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MMath

Mathematics and Statistics MMath (Hons)

Take your knowledge of mathematical techniques and statistical principles to a higher level. The MMath includes an integrated year of Master's-level study and the opportunity to conduct a project in collaboration with an external organisation.

How to apply Apply via UCAS

Course details

Start date

Degree Type

MMath

UCAS Code

G114

Course length

4 years full-time

Typical offer

A*A*A

Tuition Fees

  • Home (Full-time): per year
  • Overseas (Full-time): per year

Overview

This challenging degree takes your study of Mathematics and Statistics to Master’s level. It is the ideal choice if you are considering postgraduate study or a career that requires high-level numeracy skills or research. The MMath combines a strong mathematical grounding with the latest developments in statistics and machine learning to provide the foundation you’ll need to step into a data-driven workplace. The first two years follow a similar structure to the BSc. The wider range of modules introduced in Years 3 and 4 explore more sophisticated mathematical and statistical techniques in greater depth. The course is based in a brand-new facility, purpose-built to meet the learning, teaching and study needs of students from the Department. You will be taught by a team of mathematicians and statisticians with a wealth of experience in industry and research. The Department is home to a number of research groups with specialisms in both pure and applied mathematics. With many members of the teaching team actively involved in research there are plenty of opportunities to link learning to the latest research in distinctive and creative ways.

Course details

Start date

Degree Type

MMath

UCAS Code

G114

Course length

4 years full-time

Typical offer

A*A*A

Tuition Fees

  • Home (Full-time): per year
  • Overseas (Full-time): per year

What you'll study

The first year begins with a broad-based introduction to pure and applied mathematics, statistics and probability and provides a sound foundation for in-depth study in subsequent years. As you move into the second and third year the focus on statistics increases. During the final year you complete a double-module project. This can be the individual project in which you tackle a theoretical area or an applied problem in depth. Alternatively, the internship project is a statistics and machine learning piece of work based on a third-party problem. Both projects can be carried out in collaboration with external organisations to add valuable real-world context to your degree.

Core modules

  • Analysis

    aims to provide an understanding of real and complex number systems, and to develop rigorously the calculus of functions of a single variable from basic principles.

  • Calculus

    builds on ideas of differentiation and integration in A level mathematics, beginning with functions of a single variable and moving on to functions of several variables. Topics include methods of solving ordinary and partial differential equations, and an introduction to Fourier Series and Fourier transforms.

  • Dynamics

    develops an understanding of elementary classical Newtonian dynamics as well as an ability to formulate and solve basic problems in dynamics.

  • Linear Algebra

    presents mathematical ideas, techniques in linear algebra and develops geometric intuition and familiarity with vector methods in preparation for more demanding material later in the course.

  • Probability

    introduces mathematics ideas on probability in preparation for more specialised material later in the course. The module presents a mathematical subject of key importance to the real-world (applied) that is based on rigorous mathematical foundations (pure).

  • Programming

    is taught via lectures and practical sessions that introduce basic principles and competence in computer programming. You will also study control structures; floating point arithmetic; and lists, strings and introduction to objects.

  • Statistics

    introduces frequentist and Bayesian statistics and demonstrates the relevance of these principles and procedures to real problems. This module lays the foundations for all subsequent study of statistics.

Core modules

  • Analysis in Many Variables

    provides an understanding of calculus in more than one dimension, together with an understanding of, and facility with, the methods of vector calculus. It also explores the application of these ideas to a range of forms of integration and to solutions of a range of classical partial differential equations.

  • Data Science and Statistical Computing

    equips you with the skills to import, explore, manipulate, model and visualise real data sets using the statistical programming language R. The module introduces the concepts and mathematics behind sampling. It also covers data protection and governance issues when working with data.

  • Statistical Inference

    introduces the main concepts underlying statistical inference and methods. This module develops the foundations underlying classical statistical techniques, and the basis for the Bayesian approach to statistics. You will also investigate and compare frequentist and Bayesian approaches.

  • Statistical Modelling

    provides a working knowledge of the theory, computation and practice of the linear model. You will cover areas including analysis of variance, model selection, diagnostics and transformation methods.

Optional modules

  • In recent years optional modules have included: AlgebraComplex AnalysisMathematical PhysicsNumerical AnalysisElementary Number TheoryGeometric TopologyMarkov ChainsMathematical ModellingProbabilitySpecial Relativity and Electromagnetism

Optional modules

  • In recent years optional modules have included: Advanced Statistical ModellingBayesian Computation and ModellingDecision TheoryMachine Learning and Neural NetworksMathematical FinanceStochastic Processes

Core modules

  • Internship Project

    The gives you the opportunity to conduct a substantial piece of independent statistics and machine learning work in a real-world context working with a third party, and to write up and present this work. This will further your analytical, collaborative and transferable skills, and your knowledge of the practice of statistics and machine learning, as well as advance your abilities in oral or written communication.

  • Project

    In the final year Project you will investigate a statistical topic of interest or perform an in-depth analysis of a data set using the tools acquired earlier in the course. You then produce a written report and give a short presentation. Subject to availability, you may have the opportunity to perform this project in collaboration with an external organisation. The project develops your research and communication skills which are important for future employment or postgraduate studies.

Optional modules

  • In recent years optional modules have included: Spatio-Temporal StatisticsDeep Learning and Artificial IntelligenceDiscrete and Continuous ProbabilityHigh-Dimensional Data AnalysisNon-Parametric StatisticsObject-Oriented StatisticsRobust Bayesian AnalysisTopics in ProbabilityUncertainty Quantification

Learning

Methods of teaching and learning include lectures, tutorials, problem classes, homework problems, written and oral presentations and individual projects. You will also take part in computer practicals, in which you learn how to implement computational methods and how to analyse real data. For most modules you will attend two lectures a week. Mathematical questions are set in lectures and may form the topic of discussion in tutorials or problem classes. The best way to learn maths is to work through problems, so in addition to independent study we recommend collaborative working with other students. The final-year project is organised around fortnightly small-group meetings with lecturers. You are free to choose the remaining modules from a range of subjects including statistics, machine learning, and probability, plus a teaching module in which you study how pupils learn in school.

Assessment

We use a combination of methods to assess the different modules, these include written examinations, computer-based examinations, project reports and presentations of project work. In your final year you also complete an in-depth project which is worth one-third of your final-year marks.

Entry requirements

Fees & Funding

Choose which fees you want to see:

Home / Island students

per year

International / EU Students

per year

Home / Island students

Part Time - per year

International / EU Students

Part Time - per year

The tuition fees shown are for one complete academic year of study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).

Find out more

Department Information

  • Mathematical Sciences offers a high-quality education that is taught by subject specialists, informed by the latest research and delivered in a stimulating academic environment. Using distinctive and creative methods, we do all we can to incorporate relevant aspects of the Department’s world-leading research into the undergraduate curriculum. We offer a range of degrees which give you a choice from a wide spectrum of pure mathematics, applied mathematics (including mathematical physics) and statistics. The overall aim is to develop you as a member of the community of professional mathematicians. Degrees combine theoretical learning with practicals and mini projects, enabling you to develop your capacity for critical thinking, problem-solving and independent learning, which will equip you with the skills to meet a variety of challenges in the workplace. We seek to develop both the generic and subject-specific skills you need to pursue a range of careers, and to further develop your skills we offer the opportunity to spend a year studying overseas or working in industry. For more information see our department pages.

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    Facilities

    The Department lies in the heart of the University on the Upper Mountjoy campus near to the University library and the science and engineering departments. We share our purpose-built £40 million new building with Computer Sciences given the natural synergy between the subjects.

    • Innovative technology

      It is home to several supercomputers, keeping our education at the forefront of innovation.

    • Cutting-edge study

      The building provides cutting-edge learning, teaching and study areas, with plenty of space for group work to deepen the Durham experience and enhance the staff-student relationship.

    Find out more

Career Opportunities

Of those UK/EU students who graduated in 2022/23:

86%

in paid employment or further study.

93%

in highly and medium skilled employment.

£33,700

is the average salary.

HESA Graduate Outcomes Survey. The survey asks leavers from higher education what they are doing 15 months after graduation. Further information about the Graduate Outcomes survey can be found here www.graduateoutcomes.ac.uk

Employability

As well as developing you academically, a Durham University degree will equip you with a range of practical skills including critical thinking, an analytical approach and ability to reason with information, alongside experience in building relationships and leading teams. A significant number of our students progress to higher level study following their degree. Some remain within their academic field of interest and pursue higher level research, notably at Durham but also other prestigious institutions. Others take a different route and pursue postgraduate programmes or employment in areas from statistics and financial management to conservation and teaching. Some of the high-profile employers our graduates have gone on to work for include Royal London, Deloitte, CERN, Morgan Stanley and Ocado.

More Information

In the Department of Mathematical Sciences, we seek to link education and research in distinctive and creative ways.

Learn more about why the department of Mathematical Sciences is a great place to study

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