BSc (Hons) Mathematics with Computer Science and Placement Year Bachelor's degree at Brunel University of London

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.

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.

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.

This module aims to provide a basic level of programming competence.

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.

Aims to introduce key notions of mathematical probability and develop techniques for calculating with probabilities and expectations: to lay the foundation of subsequent modules in probability and statistics. To obtain a solid grounding in some applications of probability and elementary statistical concepts, with applications. To develop skills in extracting meaning from data, presenting data graphically and summarising results in writing.

This module aims to provide opportunities for students to apply fundamental programming concepts as a solution to non-trivial problems.

This module aims to develop an understanding of a set of useful data abstractions and algorithms, to stimulate students' critical thinking and develop their ability to choose appropriate algorithms in solving practical problems and implementing them in software.

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.

Graphs serve as a background for many important problems in real world applications. This gives an understanding of this area of discrete mathematics, and develops a knowledge of graph theory applications. Also, to introduce operational research optimisation modelling and problem solving and, in particular, linear programming (LP) problems. To introduce algorithms for numerical optimisation and the modelling of random events.

This module aims to develop knowledge and comprehension of various valuable data structures and algorithms, to stimulate critical thinking and enhance skills in evaluating the trade-offs involved in selecting the most suitable algorithms for specific purposes.

Aims to further develop skills in continuous and multivariate probability. To impart an understanding of statistical concepts and applications. To develop skills in extracting meaning from data, using software, and presenting results. To develop the concepts of confidence intervals and hypothesis tests. To apply these concepts in a variety of situations and to interpret the results of these procedures.

Aims to develop skills required for planning and obtaining employment, whether it is an internship, a placement or a graduate job, in a field related to the student degree programme. To see the development of these skills as a continuing, managed, lifelong process. To apply techniques, methods, algorithms and/or theories to an applied problem cognate to your degree studies.

The module aims to develop the knowledge and skills necessary for the design and implementation of software systems using recognised methodology, tools and technologies. The module provides an introduction to software engineering and follows a development process from requirements and design through to implementation, creating a number of software artefacts.

To introduce students to advanced modelling techniques in linear and integer programming (LP/IP) and their investigation using an industrial software package. To introduce students to the concepts of optimum allocation of financial resources under uncertainty. To familiarise the students with the basic issues of financial planning and with the models that provide mathematical descriptions of these investment problems. To introduce financial risk measures and illustrate how they can be incorporat

The aim of this module is to introduce the key concepts, principles and fundamental methods of artificial intelligence, and to develop your skill in analysing of problem requirements, applying appropriate artificial intelligence methods to defined problems, and evaluating the effectiveness of the adopted approach.

The aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques, such as clustering, regression, support vector machines, boosting, decision trees and neural networks.

This module aims to stimulate independent learning and critical thinking by the student, both as a means for studying their chosen topic and for approaching other real-life problems, to enable the student to plan and execute a major piece of work with limited input from a more experienced worker, and to give the student experience in the written communication of complex ideas and concepts and the presentation of a substantial piece of work.

This optional module aims to provide a grounding in topics that directly influence the quality of software, an understanding of key artefacts that inform software quality from a process and product perspective (in particular, the use of and application of software metrics), and to articulate the state-of-the-art in terms of topics such as software patterns, service-oriented engineering and security issues.

This module aims to introduce the students to the main aspects of modern mathematical finance, in particular to the Black-Scholes-Merton theory of option pricing, the ideas of arbitrage pricing, replication and dynamic hedging. It aims to illustrate the necessary ideas from continuous time stochastic process theory by using discrete time models to, in particular, outline the concept of a lognormal random walk.

This module aims to introduce students to the mathematics of differential equations; techniques of analysing such equations, and methods of solving them, exactly or approximately.

The aim of this module is to develop practical skills in applying selected machine learning techniques to real-world data analytics problems. Focus is placed on artificial neural networks, deep learning, support vector machines and hidden markov 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