Mathematics BSc (Hons)
Take on this creative, investigative degree course and learn about the subject underpinning our understanding of almost everything.
You may have recently studied maths at school. You may have learnt formulas and how to apply them, how to draw graphs and how to calculate areas and volumes. How did you know that the formulas would work? Who was the first person to use that formula – and how did they know it would work?
- UCAS Code – G100
- Duration – 3 years full-time | 6 years part-time
- Start date – September 2021
- School – Psychological & Social Sciences
Minimum Entry Requirements
96 points to include a C in Maths A-Level
3 GCSEs Graded C/4 or above (or equivalent) including English Language and Maths
UK and EU 2020-21 £9,250 per year
International 2020-21 £12,750 per year
The York St John Experience
Studying Mathematics at degree-level is about much more than just applying what you already know to a series of problems. It is a creative, investigative subject where you will push the boundaries of what is possible to achieve through thinking and then apply it to the world.
There are amazing discoveries to be made everywhere, but no science has the reach and impact of mathematics - touching on art, chemistry, music, design, construction, journalism, finance, healthcare, physics, geography, technology, driving, shopping, in fact… underpinning our understanding of almost everything!
Our Honours course in Mathematics is designed to be intellectually stimulating and will appeal to students who like to tackle real-world challenges through the rigorous and logical methods offered by mathematics. The programme will develop your skills across pure and applied aspects of the subject in algebra and analysis, and combines these with training in probability, advanced statistics, modelling and data analysis.
Our degree will develop your analytical skills as mathematicians alongside other skills that are essential in the modern world and workplace, drawing on expertise from across the University. You will learn skills in communication to help you write and talk about your work, programming to harness the immense power of computers, as well as entrepreneurship and enterprise so that you can apply your new knowledge to different work environments.
In a world that is increasingly reliant on data and modelling, having confidence in researching and applying quantitative concepts can be hugely beneficial. For students with a passion for understanding the world from a mathematical perspective, we can provide you with the skills, experience and support to equip you for a range of future careers.
Level 1 develops the core areas of algebra, analysis and statistics, bridging the gap between school and university mathematics and laying the foundations for later advanced topics.
You will gain transferable skills in computer programming, data analysis, creative problem solving, and science communication as well as communication more generally. You will have a thoroughly interdisciplinary learning experience by being co-taught with computer scientists and data scientists in certain modules. Often working in groups you'll develop the ability to see problems from different disciplinary perspectives and communicate your insights to specialists outside your field
Linear Algebra (20 credits)
In Maths and Physics we can use vectors to describe the locations of points in real space. In Data Science we can use these same vectors to describe the locations of data points in other, more abstract spaces. By picturing data in this way – imagining it existing as a series of locations, shapes or geometric objects – we can uncover a special way of understanding the data.
In this module you will find out about modelling space, how to describe geometric objects and their transformations (ways of moving them - through rotation, reflection or other movements). Through this study you will gain the ability to model abstract space, learning important techniques for visualising and transforming physical objects.
Programming 1 (20 credits)
Programming will provide an introduction to harnessing the power of computers to solve problems, analyse and then present data. The vast quantities of information in Big Data make it necessary to automate as much of the analysis as possible. And in other areas of mathematics computers allow you to solve much harder problems than what you could do by hand!
Practice in Interdisciplinary Problem Solving (20 credits)
This module will bring you together in a multidisciplinary context to contribute your unique knowledge and skills to solving problems collaboratively. You will learn the basics of scientific writing, and how to apply your computing knowledge to modelling and creating simulations of topics from a wide range of different areas such as biology, physics, chemistry or finance. This versatility is part of the beauty of mathematics underpinning everything!
Analysis & Optimisation (20 credits)
Analysis describes the continuous functions underpinning all mathematical modelling. Some of these topics – such as differentiation and integration - might be familiar from school. Analysis can also help us improve processes and organisations by optimisation: what is the maximal benefit, the minimal cost, the best fit model, or the optimal way of performing a process? Analysis forms the basis of operational research, and helps us understand complex systems or organisations.
Probability, Statistics & Data Analysis (20 credits)
Probability theory describes our mathematical assumptions about the processes we are trying to understand. Statistics describes the quantitative methods used to analyse data on the basis of these assumptions. Data analysis uses powerful computational techniques to automate analysis so that it is reproducible and robust. This module will also touch on qualitative methods and the ethics of data.
Communication (20 credits)
We focus on both the communication of science and the science of communication. You will learn about cognitive pathways and the social and psychological aspects of communication. You will learn about storytelling for general audiences and how to keep listeners and readers engaged. You will produce talks, posters and group presentations and summarise your knowledge and research through storytelling.
Level 2 builds upon the foundations laid at Level 1 and develops your ability to model and simulate diverse phenomena in the world around you, both analytically and via computer simulations.
You will get the opportunity to integrate and put into practice these different topics, tools and techniques in your enterprise and research project, where interdisciplinary groups apply the knowledge they have gained to address a common question in research or entrepreneurship.
Modules may include:
- Modelling & Numerical Analysis (20 credits)
- Graphs, Networks & Systems (20 credits)
- Vector Analysis, Fluids & Electromagnetism (20 credits)
- Geometry & Groups (20 credits)
- Complex Analysis & Transforms (20 credits)
- Enterprise & Research Group Project (20 credits)
Level 3 brings you to an advanced level of mathematical sophistication and specialisation, with a portfolio of skills tailored to your personal interests.
A substantial individual research project allows you to accentuate your programme of studies and develop your interests in different directions. This dissertation is an extended piece of research and writing that shows your individuality, independence and creativity, and as such is very highly-rated by employers. Complementing this will be advanced modules in algebra, analysis and statistics, allowing you to pursue a unique route that prepares you for work in industry, teaching, or advanced studies.
Modules may include:
- Advanced Data Applications (20 credits)
- Number Theory, Information Theory & Cryptography (20 credits)
- Mathematical Physics & Applied Mathematics (20 credits)
- Advanced Topics in Algebra (20 credits)
- Patterns in Mathematics (20 credits)
- Operators, Quantum Mechanics & Manifolds (20 credits)
- Dissertation (40 credits)
Teaching & Assessment
Your skills in Mathematics will be developed through a range of teaching formats: lectures, workshops, problem-solving sessions and small-group tutorials. Self-directed study will also be important as you practice and perfect the mathematical tuition you receive in formal timetabled sessions. Problem-solving, data modelling and analysis will draw on examples from the real world so that the overall learning experiences bridges seamlessly between mathematical theory and its application.
Students studying for a degree in mathematics are expected to conduct around 20 hours of independent study a week. This helps to develop self-motivation, persistence, discipline and good time management skills. Self-study can take a number of forms including i) preparation and revision for assessments; ii) solving problem sets; iii) reading set texts and iv) working on case studies.
One feature of the York St John experience is that staff are accessible so no matter how challenging the material, staff advice, guidance and support will be available to help you succeed. This support extends from subject-specific areas to the development of writing and study skills, and covers leaning support and career planning.