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A supportive environment with great facilities and opportunities to gain work experience.
Why Plymouth is an exciting place to study mathematics
Discover what its like to study mathematics at Plymouth and how it can provide a firm basis for a successful career.
Develop your skills, knowledge and confidence with a work placement
"Applying the technical skills learnt in my degree to real problems has been invaluable".
Babbage Building: where engineering meets design
A state-of-the-art space to inspire creativity and collaboration on our campus.
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.
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.
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:
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.
UCAS tariff
112 - 128
Student | 2023-2024 | 2024-2025 |
---|---|---|
Home | £9,250 | £9,250 |
International | £16,300 | £18,100 |
Part time (Home) | £770 | £770 |
To reward outstanding achievement the University of Plymouth offers scholarship schemes to help towards funding your studies.
Be part of a close-knit, collaborative and supportive family
With small group tutorials, study space next to staff offices, our lecturers’ open-door policy, student-led learning sessions and the Maths and Computing student societies.
Underpinned by research
Whether you are interested in cryptography, spotting patterns in large datasets, finding optimal solutions using finite budgets and resources, or using a supercomputer, you can pursue your interests in this course.
Work on cutting edge commercial and scientific applications
Final year projects have included post-quantum cryptography, neural networks, financial portfolio construction, optimal wind turbine placement and medical diagnostics.
Mathematical algorithms and logic are the foundations of computer science
Studying Mathematics in conjunction with Computer Science provides you with a thorough understanding of mathematics and how algorithms work. This will prepare you for a future career in this rapidly growing and changing field.
Develop your programming skills
Develop your programming skills on computers and high-performance clusters. The first year will provide you with a solid foundation in Python. As the course progresses, other languages and software are introduced.