Attendance | UCAS code/apply | Year of entry |
---|---|---|
3 years full time | G100 | 2017 2018 |
4 years full time including sandwich year | G102 | 2017 2018 |
4 years full time including foundation year | G108 | 2017 2018 |
6 years part time | Apply direct to the University | 2017 2018 |
This degree provides a broad mathematics programme that includes application and statistics modules. It covers the fundamental techniques of mathematics with appropriate computational and statistical support to give you the tools to tackle real-world problems.
This course is accredited by the Institute of Mathematics & its Applications (IMA).
Year 1 introduces a variety of topics, laying the foundations for further work. Studies include calculus, linear algebra, ordinary differential equations and an introduction to numerical methods. There is broad coverage of probability and statistics. Financial mathematics is introduced, providing essential skills and knowledge that can lead to lucrative careers.
In Year 2, the module Mathematical and Numerical Methods introduces a range of useful mathematical techniques. In the Mathematical Analysis and Argument module you will further explore calculus, and will learn to construct and communicate rigorously logical arguments. You will study sophisticated mathematical and statistical modelling, investigating real-world problems through the use of industry-standard software (such as SAS, Maple and Matlab).
Once Year 2 is successfully completed, you will have the opportunity to take a professional placement year to develop your skills in a real work setting.
Year 3 extends your study to partial differential equations and optimisation (areas of mathematics that are applicable to many real-world problems). You will undertake a major project (independent study) or studies in mathematical education (including a placement in a local school). These draw together the academic strands of the course and enhance skills for future employment. In addition, you may select specialist option modules from different areas of mathematics and statistics, such as fluid dynamics, medical statistics and operational research.
The flexibility of this course enables you to transfer to related courses at the end of Year 1, and you may choose among applied mathematical, statistical and computational modules, as your interests develop.
Please note that this is an indicative list of modules and is not intended as a definitive list. Those listed here may also be a mixture of core and optional modules.
This module provides the foundations for further study of (applicable) mathematics. The basic ideas of mathematics as a discipline are introduced. Topics from different areas of mathematics which may readily be applied to solve problems in the real world are considered with emphasis on study of the Calculus, one of the most powerful tools of modern mathematics and theoretical science. As a necessary preliminary to this work we first clarify our ideas of rational, real and complex numbers. The fundamental concepts of calculus, in particular, that of a limit, are introduced and the continuity and differentiability of functions on the real line are explored. The derivative concept is generalised for functions of several variables extending the breadth of its application greatly and the study of ordinary differential equations is commenced.
On successful completion of the module, you will be able to:
This module is a core part of most mathematics courses and builds upon A-level study in three strands: it aims to develop s personal skills and understanding of degree-level study; it introduces computer programming and software as a useful problem-solving tool in mathematics; and it develops mathematical techniques in a computing context that will be used in parallel and subsequent modules.
On successful completion of the module, you will be able to:
This module introduces basic probability and statistical theory, concepts and their applications to real life problem solving and learn about different types of data and how to present and summarise these. The module also covers statistical inference and the concepts of confidence intervals and hypothesis testing for the population mean and variance, for proportions, for comparing measures between two populations and for contingency tables and goodness of fit to a known distribution.
On successful completion of the module, you will be able to:
This module is designed to build on the work previously gained in order to deliver more advanced tools in calculus and numerical methods thus permitting the solution of a much wider set of problems associated with the real world. In turn, concepts developed in this module are used extensively at Level 6.
On successful completion of the module, you will be able to:
This module is designed to build on the work previously gained in order to deliver more advanced tools in calculus and numerical methods thus permitting the solution of a much wider set of problems associated with the real world. In turn, concepts developed in this module are used extensively at Level 6.
On successful completion of the module, you will be able to:
This module builds upon the foundations in mathematics and computing with the aim to systematically develop mathematical modelling skills and computer programming as well as systems analysis skills, whilst continuing to develop mathematical techniques in a computing context.
On successful completion of the module, you will be able to:
This module develops and builds on the concepts of probability and statistical modelling studied at the previous level. The module introduces some of the major discrete and continuous statistical distributions which underpin statistical methodology and the concepts of joint distributions. The module also deals with statistical modelling and how to take data analysis beyond basic techniques. The theory and practical application involved in investigating multivariate data using statistical modelling from initial investigation through to validation of a model is investigated. Example driven practice in using industry standard statistical software for the purpose of statistical modelling and how to communicate the results of their analyses effectively and coherently will be reviewed. This module provides a sound grounding in theoretical and practical statistical analysis and forms the basis for learning more advanced multivariate methodologies later in the program. It also covers much of the material required to satisfy the IFA CT3 criteria.
On successful completion of the module, you will be able to:
This module builds upon previous mathematics studied, by concentrating initially on methods of proof and clear, logical exposition of mathematical arguments. These are essential skills for mathematicians to possess in order to perform their academic tasks and to communicate their findings efficiently. Armed with this approach to mathematics, the module progresses to develop more rigorously, various techniques involving real variables to include more detailed justification of some calculus techniques which have previously been encountered. The remaining content builds on the complex number work studied by moving into the realm of complex valued functions.
On successful completion of the module, you will be able to:
This module consolidates and further develops the concepts previously acquired; consisting of two distinct but interrelated parts. The PDE part builds on analytical and numerical methods for solving ODEs whilst in the optimisation section the ideas of using calculus to find stationary points of functions (of one or two variables), introduced in earlier modules are generalised and extended to cases where the functions are constrained (by both equations and inequalities). An holistic approach covering both analytical and (approximate) numerical techniques is adopted throughout. This means that a wide range of PDEs covering many areas of application may be solved – and similarly a variety of calculus-based methods for finding optima is considered and their appropriateness for different situations discussed in the context of recent research in the area.
On successful completion of the module, you will be able to:
Please choose from the following:
This module offers the opportunity to demonstrate skills and understanding gained to date on the course through application to a project of their choice. Typically it involves drawing upon work from several different areas of the course thus reinforcing the coherence of the programme, highlighting connections (and often interdependence) between the different areas studied to be able to give an overview. It also represents an opportunity to further develop vital skills in areas of research, time and project management, and presentation as well as in technical areas.
On successful completion of the module, you will be able to:
This module gives an insight into the theoretical and practical aspects of mathematics education in schools, particularly for the 11–16 age range. It is intended to foster skills of independent learning, critical analysis, information retrieval and enhanced communication. The first half of the module covers important issues in mathematics education such as the role and content of the National Curriculum. Armed with theoretical knowledge the student is placed in a local secondary school or college for the second semester. The process is based on the nationally recognised model of the undergraduate ambassador scheme which is designed to give students interested in becoming mathematics teachers a chance to gain relevant experience in the field. In the placement they act initially as observers, but gradually they progress to become classroom assistants and, if they can demonstrate appropriate aptitude, ultimately deliver a session to a group or to the whole class, under the guidance of the mentoring teacher.
On successful completion of the module, you will be able to:
Choose from the following:
This module aims to introduce database systems and the study of artificial intelligence with applications in research-informed topics such as language modelling, speech recognition or data mining. It introduces both 'traditional' (logic-based) and 'modern' (eg neural networks and probabilistic 'machine learning' systems) approaches to artificial intelligence, and includes some case studies of modern practical applications. It deals with the ability to design and manipulate relational databases using entity-relationship modelling and the structured query language (SQL). The module also develops an understanding of the wider context in which relational databases exist.
On successful completion of the module, students will be able to:
This module consolidates previous experience using the equations of fluid motion as a basis. The module consists of two distinct but interrelated parts: the analytical part builds on understanding of partial differential equations and further develops the understanding of applications in the area of fluid dynamics, where the module introduces commonly occurring flows, both theoretically and through a series of practical examples. The numerical part takes the fluid equations and explores approaches to solving them numerically, using a software package and students' own models where appropriate.
On successful completion of the module, you will be able to:
This module introduces the applications of statistical methodologies in clinical, medical and health scenarios. The module considers how the health resources in populations are assessed, monitored and used to produce routinely published health statistics. How to determine and apply appropriate statistical methodologies for the analysis of epidemiological studies and how to interpret, present and contextualise the findings of such analyses will be considered. The processes involved in the implementation of clinical trials will be introduced to further statistical methods which are routinely used in the development of new therapies and medical interventions. An introduction to the methodology of survival analysis and to some advanced multivariate methodologies which are being increasingly used in the medical and health fields.
On successful completion of the module, you will be able to:
This module serves as an introduction to the mathematics and statistics of modern portfolio theory, the mathematical, stochastic and statistical models of risky assets and the theory of pricing contracts based on these assets. It is intended to cover the requirements of CT8 from the Institute and Faculty of Actuaries.
On successful completion of the module, you will be able to:
This module builds upon the foundations in computing previously studied and aims to further develop mathematical modelling, computer programming, problem-solving and systems analysis skills with applications in a mathematical context. Further topics in computing are presented and analysed as solutions to common problems in mathematical and computational modelling.
On successful completion of the module, you will be able to:
This module has two aspects: firstly, the application of time series modelling techniques in forecasting is introduced with background gained in earlier modules. This part includes both non-probabilistic algorithmic methods as well as the Box-Jenkins ARIMA probabilistic modelling techniques. The methods are applied to real and up-to-date time series data sets using MS-Excel and SAS software packages. Emphasis is placed on practicability of methods and their applications, although the theoretical foundation also plays a significant role in introducing the methodologies involved. You will have the opportunity to acquire, develop and consolidate both modelling and software skills through a series of exercises during practicals and tutorial sessions.
The second part of the course uses and builds upon the distribution theory with an introduction to the important properties of estimators, Neyman-Pearson's lemma and the generalised likelihood ratio test with numerical applications; followed by the Bayesian methodology and its relevance to the statistical decision making problems. Here emphasis is placed on introducing theoretical concepts but numerical techniques such as statistical simulation is also used in demonstrating applications. Bayesian and frequentist's approaches to decision-making problems are also compared with the advantages involved are identified and discussed.
On successful completion of the module, you will be able to:
The module introduces a variety of operational research techniques and the basic concepts and ideas of mathematical programming. The module goes on to explain how to apply operational techniques such as network models, inventory models, quality control and heuristics to real life problem solving issues. The module shows how industrial problems of optimization may be written in mathematical form. The module also introduces the simplex algorithm and its variants and demonstrates how such problems may be solved via these methods. Problems of a nonlinear nature are also discussed and solved. Other topics covered within the module include the methods of Lagrange multipliers, the Kuhn-tucker procedure and an introduction to game theory. The module provides an essential introduction to operational research and mathematical programming techniques and provides a depth of detail that sufficiently prepares for further study and research into advanced techniques within the applied mathematics and statistics fields.
On successful completion of the module, you will be able to:
You will have the opportunity to study a foreign language, free of charge, during your time at the University on a not-for-credit basis as part of the Kingston Language Scheme. Options currently include: Arabic, French, German, Italian, Japanese, Mandarin, Portuguese, Russian and Spanish.
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