. They are applied to solve differential equations and uncover identities involving irrational numbers.
-
Differential Equations (MATH2704)
Differential equations are used to describe changes in nature. This module introduces methods to find exact solutions to ordinary differential equations, and numerical solutions to ordinary and partial differential equations. Extensive use will be made of computational tools. The behaviour of higher dimensional systems will be analysed using the theory of continuous dynamical systems.
-
Operational Research (MATH2705)
This module gives students the opportunity to work on open-ended case studies in Operational Research (OR) and Monte Carlo methods, both of which play an important role in many areas of industry and finance. Students work both on their own and in teams to develop expertise in Operational Research and programming. They will refine their presentation and communication skills, so enhancing their employability.
-
Optional placement year
-
You'll have the opportunity to participate in an optional but highly recommended placement year, providing valuable paid professional experience and helping make your CV stand out. Typically students are paid around £17,000 and placement providers have included the Department for Communities and Local Government, Fujitsu, GlaxoSmithKline, Vauxhall Motors, VirginCare, Visteon and Jagex Games Studio.
Core modules
-
Mathematics and Statistics Placement (BPIE331)
A 48-week period of professional training is spent as the third year of a sandwich programme while undertaking an approved placement with a suitable company. This provides an opportunity for the student to gain experience of how mathematics is used in a working environment, to consolidate their previous study and to prepare for the final year and employment after graduation. Recent placement providers include GSK, the Office for National Statistics, NATS (air traffic control) and VW Group.
-
Year 4
-
In your final year, master state-of-the-art topics such as large-scale simulations, machine learning from a Bayesian perspective, and big data. Options include elliptic curve cryptography, quantum computing and optimisation of problems such as wind turbine placement. You also do an individual or group project module, which offers you the chance to study a topic of your choice in depth.
Core modules:
- Modelling and numerical simulation.
- Machine learning.
- Big data analytics.
- Project/placement.
Every undergraduate taught course has a detailed programme specification document describing the course aims, the course structure, the teaching and learning methods, the learning outcomes and the rules of assessment.
The following programme specification represents the latest course structure and may be subject to change:
BSc Hons Mathematics with Computer Science Programme Specification 7471
The modules shown for this course are those currently being studied by our students, or are proposed new modules. Please note that programme structures and individual modules are subject to amendment from time to time as part of the University’s curriculum enrichment programme and in line with changes in the University’s policies and requirements.
In light of the Coronavirus (COVID-19) pandemic, the changeable nature of the situation and any updates to government guidance, we may need to make further, last minute adjustments to how we deliver our teaching and learning on some or all of our programmes, at any time during the academic year. We want to reassure you that even if we do have to adjust the way in which we teach our programmes, we will be working to maintain the quality of the student learning experience and learning outcomes at all times.
