Curious about studying Mathematics with Computer Science - BSc (Hons)at Brunel University of London? We've gathered all the key details—entry requirements, modules, fees, and more. Hear from real students by checking out reviews, or take the next step by booking an open day to explore it for yourself.
Bachelor of Science (with Honours) - BSc (Hons)
Main Site
Full Time
Sep 2027
3 Year
The world relies on computers and so do most businesses and organisations. Mathematics is the language of computer science so if you have the skills for both you’ll find a whole range of careers available to you in most sectors. On this single honours course you’ll develop your mathematical and statistical knowledge and apply these skills to solve problems in computing, business and other areas. Approximately two-thirds of the course is devoted to mathematical and statistical areas. The mathematics subjects on this course focus particularly on aspects of modern algebra that relate to computer science and also include a considerable amount of numerical analysis of mathematical problems. You have the opportunity to specialise in Level 3 so as an example, you may choose modern encryption methods as used in internet transactions. Follow the four-year Professional Placement degree programme and you‘ll benefit from our extensive experience in helping students to find well-paid work placements with blue-chip companies. Our sandwich students find that their mathematical, computer science and transferable skills are in demand in many sectors, both in the UK and abroad. Areas recently offering placements include: accountancy, aviation, banking, defence, finance, insurance, IT (software development, network management and design), management (public and private sector), marketing and telecommunications. This programme will meet the educational requirements of the Chartered Mathematician designation, awarded by the Institute of Mathematics and its Applications, when it is followed by subsequent training and experience in employment to obtain equivalent competences to those specified by the Quality Assurance Agency (QAA) for taught master's degrees.
Basic results, techniques and elementary functions of differential and integral calculus; rigorous definitions and proofs and solution techniques of first order differential equations.
Further develops skills in differential and integral calculus and associated applications, including solution techniques of ordinary differential equations.
Manipulating mathematical expressions and using formulae; summation notation, fundamental results, rigour in definitions and proofs and the language of formal mathematics.
Provides a basic level of programming competence.
Basic linear algebra and the algebra of complex numbers; eigenvalues and eigenvectors, similar matrices and diagonalization and applications of linear algebra.
Key notions of mathematical probability and techniques for calculating with probabilities and expectations; elementary statistical concepts and extracting meaning from data.
Opportunities to apply fundamental programming concepts as a solution to non-trivial problems.
Understanding of useful data abstractions and algorithms and the ability to choose and implement appropriate algorithms in software.
Ideas and methods of multivariable calculus: Taylor series, extrema, Lagrange multipliers and integration of functions of several variables.
Graph theory and its real-world applications; operational research optimisation modelling, linear programming and algorithms for numerical optimisation.
Knowledge and comprehension of valuable data structures and algorithms and skills in evaluating trade-offs in selecting suitable algorithms.
Continuous and multivariate probability; statistical concepts and applications, confidence intervals and hypothesis tests applied using software.
Skills for planning and obtaining employment and applying techniques, methods, algorithms and theories to an applied problem cognate to the degree.
Knowledge and skills for the design and implementation of software systems; software engineering from requirements to implementation.
Advanced modelling in linear and integer programming using industrial software; optimum allocation of financial resources under uncertainty and financial risk measures.
Key concepts, principles and fundamental methods of AI; analysing problem requirements, applying appropriate AI methods and evaluating effectiveness.
Extracting value and insight from large heterogeneous data sets; data mining and machine learning techniques such as clustering, regression, support vector machines and neural networks.
Independent learning and critical thinking; planning and executing a major piece of work and written communication of complex ideas.
Topics influencing software quality, software metrics, and state-of-the-art topics such as software patterns, service-oriented engineering and security.
Accommodation (on campus)- 690-1,094 per month
4.2Founded in 1966, but with origins from 1798, Brunel University of London has achieved much in its history. Always an inn...
