BSc Mathematics and Data Science
Please note: This page is for 2027 entry. Click here for 2026 entry.
| UCAS code | GG18 |
|---|---|
| Duration | 3 years |
| Entry year | 2027 |
| Campus | Streatham Campus |
| Typical offer | A-Level: AAA-AAB |
|---|---|
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A-Level: ABB-ABC |
| UCAS code | GG22 |
|---|---|
| Duration | 4 years |
| Entry year | 2027 |
| Campus | Streatham Campus |
| Typical offer | A-Level: AAA-AAB |
|---|---|
|
A-Level: ABB-ABC |
Why study BSc Mathematics and Data Science at Exeter?
- An interdisciplinary degree designed in partnership with industry and combining traditional mathematical techniques with exciting contemporary applications in the field of data science
- Allows you to use mathematics to develop a deeper understanding of the processes behind data manipulation
- Become expert at handling large and complex datasets and understand the statistical considerations which can affect results, such as bias and uncertainty
- Study topics such as machine learning, artificial intelligence, statistical modelling and programming
- Research projects in each academic year will allow you to develop project management skills in your area of interest, using real world datasets and guided by an academic supervisor
- Opportunity to extend your degree and spend a ‘Year in Industry’ at companies such as Lloyds Banking Group, Coca-Cola, Met Office and PwC
Discover Mathematics at the University of Exeter.
Top 20 in the UK for Mathematics
20th in The Times and The Sunday Times Good University Guide 2026 and the Complete University Guide 2027
92% of Mathematics graduates in or due to start employment or further study fifteen months after graduation
Based on full-time, first degree, UK domiciled graduates, HESA Graduate Outcomes survey 2021/22
Long established partnership with the Alan Turing Institute and home to Exeter’s Institute of Data Science and Artificial Intelligence
Spend a year in industry as part of your degree
These courses will meet the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications (IMA), when they are followed by subsequent training and experience in employment to obtain equivalent competences to those specified by the Quality Assurance Agency (QAA) for taught masters degrees.
Entry requirements (typical offer)
| Qualification | Typical offer | Required subjects |
|---|---|---|
| A-Level | AAA-AAB |
GCE A-Level Maths grade A
Candidates may offer GCE A-Level Maths, Pure Maths or Further Maths. |
| IB | 36/666-34/665 | HL6 in Mathematics (Analysis and Approaches) |
| BTEC | DDD | Applicants studying a BTEC Extended Diploma are also required to achieve grade A at A-Level in Mathematics, Pure Mathematics or Further Mathematics. |
| GCSE | 4 or C | Grade 4/C in GCSE English language |
| Access to HE | 30 L3 credits at Distinction Grade and 15 L3 credits at Merit Grade | 15 L3 credits at Distinction Grade in an acceptable Mathematics subject area |
| T-Level | T-Levels not accepted | N/A |
| Contextual Offer | A-Level: ABB-ABC |
Specific subject requirements must still be achieved where stated above. Find out more about contextual offers. |
| Other accepted qualifications | ||
| English language requirements |
International students need to show they have the required level of English language to study this course. The required test scores for this course fall under Profile B1. Please visit our English language requirements page to view the required test scores and equivalencies from your country. |
|
NB General Studies is not included in any offer.
Grades advertised on each programme webpage are the typical level at which our offers are made and provide information on any specific subjects an applicant will need to have studied in order to be considered for a place on the programme. However, if we receive a large number of applications for the programme we may not be able to make an offer to all those who are predicted to achieve/have achieved grades which are in line with our typical offer. For more information on how applications are assessed and when decisions are released, please see: After you apply
International Foundation programmes
Preparation for entry to Year 1 of an undergraduate degree:
Course content
First year modules introduce you to the fundamental technical and professional skills needed to understand and engage with machine learning, artificial intelligence and data science. You will learn core knowledge and practical skills relating to data structures and algorithms that are commonly applied in this topic area.
Second year compulsory modules further develop your knowledge of computational intelligence, data science in society and software development, giving you a broader skill set to continue on to your final year.
In your final year you’ll choose from advanced modules in a wide range of topics. Industry-linked projects also take place and work placement opportunities are recommended. This variety of learning gives you practical experience and the confidence to conduct individual research, applying your expertise to solve real mathematical problems and find computing solutions.
You may notice changes to some of our modules over the coming months. This is because we are making space for the following:
- Minors: Future Skills Pathways - Alongside your main degree you may be eligible (depending on your course) to choose modules from another subject to broaden your skills and interests.
- Skills to Thrive built into every degree - Essential skills for your future, including communication, problem-solving, teamwork and digital confidence.
- Increased innovation and wellbeing - More room for creative learning, real-world projects and a healthier study rhythm.
The modules below provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.
Please note that the module information displayed here is subject to change.
120 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Fundamentals of Machine Learning | 15 | |
| Programming | 15 | |
| Social and Professional Issues of the Information Age | 15 | |
| Object-Oriented Programming | 15 | |
| Foundations | 0 | |
| Mathematical Methods | 30 | |
| Probability, Statistics and Data | 30 | |
COM1011: Fundamentals of Machine Learning
Differently from traditional software, artificially intelligent software can improve performance upon ingesting increasing quantities of data. This module will introduce you to the core concepts that are needed to understand the field of Artificial Intelligence and Machine Learning. You will learn about the principal paradigms from a theoretical point of view and gain practical experience through a series of workshops. In this module we will emphasize the notion and importance of data and you will learn how machines can deal with different types of data sources, ranging from images and text to networks and user preferences.
Co-requisite Modules: ECM1400, MTH1002, MTH1004, or equivalent.
This module is suitable for students with sufficient preparation in Mathematics and Programming.
This module aims to equip you with the fundamental notions to understand and identify the compromises and trade-offs that must be made when using a machine learning approach. It will provide the foundations to understand the principal flavours of machine learning techniques. Emphasis will be placed on how to work effectively with different information sources.
ECM1400: Programming
We use computers in almost all aspects of our daily lives and throughout science, so it is easy to take them for granted. However, in order that we can use computers to solve new problems and create new things, we have to be able to program them. This module introduces you to programming and problem solving with a computer. You will learn how to formulate an algorithm to solve a problem, and you will acquire the skills to write, test and debug programs.
This module is an introductory course in computer programming and will introduce you to the fundamental concepts of computer algorithms and programming, with a strong emphasis on practical implementation. You will also learn how to apply analytical and problem-solving skills to the design and implementation of small applications.
ECM1407: Social and Professional Issues of the Information Age
The module aims to provide you with the tools to reflect upon your role in the interface between digital technologies and society and on the moral and ethical use of information and information systems. By taking this module, you will become aware of your legal responsibilities and rights as an IT professional and as a user of digital technologies. The module will cover ethical theories, computer law and professional codes of conduct, and will address the ways in which broader areas of law (e.g. defamation, contracts, privacy and freedom of information legislation) impact upon technology users and IT professionals.
This module will introduce you to the law regulating the use of information and digital technology. It will enhance your awareness and critical thinking skills regarding the social impact of information technology, and help you relate professional codes of conduct to ethical theories.
ECM1410: Object-Oriented Programming
This module will introduce you to object-oriented problem-solving methods and provide you with object-oriented (OO) techniques for the analysis, design and implementation of solutions. We will introduce you to these concepts, and you will develop skills with a new programming language. By the end of this module, you will be able to apply these skills to design and implement small applications.
The module aims to provide you with a thorough grounding in the fundamentals of object-oriented design concepts, alongside the fundamentals of the Java programming language, and general object-oriented design concepts. It will also introduce you to widely used components of the unified modelling language (UML), teach you how to interpret and implement a Java program from these higher-level designs, along with the pair programming approach used in industry.
MTH1000: Foundations
University level mathematics differs from that taught in schools not only in the difficulty of the topics and higher abstraction, but also in the style of teaching. This module aims to ease the transition to university level mathematics by bridging the gap between mathematics taught prior to university level, and the material covered in the first year of our mathematics degree, including the programming languages which will be taught in depth in other modules. The module eases you into a university teaching and learning environment and helps revise material from A-level. You will revisit essential skills in algebra, coordinate geometry, vectors, series and sequences, as well as some topics which are covered in Further Mathematics A-level such as complex numbers, matrix algebra, differential equations, and Maclaurin series. In this module, you will go over the theory and see many solved examples, as well as practice many examples to master these essential topics. Attending the lectures of this module is highly recommended to those students who do not have an A-level in Further Mathematics or equivalent, but those who do can also utilise these sessions to review the material and gain more practise experience. This module will also provide the skills needed to communicate mathematics which is a vital skill in all modules to be taken throughout a mathematics course.
MTH1002: Mathematical Methods
During your mathematics degree, you will be solving problems and proving theories in several branches of mathematics such as in pure mathematics, in applications to science and engineering, and in statistics. Inevitably you need to be able to calculate. That is what gives the mathematics its great power. This module covers developed bodies of useful techniques as a toolkit of common knowledge. It brings emphasis on the techniques rather than the applications of the techniques. Such techniques will enable you to deepen your familiarity with, and generalise, methods that you have seen at school level mathematics. This module will study topics that include the geometry of conic sections, properties of functions such as continuity and differentiability, differential and integral calculus, limits and convergence of sequences and series including Power Series and Taylor Series. The module also develops the fundamentals of vector and matrix theory, multivariate calculus, and the classification of various types of differential equations as well as analytical methods for solving them. The material in this module provide intuition for, and examples of, many of the mathematical structures that will be discussed in the module MTH1001 Mathematical Structures, and supply a firm understanding of methods required in future modules in the mathematics degree. In particular, it develops methods that underpin the modules MTH2003 Differential Equations and MTH2004 Vector Calculus and Applications.
MTH1004: Probability, Statistics and Data
Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the mathematics of combinatorics and probability, and the key ideas of statistical modelling and inference, in which probability is used to quantify uncertainty. You will also gain experience of employing these ideas to analyse data using statistical software such as the R programming environment. The module develops key ideas and techniques that form the foundation of modules such as MTH2006 Statistical Modelling and Inference.
The aim of this module is to introduce you to basic topics in probability, statistics and data analysis. This module provides the foundation for the second-year stream in Statistical Modelling and Inference, and subsequent modules in statistics in years 3 and 4.
Please note that the module information displayed here is subject to change.
90 credits of compulsory modules, 30 credits of optional modules (You can select between COM2104, MTH2015 or any free choice elective module).
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Machine Learning and Data Science | 15 | |
| Team Project | 15 | |
| Differential Equations | 15 | |
| Vector Calculus and Applications | 15 | |
| Statistical Modelling and Inference | 30 | |
COM2011: Machine Learning and Data Science
This module will improve your knowledge and skills in machine learning and data science. You will gain theoretical and practical understanding of some of the core techniques in machine learning (including supervised/unsupervised methods, feature extraction, binary classification, elementary text and image analysis, amongst others). You will also understand how machine learning and other techniques are combined in effective data science workflows, alongside some of the practical challenges faced in real-world data science, such as handling missing or erroneous data, linking different datasets, and data visualisation.
This module is suitable for students with sufficient preparation in Mathematics and Programming.
This module aims to equip you with the fundamentals of machine learning and data analysis. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods. We will pay particular attention to methods for visualising complex datasets.
COM2020: Team Project
This module gives you the opportunity to work collaboratively on a substantial practical problem, which you will solve from a computational and data-based perspective. Teams will apply technical skills in software development and data analysis while managing project planning, teamwork, and communication. During the module you will design and develop a solution that balances innovation with feasibility, and develop a prototype that you demonstrate tackles the project according to specific measures of success. You will develop the professional skills required to succeed in technology and data-driven industries.
The aim of this module is to equip you with the necessary practical and theoretical skills to enable you to develop and implement a computational and data-driven solution to a given problem. Early in the module you will be presented with a realistic problem, and you will be asked to work within a team to propose, develop, and implement a solution to the problem. You will learn how to apply a range of evaluation measures to evaluate the success of your team’s solution. Throughout the module you will learn about and deploy teamwork skills to ensure the success of your project.
MTH2003: Differential Equations
Differential equations are at the heart of nearly all modern applications of mathematics to natural and man-made phenomena. Mathematically, all rates of change and acceleration can be described by derivative functions. These include the growth of populations, the spread of diseases, movement of physical objects in response to forces acting on them, or even the fluctuations of the stock market. This course will enable you to demonstrate an understanding of, and competence in, a range of analytical tools for posing and solving differential equations, and their application to situations in science and technology.
MTH2004: Vector Calculus and Applications
This module introduces vector calculus and its applications in particular fluid dynamics and electromagnetism. The module consists of two parts, which are closely linked. In the first part of the module, you will learn about the mathematical theory and techniques of vector calculus. You will develop your competence in using vector calculus in both differential and integral forms. The second part of the module gives an introduction to fluid dynamics and electromagnetism as two applications of vector calculus. It lays down some basic principles using a number of simplifying assumptions.
This introductory vector calculus course aims to increase your understanding of fluid dynamics and electromagnetism. It examines how one can use vector formalism and calculus together to describe and solve many problems in two and three dimensions. For example, the rules that govern the flow of fluids can be described using vector calculus, with resulting laws of motion described by partial differential equations rather than ordinary differential equations.
MTH2006: Statistical Modelling and Inference
Statistical modelling lies at the heart of modern data analysis, helping us to describe and predict the real world. Statistical inference is the way that we use data and other information to learn about and apply statistical models. In this module, you will learn the theory underpinning modern statistical methods such as fitting normal linear models, evaluating how well they fit the data and taking inferences from it. You will apply the theory using statistical software such as R to analyse and draw conclusions from a range of real-world data sets. Topics covered in the module range from estimators, confidence intervals, design of experiments and hypothesis testing to statistical modelling, regression, inference and comparison of models. Skills developed in the module are taken further in modules such as MTH3012 Advanced Statistical Modelling.
This module aims to develop understanding and competence in statistical modelling by introducing you to the Normal linear model from a modern perspective. It will provide you with the ability to formulate and apply these models in a range of practical settings, to carry out associated inference appreciating how this relates to the general likelihood inferential framework, and to perform appropriate model selection and model checking procedures. Use will be made of a suitable statistical computer language for practical work.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Computational Intelligence | 15 | |
| Mathematics of Machine Learning and AI | 15 | |
COM2014: Computational Intelligence
Computational intelligence is the science of computational systems that are able to perform specific tasks, adapting to particular data. The module will equip you to design and use computational intelligence to solve a variety of problems such as planning, scheduling, optimisation, using a variety of techniques including biologically inspired computational, fuzzy logic, agent-based models and simulation.
Pre-requisite Modules: COM2013 (Data Science Group Project 2); ECM1400; MTH1004
The aim of this module is to introduce and give you practice in some of the main areas of computational intelligence that can be used to solve problems arising in data science. It aims to give you and understanding of the theoretical basis of these methods and their relation to other artificial intelligence techniques. Specifically, it will introduce classical “crisp” logic and knowledge representation before proceeding to fuzzy logic to cope with uncertain and vague processes. Searching and optimisation arise in many contexts and this module aims to introduce you deterministic and stochastic optimisation methods, particularly evolutionary optimisation.
MTH2015: Mathematics of Machine Learning and AI
This module introduces mathematical foundations of modern machine learning (ML) and artificial intelligence (AI). It covers the mathematical theory of learning (PAC learning), analysis of machine learning algorithms (eg decision trees, artificial neural networks) as mathematical methods for function approximation, and gradient-based optimisation as a paradigm for training ML models for specific tasks. Practical work includes studying code examples of machine learning applications in different fields, and guided projects on advanced topics in ML and AI, such as Natural Language Processing, Formal Proof Systems, and Search Algorithms. Programming/Coding: The main programming language for the examples in this module is python. Students will receive guidance on how to translate examples from python to R. The emphasis of the course is to gain understanding of mathematical foundations of ML and AI and practical experience on worked examples and real-world applications. The module suits studePlease note that the module information displayed here is subject to change.
If you choose the 'with Year in Industry' variant of this course, your placement will take place in your third year. Find out more about the industrial placement.
120 credits of compulsory modules
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Industrial Placement | 120 | |
MTH3100: Industrial Placement
This module will provide you with extensive practical work experience in a business or commercial setting that is of direct relevance to your development as an experienced mathematician. You will apply the knowledge and skills from taught modules to scientific, business or industrial problems at a professional level. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills.
Placements will involve a substantial role in the host organisation. Individual placements are subject to availability and approval by the module leader.
Placements are normally for one year, and must be at least 6 months. International placements are acceptable. It is not required that you are paid a salary for the placement.
This module aims to provide you with the experience of working in science, business or industry in order for you to apply the knowledge and skills acquired in an academic environment to a professional work setting.
Please note that the module information displayed here is subject to change.
45 credits of compulsory modules, 75 credits of optional modules
You may select up to 2 modules (0-30 credits) from Optional Module group 1
You must select 45-75 credits from Optional Group 2
You may select up to 15 credits from Optional Group 3
You may select up to 30 credits of free choice modules at NQF Level 5 (Stage 2) or NQF Level 6 (Stage 3)
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| Compulsory 1 | ||
| Individual Literature Review and Project | 45 | |
ECM3401: Individual Literature Review and Project
This is the module in which everything you have learnt in your Computer Science studies comes together in a substantial piece of individual project work, involving initial research and literature review, and specification and design of a software system, followed by implementation, testing, evaluation, and demonstration of the system. You will work under the supervision of an individual staff member who will provide guidance and advice as appropriate.
The aim of the module is to enable you to consolidate the knowledge, understanding, techniques and skills acquired over the previous two years through the specification, design, implementation, testing, evaluation and demonstration of a software system. The module includes both initial research into the project area (including production of a literature review) and production of the system itself following an appropriate development method.
Optional modules
| Code | Module | Credits |
|---|---|---|
| Optional 1 | ||
| Data Science at Scale | 15 | |
| Probabilistic Machine Learning | 15 | |
| Optional 2 | ||
| Theory of Weather and Climate | 15 | |
| Mathematical Biology and Ecology | 15 | |
| Fluid Dynamics | 15 | |
| Partial Differential Equations | 15 | |
| Mathematics: History and Culture | 15 | |
| Graphs, Networks and Algorithms | 15 | |
| Stochastic Processes | 15 | |
| Statistical Inference | 15 | |
| Mathematics of Climate Change | 15 | |
| Computational Nonlinear Dynamics | 15 | |
| Bayesian Statistics, Philosophy and Practice | 15 | |
| Integral Equations | 15 | |
| Statistical Computing | 15 | |
| Dynamical Systems and Chaos | 15 | |
| Statistical Data Modelling | 15 | |
| Optional 3 | ||
| Commercial and Industrial Experience | 15 | |
COM3021: Data Science at Scale
Data science relies on large amounts of data to be effective and many commercial and scientific applications require the analysis of large quantities of heterogenous, noisy data on distributed machines. This module will examine the ways in which algorithms for data science can be implemented for large data and will discuss new algorithms specifically designed for large scale data. You will also work with large-scale distributed and cloud systems for storing and computing with big data.
Through theory and practice this module aims to equip you with an understanding of the principles of distributed computing, particularly on cloud-based systems, the ways in which data can be stored and accessed to allow efficient computation, and efficient algorithms for large-scale computation.
Distributed cloud computing will provide you with the underpinning knowledge required to develop and implement machine learning and artificial intelligence algorithms on distributed high-performance computing systems.
COM3031: Probabilistic Machine Learning
This module provides an advanced exploration of machine learning and artificial intelligence, focusing on probabilistic modeling, inference techniques, and structured learning methods. It also examines key theoretical foundations alongside advanced techniques, such as Bayesian Neural Networks and Variational Autoencoders, which enable uncertainty quantification and probabilistic generative modeling.. The module delves into Bayesian theory, its role in handling uncertainty, and its connections to approximate inference methods and information theory. Students will also explore techniques for modeling temporally and spatially structured data, including Hidden Markov Models. Additionally, the module introduces reinforcement learning. By integrating probabilistic reasoning, approximate inference, and structured learning, this module equips students with the theoretical depth and practical skills required for tackling complex machine learning problems.
EMP3001: Commercial and Industrial Experience
This module will provide you with an opportunity to undertake practical work experience in a business, commercial or public sector setting that is of direct relevance to your development as an experienced professional. You will apply the knowledge and skills from taught modules to authentic problem solving in the workplace, which will give you important insights into your potential job role once you graduate from university. You will be encouraged to use imagination and creativity in problem solving and to develop communication skills, planning and time management and team-working skills. Placements will involve taking responsibility for a substantial project, which may be a problem to be solved in the host organisation, in line with your degree programme. Placements are subject to availability, approval by the module convener and full compliance with important Health and Safety procedures and requirements. Placements are normally three months some time during May-September, finishing before autumn classes start. Placements must be a minimum of six weeks full time. It is understood that this will entail around 210 hours of supervised work in order to generate the depth of experience equivalent to the 125 hours of self‑directed study on a focused topic specified under the regulations, as workplace activity is not counted directly as academic study International placements are allowed. Placements can be paid or volunteer.
My course has been engaging, mixing theory with practical application. I have particularly enjoyed the machine learning modules and the opportunity to apply what I learned during a summer online placement at HSBC London. This experience not only reinforced my learning but also gave me a taste for the professional world of data science.
Joel
BSc Data Science
Fees
Tuition fees for 2026 entry
UK students: £9,790 per year
International students: £30,100 per year
Scholarships
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships*. We also provide scholarships for sport, music and other achievements, alongside regional and partner awards such as Chevening, The Beacon Trust and the British Council. Financial support is available for students from disadvantaged backgrounds, lower income households and other under-represented groups to help them access, succeed and progress through higher education.
* Terms and conditions, including deadlines, apply. See our website for details.
Course variants
BSc Mathematics and Data Science with a Year in Industry
UCAS code: GG22
The BSc Mathematics and Data Science with a Year in Industry programme includes an industrial placement which takes place in the third year of this four-year degree.
Your placement will be spent working in an appropriate business or industry related to mathematics, and you will benefit from our established connections with local, national and multinational organisations. As well as increasing your first-hand knowledge, you’ll also improve many personal and transferable skills, making new contacts and enhancing your employability.
Does it count towards my degree?
Yes, it’s worth 120 credits.
How does it affect my tuition fee?
During this year you will pay a reduced tuition fee. Visit the Tuition Fees page for more information.
How do I apply?
You can apply for this programme through UCAS using the code above. You can also transfer to the ‘Year in Industry’ programme from BSc Mathematics and Data Science during your first year.
Preparation and support
We will help you to prepare for your work placement from early in your studies. A special module 'Employability and Placement Preparation’ takes place at the start of your first year. This is an opportunity to start thinking about your placement well in advance. You will also be invited to attend workshops offering guidance and support such as ‘Making the most of your placement’ and ‘How to use your placement as an individual project’.
Learning and teaching
All our degrees involve a combination of teaching methods, including lectures, seminars, examples classes, workshops and tutorials. Most modules in mathematics involve three one-hour lectures per week, so you typically have 12 lectures per week. In the first year there are tutorial classes for each module every fortnight, except for modules involving computing or project work. Thus in the first year you would typically have around 16 contact hours per week. In the first term, the ‘Foundations’ module helps you with the transition from A level to university mathematics.
Private study and support
In addition to lectures and seminars, you should spend about 20 hours per week in private study. Working through examples and solving problems is a vital part of learning mathematics, and we advise you attempt all coursework problems, whether formally assessed or not. You will be allocated a personal tutor who will be happy to advise or put you in touch with support services and you are encouraged to discuss mathematical problems or questions with tutors and lecturers who advertise regular office hours. Extra support is available, for example through lunchtime mathematics surgeries or our peer mentor scheme, and we have an active student-staff liaison committee.
Project and computer work
There are modules at all levels that involve project work and report writing, and the final year project is a major piece of research and writing that allows you to go into depth for a specific area under the guidance of a member of academic staff. You can choose from wide range of possible project topics each year, or negotiate a topic/title with a member of academic staff. Several of the modules develop skills to use a range of modern computer tools for working with data, programming or symbolic algebra as well as typesetting and presentation.
Elective modules
Once you have mastered the foundations, our mathematics programmes offer in later years a wide range of options within the programme. In addition to the named degrees with study abroad, professional experience and year in industry, you can take optional (called elective) modules from all over the university in years 2 and 3. These options are subject to your availability, having the appropriate background (pre-requisites) and certain programme constraints.
A research and practice led culture
You will benefit from teaching by academic staff comprising internationally-recognised mathematicians, scientists and practitioners active across a wide range of topics in pure and applied mathematics, statistics and applications. As you progress through your degree, you will hear about the latest mathematical research and have opportunities (for example, the independent research project) to become actively involved in a research project yourself.
Assessment
Assessment for all degrees is through a combination of examinations and coursework. Examinations are the more important part of the process, but the coursework helps you to work steadily throughout your degree. This is particularly important in Mathematics where the subject matter develops logically as the degree progresses. Written examinations for mathematics modules are held in January and May/June of the first and second years and in May/June of each subsequent year. Some modules have tests, essays, presentations and/or project reports that contribute to the assessment.
Optional modules outside of this course
Each year, if you have optional modules available, you can take up to 30 credits in a subject outside of your course. This can increase your employability and widen your intellectual horizons.
Minors: Future Skills Pathways
You can study a Future Skills Pathway alongside your main degree by choosing up to 30 credits of modules from a different subject area in your second and final years.
Your future
Your curriculum has been developed with partners, including IBM, the Met Office, South West Water, Black Swan and Oxygen House. Throughout your studies you will conduct individual and group projects using real world data sets. Modules will use current industry methods, platforms, software and data, to ensure that they are fully reflective of workplace practice.
We’ve designed our degrees with employability firmly in mind. As well as hard skills such as programming and data analysis, you’ll develop important work place skills such as communication, presentation and teamwork.
There is an established strong market demand for suitably skilled data scientists and data science skills are increasingly being sought across many sectors, particularly by the finance and accounting industries, supermarkets, online retailers such as Amazon and the NHS.
You’ll be able to meet with local and national employers who regularly visit the university to engage with students, hosting mock interviews, CV workshops, drop-ins and lectures. This is a great opportunity for you to find out more about the day to day activities of their business and recruitment opportunities. Our Careers Service also host a wealth of employer activity, such as Careers Fairs, so you’ll never be short of chances to network with potential employers.
Career paths
The broad-based skills acquired during your degree will give you an excellent grounding for a wide variety of careers, not only those related to Mathematics but also in wider fields. Examples of roles recent graduates are now working as include:
- Accountant
- Actuary
- Analyst Programmer
- Business Analyst
- Credit Risk Analyst
- Data Science Developer
- Investment Analyst
- Software Engineer
- Statistician
- Tax Manager
