Mathematics for Data Science BSc (Hons)

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.

Year 1

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 1
This 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 1
The 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 Algebra
This 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.

Year 2

Compulsory

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.

Year 3

Compulsory

Data Science Project
The 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 Regression
The 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 Learning
The 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 Models
This 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 undefined undefined, starting in undefined.

This course has a placement option. Find out more about work placements available.

Please note that all modules are subject to change.

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

2026/27 entry

For Brunel Mathematics with an Integrated Foundation Year requirements, see the course pages.

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.

GCSEs

Five GCSEs at grade C or grade 4 and above are also required, to include Maths and English Language.

A-level

Standard Offer: ABB including Maths or Further Maths

Contextual Offer: BBB including B in Maths or Further Maths