Overview
Ranked no.4 in London for student satisfaction in mathematics by The Complete University Guide 2024.
Data analytics (Big Data) is a major phenomenon in the 21st century, there is an increasing demand for data analysts trained in this area who can collate, interpret and draw value from complex data sets.
This programme brings together a range of techniques that modern data analyst needs. You will study blocks in mathematics, statistics, data analysis and computing, and tackle a variety of interesting and engaging problems from business and industry. A good grounding in all these subjects is essential for creating and using algorithms and systems that identify patterns and extract value from masses of data.
The course will also develop key graduate skills such as problem-solving and communication, with a third of the credits at each level based on project-oriented work where students will develop their knowledge, professionalism and creativity in a supportive environment.
As an example, in your second year, you will be introduced to neural networks and deep learning. This important topic is at heart a powerful blend of linear algebra, nonlinear activation functions, vector calculus chain rule for gradients, and steepest descent optimisation with sampling. These fundamental building blocks will be brought together in theory and in software so that you will be able to build your own deep learning neural net, and be able to explain the function of every part of the algorithm. This last aspect of being able to explain the software’s function is key to the role of a mathematician as an understander as well as a user of methods, as opposed to just a consumer of software. The emphasis throughout will be on the practical rigour associated with getting deep learning to work.
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 and transferable skills are in demand in many sectors.
You can explore our campus and facilities for yourself by taking our virtual tour.
Course content
At Brunel, we aim to make your transition into the university style of learning as easy as possible. So in the first few weeks of first year you’ll start your learning in small classes, a variety of teaching styles will be introduced during this time. By second year you’ll be learning through lectures, seminars and in computer labs.
In your final year you’ll be able to select some optional modules, allowing you to pursue your interests. In this year you will also complete a Data Science Project, supported by an academic staff member, providing you with the opportunity to demonstrate the professional skills you’ve acquired throughout your studies.
Compulsory
- Fundamentals of Mathematics
Aims to manipulate mathematical expressions accurately, as well as recall and use mathematical formulae in areas of interest for Year 1. To develop skills in handling summation notation. To introduce students to fundamental results in mathematics. To develop an understanding of the need for rigour in definitions and proofs. To introduce the language of formal mathematics, in particular sets and functions.
- Calculus 1This module aims to familiarise students with the basic results, techniques and elementary functions of differential and integral calculus, to introduce students to rigorous definitions, arguments and proofs through many simple examples, to develop students’ manipulative skills in performing operations in differential calculus through work on many simple examples, and to illustrate the solution techniques of first order differential equations.
- Calculus 2
Aims to further develop skills in differential and integral calculus and associated applications. To further develop students’ manipulative skills in performing operations in differential calculus through work on examples, including solution techniques of ordinary differential equations.
- Elements of Applied Mathematics 1The first of a sequence of blocks aimed at developing modelling skills. The main aim of this module is for students to develop a facility for mathematical modelling by examining a problem in its original form, extracting the principal features, formulating and solving appropriate mathematical models, and interpreting the results in terms of the original problems.
- Elements of Applied Mathematics 2
Second of a pair of modules developing modelling skills. Furthering a facility for mathematical modelling by examining a problem in its original form, extracting the principal features, formulating and solving appropriate mathematical models and interpreting the results in terms of the original problems.
- Linear AlgebraThis module aims to enable students to understand and become proficient in basic linear algebra and the algebra of complex numbers, to determine the eigenvalues and eigenvectors of matrices and understand their role in the theory of similar matrices and diagonalization, and to see and practice applications of linear algebra.
Compulsory
- Scientific Computing
Aims to introduce the need for approximation in mathematics and/or statistics in the context of important pure and/or applied problems that cannot be solved exactly ‘by hand’. To introduce Computer Algebra as a tool for symbolic ‘exact’ computation as well as aid for approximation of otherwise intractable problems. To introduce the concept of simulation and its use in modelling.
- Applied Statistics
Aims to introduce and consolidate the notions of single and multivariable probability. Introduce statistical tools and explain their use in extracting and/or inferring meaning from data. To introduce sampling and inference, along with confidence intervals and hypothesis tests.
- Linear and Abstract Algebra
Aims to enlarge the set of technical tools of linear algebra and develop its applications to different problems, including construction and analysis of linear models. To introduce basic algebraic structures, concentrating on group theory. To exemplify their power, relevance and importance in real life applications.
- Calculus 3
Aims to develop ideas and methods of multivariable calculus, including Taylor series, extrema, the use of Lagrange multipliers, and the integration of functions of several variables. To understand the extension from single variable to several variables of basic concepts such as continuity and differentiability.
Compulsory
- Data Science ProjectThe main aim of this module is to stimulate independent learning and critical thinking to enable the student to plan and execute a major piece of work on an advanced topic in data science.
- Experimental Design and RegressionThe aim of this module is to provide an understanding of the principles of the statistical design of experiments through the study of particular design, of sampling theory for finite populations, and the concepts of survey design.
- Statistical Data Science and Machine LearningThe main aim of this module is to equip students with the knowledge and understanding of the challenges that arise when working with data science and the solutions that are needed to overcome those challenges.
- Stochastic ModelsThis module aims to introduce students to the concept of a stochastic process, so that they may develop an understanding of the theory underlying some of the standard models and acquire knowledge of methods of applying these models to solve problems. Students will further develop their general ability to think abstractly, to generalise, to formulate and structure stochastic problems, and to apply their knowledge of analytical and numerical mathematical techniques to solving a variety of problems in stochastic modelling.
- Deep Learning
Within this module, an in-depth introduction will be provided to the area of learning using deep neural networks. A wide variety of the architectures of deep neural networks and their learning methods will be covered, including convolutional networks, recurrent networks, generative models and deep reinforcement learning etc. The main focus of the module is to develop students’ skill in analysing of problem requirements, applying appropriate deep learning methods to real-world problems, and evaluating the effectiveness of the adopted approach.
- Decision Making in the Face of Risk
- Practical Machine Learning
This course can be studied 3 years full-time or 4 years full-time with placement, starting in September.
This course has a placement option. Find out more about work placements available.
Please note that all modules are subject to change.
Read more about the structure of undergraduate degrees at Brunel.
Careers and your future
Career prospects for mathematicians are excellent. This course emphasises the relevant mathematical and statistical underpinning while providing the essential practical training needed to function in the workplace. Graduates from this course will possess key skills that are highly sought after by any organisations that use data analytics to make data-driven business decisions, e.g., finance, health care, logistical, manufacturing and pharmaceutical sectors, government agencies, big data processing companies, and sport.
Our combination of work experience and up-to-date teaching means that you will be well-equipped to follow the career you want after graduation.
UK entry requirements
2024/25 entry
- GCE A-level ABB-ABC, including grade A in Mathematics or Further Mathematics.
- BTEC Level 3 Extended Diploma DDM, plus A-level Mathematics or Further Mathematics at Grade A.
- BTEC Level 3 Diploma DM in any subject with an A-level Mathematics or Further Mathematics at grade A.
- BTEC Level 3 Subsidiary Diploma D in any subject with A-levels grade AC including grade A in Mathematics or Further Mathematics.
- International Baccalaureate Diploma 30 points, including 6 in Higher Level Mathematics. GCSE English equivalent SL 5 or HL 4
- Obtain a minimum of 120 UCAS tariff points in the Access to HE Diploma with 45 credits at Level 3 and A-level in Mathematics or Further Mathematics grade A.
- T levels : Merit overall and grade A in A level Maths
For Brunel Mathematics with an Integrated Foundation Year requirements, see the course pages.
Five GCSEs at grade C or grade 4 and above are also required, to include Maths and English Language.
Brunel University London is committed to raising the aspirations of our applicants and students. We will fully review your UCAS application and, where we’re able to offer a place, this will be personalised to you based on your application and education journey.
Please check our Admissions pages for more information on other factors we use to assess applicants as well as our full GCSE requirements and accepted equivalencies in place of GCSEs.
EU and International entry requirements
If you require a Tier 4 visa to study in the UK, you must prove knowledge of the English language so that we can issue you a Certificate of Acceptance for Study (CAS). To do this, you will need an IELTS for UKVI or Trinity SELT test pass gained from a test centre approved by UK Visas and Immigration (UKVI) and on the Secure English Language Testing (SELT) list. This must have been taken and passed within two years from the date the CAS is made.
English language requirements
- IELTS: 6 (min 5.5 in all areas)
- Pearson: 59 (59 in all sub scores)
- BrunELT: 58% (min 55% in all areas)
- TOEFL: 77 (min R18, L17, S20, W17)
You can find out more about the qualifications we accept on our English Language Requirements page.
Should you wish to take a pre-sessional English course to improve your English prior to starting your degree course, you must sit the test at an approved SELT provider for the same reason. We offer our own BrunELT English test and have pre-sessional English language courses for students who do not meet requirements or who wish to improve their English. You can find out more information on English courses and test options through our Brunel Language Centre.
Please check our Admissions pages for more information on other factors we use to assess applicants. This information is for guidance only and each application is assessed on a case-by-case basis. Entry requirements are subject to review, and may change.
Fees and funding
2024/25 entry
UK
£9,250 full-time
£1,385 placement year
International
£21,260 full-time
£1,385 placement year
Fees quoted are per year and may be subject to an annual increase. Home undergraduate student fees are regulated and are currently capped at £9,250 per year; any changes will be subject to changes in government policy. International fees will increase annually, by no more than 5% or RPI (Retail Price Index), whichever is the greater.
More information on any additional course-related costs.
See our fees and funding page for full details of undergraduate scholarships available to Brunel applicants.
Please refer to the scholarships pages to view discounts available to eligible EU undergraduate applicants.
Scholarships and bursaries
Teaching and learning
Lectures will primarily be delivered in-person on-campus, though some may be delivered online either as pre-recorded or live sessions. The expectation is that you will attend all timetabled on-campus lectures, and that online lectures will be viewed by you in advance of related on-campus activities.
Tutorials & discussion-based sessions will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. You will attend all timetabled on-campus or online tutorials.
Computing Labs will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. You will attend all timetabled on-campus or online computing labs and be provided with access to the specialised software required.
Support/resources: Learning materials for every module will be made available online, through the University’s Virtual Learning Environment.
Assessments will be varied, and may include: CAA (computer aided assessment) tests, written coursework assessments (including software tasks), presentations (in-person or video presentations) and written examinations. You will be expected to attend assessments in-person on campus.
Access to a laptop or desktop PC is required for joining online activities, completing coursework and digital exams, and a minimum specification can be found here.
We have computers available across campus for your use and laptop loan schemes to support you through your studies. You can find out more here.
Mathematics at Brunel has an active and dynamic research culture and many of our lecturers are widely published and highly recognised in their fields. Their work is frequently supported by external grants and contracts with leading industry and government establishments. Lecturers are consequently at the frontiers of the subject and in active contact with modern users of mathematics. This means that you can be assured that our academics are teaching you a truly up-to-date degree and you’ll benefit from a wide range of expertise across the different areas of mathematics.
Your academics are always here to help and offer support. There are maths and numeracy workshops run throughout the year where you can seek support in linear algebra, calculus, LaTeX, MATLAB and more. You’ll also benefit from the extra support offered to you at our Maths Café. Here you can bring along any maths-related questions and receive one-to-one help in an informal setting.
Our teaching methods help you to bridge the school-university gap. Our small classes at the beginning of first year mean that you’ll get to know your lecturers and be able to discuss any problems with them. At Brunel, we realise that you might not remember everything from your previous studies and we offer plenty of opportunities to reinforce essential skills, we even offer extra help sessions throughout the academic year.
Should you need any non-academic support during your time at Brunel, the Student Support and Welfare Team are here to help.
Assessment and feedback
The Mathematics for Data Science BSc programme uses elements of formative and summative assessment. Although both forms of assessment will be graded, only the summative assessment will count for progression for your final degree.
Our academics use formative assessment as a fundamental component in the learning process, including; class tests (both in paper and electronic format), electronic quizzes, and short written exercises. Summative assessments throughout this course consist of coursework and examinations.
We base your final degree class on your performance in second and final year. Final year carries twice the weight of second year.
Read our guide on how to avoid plagiarism in your assessments at Brunel.