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Mathematics BSc (Hons)

Why choose this course?

The course is an exciting applications-focused mathematics programme with the curriculum oriented towards potential career opportunities. There is a strong focus on setting the application of mathematics in context - providing you with relevant commercial and social awareness and appropriate professional skills for your future career development. 

The overarching ethos of the delivery is that you should be engaged in active learning wherever possible. A largely problem- centred learning approach is adopted, whereby you will begin with the problems of interest and learn the necessary theory and techniques required to solve them. To assist with this, extensive use is made of computational support. You will gain computing skills and experience of a variety of up to date professional, industry-standard software packages deployed on the University's modern computing facilities.   

Of key importance is a theme integrating mathematical and professional skills, culminating in you undertaking a substantial piece of independent study allowing you to design and create solution implementations or other appropriate artefacts. A distinctive feature of this theme is that you will work in groups together with students from other (IT-based) disciplines on real world case-studies, developing your own professional skills and awareness of your place in the wider professional world. Kingston University scored 100% for overall student satisfaction for Mathematics in the National Student Survey 2018.  

Attendance UCAS code/apply Year of entry
3 years full time G100 Clearing 2019
2020
4 years full time including sandwich year G102 Clearing 2019
2020
4 years full time including foundation year G108 Clearing 2019
2020
6 years part time Apply direct to the University Clearing 2019
2020
Location Penrhyn Road

Reasons to choose Kingston University

  • This course received 100 per cent overall student satisfaction (National Student Survey 2018).
  • You'll use applications that model the real world, practical data analysis methodologies and industry standard software (such as Maple and Matlab).
  • This course is accredited by the Institute of Mathematics and its Applications (IMA).

Mathematics at Kingston University

What you will study

Please note that this is an indicative list of modules and is not intended as a definitive list as these could change before your year of entry.

Year 1

Year 2

Optional sandwich year

Final year

Year 1 introduces a variety of topics useful for the application of mathematics. The foundations are laid for later work in themes developing calculus based techniques with applications modelling the real world and also practical data analysis methodologies. The power of computational methods is introduced, enabling the investigation of more realistic problems, through the use of industry-standard software (such as SAS, Maple and Matlab).

You will have the opportunity to work together with students from other disciplines on case - studies developing team working and professional skills and awareness of the wider professional world.

Core modules

Mathematical Methods and Modelling of Applications

30 credits

This is the first in a spine of 3 core modules progressing through Levels 4 to 6 of the Mathematics BSc which place considerable emphasis on the important topic of calculus and its application to real-world problems. Although the necessary fundamental aspects of calculus, such as that of a limit are introduced, and the continuity and differentiability of functions on the real line explored, the delivery is primarily from an applicable, modelling perspective.

Typically the class sessions are problem-centred in nature where students are first presented with a real-world or other authentic problem as motivation for the solution being sought. A significant proportion of the time in class is spent with students working in small groups where these problems are formulated mathematically, solved (possibly with the use of computing packages) and the results presented in various formats to enhance employability skills. Lecturer input for the modelling methodology (the modelling cycle) and necessary new theory is given at appropriate stages of these formative tasks. In the latter stages of the module the study of ordinary differential equations is commenced which provide the opportunity to model many additional real-world scenarios and also provides essential foundation knowledge for the higher level calculus modules to follow.

On successful completion of the module, students will be able to:

  • Solve problems related to the calculus and its foundations;
  • Apply the techniques of calculus in a range of real-world modelling scenarios;
  • Formulate and solve authentic mathematical models based on simple ordinary differential equations (ODEs);
  • Communicate mathematical ideas and arguments in a variety of forms.
Problem Solving and Computational Mathematics

30 credits

This module is taken by all first year undergraduate students undertaking a degree in Mathematics. The module combines computer programming with an introduction to specialist mathematics software in the context of which some fundamentals of linear algebra are explored. Previous experience of programming is not assumed. The module seeks to introduce a foundation for programming that can be built on in subsequent years and that accommodates specialist practice within Mathematics.

Teaching and learning is split between a variety of different units the first of which is in common with module CI4100. As befits a practical discipline like programming, a hands-on approach is used that facilitates self-paced and self-directed learning. This approach continues as students move on to applying algebraic and numerical software to solve mathematical problems that arise in applications. Students are encouraged to engage with, develop and experiment with programs and mathematical problems in a constructivist fashion inspired by bricolage. The intent is to build students' confidence as they progress through the module's topics, and provide a foundation that can be built on so that in later years they can go beyond simple solutions to problems and be ready to engage in fully-fledged mathematical modelling and problem solving.

On successful completion of the module, students will be able to:

  • Decompose a mathematical problem or programming task into a set of smaller sub-tasks
  • Write programs that demonstrate the appropriate use of variables, arrays of variables, expressions, subroutines, conditional and iterative control flow structures
  • Use debugging and problem analysis strategies to find errors and validate problem or model solutions using appropriate tools and techniques
  • Apply modern graphical, numerical and algebraic computing techniques to simple mathematical problems or structures
  • Use matrices and vectors to represent, analyse and solve simple problems from the real world
  • Construct and present rigorous logical arguments (e.g. combining theory and output from mathematical software)
Practical Data Analyst Skills

30 credits

We make the first steps into the analysis of data. We begin by considering what are data, how they are obtained and introduce consideration of aspects of data collection, including designing surveys to obtain the information desired. Then, we look at how to approach data analysis, defining questions and identifying the best techniques to achieve  solutions to the problems posed. Some probability concepts are introduced to support  the statistical inference methods used as the module progresses. The main objective of the module is to teach practical data analysis skills using a problem centred approach simulating the practice most commonly encountered in industry and other real life scenarios, thus improving students' employablity. We teach students to work together and ask questions of the data and to find the correct statistical analysis tools to obtain good information and make useful decisions.

The module is the basis for much of the work in Statistics and in part the Data Science stream of the Mathematics course. It is foundational for the Data Science degree.

On successful completion of the module, students will be able to:

  • characterise the data set in terms of purpose, source timescale and measurement.
  • describe and summarise the main features of a dataset by using appropriate tables,  diagrams and summary statistics
  • construct confidence intervals and conduct hypotheses tests for means and proportions in well-defined, appropriately sized samples and interpret the results;
  • enhance employability by using appropriate industry standard software for data manipulation, basic statistical analysis and presentation of data;
  • demonstrate awareness of the key principles and practices of survey design and implementation.
Professional Environments 1

30 credits

The goal of the Professional Environments module is to prepare students for professional practice firstly by ensuring they acquire suitable employability assets and secondly by equipping them with an understanding of the role of a professional in society and the role of professional bodies.

While the bulk of the taught programme focuses primarily on domain knowledge, the Professional Environments module focuses on developing key skills (as enumerated in the Programme Specification), personal qualities (eg commercial awareness, reliability and punctuality, understanding the centrality of customers and clients), and professional knowledge including the need to engage with continuing professional development. With such assets, students will generate a CV, an employment portfolio, and a professional online presence.

Being a professional also means understanding the key legal, ethical and societal issues pertinent to the domain, and understanding the need for continuing professional development (CPD) especially when technology develops at such a rapid pace. The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.

Reflecting the fact that team working is ubiquitous in the modern workplace, a significant proportion of the assessment work on the course is group-work based. There is considerable evidence that group work promotes a much deeper engagement with taught content. It also encourages the development of diverse learning communities. This module will therefore introduce students to best practice in group working covering how to approach group work, how to deal with different types of people, and methods of selecting and managing groups.

Year 2, extends the themes introduced in Year 1, refining the integration of mathematical and professional skills to model real-world problems and develop and present solutions. You will continue to build a portfolio of products, showcasing your growing knowledge and skills.

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.

Core modules

Applications of Calculus and Linear Systems

30 credits

This is the second in a strand of essentially calculus-based core modules for the BSc in Mathematics, and concepts developed here are used extensively to underpin the knowledge delivered at level 6, including in the capstone project. The module content is designed to build on the work undertaken at level 4 by further developing the students' knowledge and skills necessary to tackle a wide variety of interesting real-world problems. For example, the treatment of ordinary differential equations is extended so that linear systems of these can be considered, both analytically and numerically as befits the application, thus permitting the solution of a much wider range of problems associated with the real-world scenarios. These may be associated with medical applications, industrial processes, environmental hazards and disasters, to name just a few.

The module also considers the topic of multiple integration and vector calculus thus permitting consideration of authentic problems where changes occur in 3-dimensions, such as problems in computer game and animation development. In common with the preceding level 4 calculus module, MA5500 is rooted in the methodology of modelling real-life problems and its delivery is centred on active student participation in tackling interesting and engaging tasks.

On successful completion of the module, students will be able to:

  • Evaluate multiple integrals, in different co-ordinate systems.
  • Perform vector algebra and calculus, including evaluations of gradient, divergence and curl, and applications of (integral) theorems linking these quantities.
  • Solve a variety of ordinary differential equations (ODEs), analytically and numerically as applied to real-world problems through individual and collaborative group work.
  • Solve numerically systems of linear and nonlinear equations from real-life problems.
Mathematics of Finance and Investment

30 credits

This module introduces students to basic mathematical models for assessing investments and projects taking place over a period of time. The module goes on to explain how concepts of compound interest and discounting are used to value payments to be made in the future. Compound interest functions are introduced and formulae for regular level or varying payments made for specified periods (annuities certain) are derived. Practical applications are demonstrated by analysing elementary compound interest problems relating to investments such as bonds and ordinary shares. The module provides the basis for the final year modules Financial Portfolios and Derivatives, and Insurance Risk Mathematics. 

On successful completion of the module, students will be able to:

  • Distinguish between interest rates expressed in different time periods and derive the relationships between them;
  • Evaluate the present value and the accumulated value of a given cash flow series;
  • Define and derive compound interest functions including annuities certain;
  • Construct a schedule of loan repayments and evaluate the price of, or yield from a bond using the concept of an equation of value;
  • Apply discounted cash flow and equation of value techniques in investment project appraisal;
  • Demonstrate an understanding of the term structure of interest rates and evaluate spot rates, forward rates, duration, convexity and immunisation.
Modelling Real World Data with Statistics

30 credits

This module develops and builds on the concepts of probability and statistics introduced in the module Practical Data Analyst Skills . It is a core module for students taking Mathematics, and Data Science degrees.

Probability underpins aspects of statistics and we need a sound grounding in those topics that are directly applicable to many real world applications of the subject. We also need to be able to apply these probability distributions to real world data in order to obtain more information. In addition, we will be looking at how data from experiments and related studies are analysed  and how we can make useful sense of data. We also study some of the general linear models which give us understanding how various factors influence output data.

On successful completion of the module, students will be able to:

  • solve problems in applied probability, using discrete and continuous random variables and probability distributions
  • estimate the parameters of certain probability distributions using appropriate techniques of statistical inference
  • choose and apply the appropriate techniques of General Linear Models to obtain useful information from experimental and other related data sets and test the efficacy of these models
  • develop and apply statistical and other software to analyse data, and communicate the results of the analysis
Professional Environments 2

30 credits

Following a project-based pedagogic approach, students will undertake a major inter-disciplinary team-work project drawn from a list of authentic industrial problems. Achieving the goals of the project will require students, firstly, to apply the various development methodologies they have acquired on their course and, secondly, to develop professional skills in project management and team working.

While the bulk of the taught programme focuses primarily on the learning of domain knowledge, the goal of the Professional Environments 2 module is to prepare students for professional practice in their respective domains. They will develop the necessary project management and team-working skills, and, by working as a team on an authentic industrial project, they will gain a high degree of familiarity with the typical requirements capture, design, and development methodologies relevant to their discipline. With the focus on making real-world artefacts, the students will integrate their work into an employment focused portfolio.

Being a professional practitioner also mean critically assessing both goals and solutions from legal, ethical and societal perspectives as well as addressing security and safety concerns. Students are also encouraged to consider their continuing professional development needs and to engage with their professional bodies. To encourage career management skills and promote employability after graduation, students are expected to integrate the artefacts they produce and reflective practice narratives into their employability portfolios and personal development plans.

The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.

Core modules

Industrial Placement

60 credits

This module is an essential course programme component for students on the sandwich route of an honours degree "with professional placement".  It is a key element in providing an extended period in industry gaining real world employability skills. Students are supported both before and through their placement by the SEC Placement team. Students that successfully complete their placement year will graduate with a 4 year sandwich degree.

Final year completes the calculus based modelling journey with the study of partial differential equations and nonlinear systems (areas of mathematics that are applicable to many real-world problems). Everyone undertakes a major project (independent study) as the culmination of the theme integrating mathematical and professional skills in preparation for future employment. In addition,

you may select specialist option modules from different areas of mathematics and statistics, such as modelling financial investments, optimisation or modern applications in the analysis of 'Big Data'.

Core modules

Advanced Mathematical Methods and Models

30 credits

This module is core to the Mathematics BSc and it completes the theme of modules at lower levels which concentrate on calculus and differential equations. There are two main topics which advance the earlier material, namely partial differential equations (PDEs) and nonlinear systems. Whereas the ordinary differential equations (ODEs) you have studied at lower levels can handle only a single independent variable, PDEs accept several variables such as situations where a quantity being measured or predicted varies both with position and time. The enriched range of real-world scenarios that can be modelled mathematically then include traffic flows, heat conduction and vibrations in bodies, electrical properties in transmission lines, fluid dynamics, acoustics and option pricing in the banking industry.

The models of systems of ODEs studied at Level 5 make the assumption that all the equations are linear; in the other major topic of this module that restriction is removed and so again a larger range of models can be created which can represent such scenarios as interacting populations, chemical reactions, electrical circuits, mechanical and control systems. Of particular importance is the predicted stability of systems arising in the models produced. In both the cases of PDEs and nonlinear systems the analytical solutions may be impossible to find, but you will be introduced to tools to aid in their analysis including approximations afforded by industry standard computing packages. As with the earlier modules in this core strand, the exciting applications of these techniques to authentic scenarios makes the module ideal for a problem-centred, active learning environment where significant student participation is the norm.

On successful completion of the module, students will be able to:

  • solve appropriate partial differential equations analytically by separation of variables, the method of characteristics or integral transform techniques
  • formulate mathematical models of real-world problems using partial differential equations and critically evaluate their suitability by reference to the assumptions on which they are constructed
  • employ finite difference methods for solving PDEs and understand limitations of numerical methods
  • use appropriate techniques such as phase-plane analysis, aided when appropriate by mathematical software, to extract qualitative information from real-world systems
Mathematics Individual Project

30 credits

This module provides the opportunity for you to showcase your accumulated knowledge and practical skills gained throughout the programme, including development of an end product which showcases your skills portfolio and may be a useful discussion tool for interviews and graduate employment. The project will often draw from several different areas of your course, highlighting connections (and often interdependence) between the different skills acquired thus giving you experience of mathematics in practice. The project aims to further develop vital skills in areas of research, time and project management, and presentation as well as in technical areas. You can choose from a varied range of proposed project titles or work with academic staff to develop a title of your own within a particular field of interest and on completion of the project you will have gained expertise in your chosen topic area.

Optional modules

Financial Portfolios and Derivatives

30 credits

This module is on the Financial Modelling Guided Option Route within the Mathematics field. It serves as an introduction to financial markets, the mathematics of modern portfolio theory, the stochastic models of risky assets and the theory of pricing contracts based on these assets. The first part of this module introduces the main theories and techniques of modern pricing models and portfolio management. The second part of the module exhibits the basic features of financial derivatives (internationally traded financial contracts that depend on the values of underlying assets such as stocks or bonds). These instruments are defined, their payoffs and the markets in which they are traded are considered, and the importance of valuing these instruments in the absence of arbitrage is discussed. The topics covered in this module provide students with a thorough understanding of the characteristics and mechanics of financial markets and therefore enhance the employability of graduates wishing to pursue a career in trading, investment banking or risk management.

On successful completion of the module, students will be able to:

  • Employ appropriate measures to select, analyse and optimise the returns of portfolios and other investments,
  • Describe various models of asset returns and equilibrium and perform calculations using these models,
  • Discuss the various forms of the efficient market hypothesis and their limitations,
  • Formulate models of the securities markets using discrete and continuous-time stochastic processes and partial differential equations,
  • Use and extend these models to obtain the value of options and other contingent claims on assets.
Insurance Risk Mathematics

30 credits

The module provides a grounding in mathematical techniques which can be used for pricing and valuing life insurance and pension products, with examples drawn from current professional practice. Mathematical techniques used to model and value cashflows which depend on death, survival or other uncertain risks are explained. The module goes on to define simple assurance and annuity contracts and develop practical methods of evaluating cash flows arising from the contracts. This module provides students with an insight into the methods used by a professional in the insurance industry as well as many other sectors where risk modelling is needed.

On successful completion of the module, students will be able to:

  • Construct and analyse statistical distributions for risk modelling;
  • Formulate the model of lifetime or failure time as a random variable and evaluate survival probabilities and rates of mortality;
  • Compute estimates of mortality rates and hazard rates based on a range of modelling assumptions.
  • Define various assurance and annuity contracts and develop formulae for the expected values and variances of payments under the contracts;
  • Explain and derive premiums and reserves of assurance and annuity contracts and apply a profit test to the product;
  • Predict expected future cash flows for various assurance and annuity contracts.
Optimisation Techniques and Applications

30 credits

This module can be taken as an option module by students studying on the BSc Mathematics degree course. The module introduces students to a variety of Optimisation Techniques and their applications. The module consists of two distinct but interrelated parts. 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). A variety of calculus based methods for finding optima is considered and their appropriateness for different situations and applications is discussed. Whilst, in the operational research section, the basic concepts and ideas of Mathematical Programming are introduced. The section goes on to explain how to apply operational research techniques such as network models, location models, inventory models and heuristics to real life problem solving issues.

The module shows how  industrial problems of optimisation 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. Numerical software is employed to develop the students' practical skills to solve optimisation problems and to verify solutions from theoretical analysis. The module provides a depth of detail that sufficiently prepares students for further study and research into more advanced techniques while the exciting applications of these techniques to authentic scenarios makes the module ideal for a problem-centred, active learning environment where significant student participation is the norm.

On successful completion of the module, students will be able to:

  • find analytically the extrema of functions of two or more variables, with and without constraints,
  • apply appropriate numerical methods to solve unconstrained and constrained optimisation problems, and evaluate the relative merits and limitations of the methods;
  • apply the above methods in a range of application areas;
  • use several types of operational research methodology to formulate models, solve them, interpret and define the limitations of solutions found by the above methodology;
  • identify the basic principles of mathematical programming methods and display a deep understanding of several mathematical programming algorithms for linear programming;
  • develop models for the solution of real life problems by optimization techniques using software to find, verify and interpret solutions.
Practical Applications of Advanced Statistics

30 credits

This module is designed to introduce students to further developments of statistical modelling methodologies introduced at Level 5. The module will be taught in a very practical way using an example driven approach to present applications of the theory, and subsequently interpretation and communication of the outcomes. Students will also be introduced to the applications of advanced models in real life scenarios  including within the Business and Health fields where demand for such skills is consistently high. During the module students will gain practical experience of how to determine and apply appropriate statistical methodologies and how to interpret, present and contextualize the findings of such analyses to the standard expected in a professional setting. They will also learn about the processes involved in such applications such as the full cycle of clinical trial analysis and the practical implementation of forecasting methods in business. Throughout students will be instructed in appropriate statistical software for carrying out such analyses and in the effective communication of their results, hence enhancing employability potential.

On successful completion of the module, students will be able to:

  • demonstrate understanding of selected Generalised Linear Models and when it is appropriate to use them;
  • choose and apply a statistical methodology appropriate to a given data analysis problem;
  • identify and analyse data obtained from clinical studies, and interpret and report the results of such analyses;
  • select and apply appropriate forecasting techniques for data analysis and critically assess the validity of the modelling results for time series data from the computer output; and
  • design, implement and produce solutions using appropriate modern computational software for the statistical techniques learned.
Artificial Intelligence and Machine Learning

30 credits

This module is an elective (option) module for the BSc Mathematics programme. It builds upon the foundations of Data Analysis & Modelling and computing skills developed in earlier modules. This module aims to introduce the study of artificial intelligence with applications in research-informed topics such as language modelling, speech recognition or pattern recognition in "big data" applications.

It introduces both "traditional" (logic-based) and "modern" (eg neural networks, including "Deep Learning", decision tree-based and probabilistic) "machine learning" approaches to artificial intelligence, and includes some case studies of modern practical applications. These are important mathematical and statistical concepts that are essential attributes for employable data scientists, mathematicians and statisticians in the modern, data-driven world.

The information above reflects the currently intended course structure and module details. Updates may be made on an annual basis and revised details will be published through Programme Specifications ahead of each academic year. The regulations governing this course are available on our website. If we have insufficient numbers of students interested in an optional module, this may not be offered.

Computing and Mathematics foundation year

Our Computing and Mathematics Foundation Year specifically caters for those who lack the traditional entry qualifications to join the first year of a science degree.

Entry requirements

If you want to join us in 2019 through Clearing, please call us on 0800 0483 334 (or +44 020 8328 1149 if you are calling from outside the UK) and speak to our friendly and knowledgeable hotliners who will be able to provide information on available courses and will guide you through your options.

Please note the tariff information below is for 2020 entry only.

Typical offer

  • 112 UCAS points from a minimum of two A Levels or equivalent Level 3 qualifications.
  • A Levels to include Mathematics at a minimum of a grade C. General Studies not accepted

Alternatively, BTEC Extended Diploma with grades DMM if an A Level in Maths at grade C is held.

Candidates are normally required to hold five GCSE subjects grades A*-C including Mathematics and English Language (or comparable numeric score under the newly reformed GCSE grading).

Alternative routes

We will consider a range of alternative Level 3 qualifications such as an Access Course in Applied Science or Maths,  which has been passed with 112 UCAS points.

Applications from those that have undertaken a Computing and Maths foundation year will also be considered.

International

We welcome applications from International Applicants. View our standard entry requirements from your country.

All non-UK applicants must meet our English language requirements. For this course it is Academic IELTS of 6.0, with no element below 5.5.

Teaching and assessment

Teaching includes lectures, computer practicals and tutorials. Drop-in sessions for mathematics support and assistance with study skills are available.

The format of assessments is varied - for example, in addition to examinations, you will investigate case studies, individually and in groups, writing reports and giving oral presentations. Typically you will produce simulations, posters, videos, schedules/quotations for customers, write articles, etc. In this way, as  you progress through the course, you will 'learn by doing and making' and assemble a portfolio of tangible outputs which evidence, explicitly, the knowledge and skills you have gained and which may be used to demonstrate your capabilities to future employers.

Guided independent study

When not attending timetabled sessions you will be expected to continue learning independently through self-study. This typically will involve reading journal articles and books, working on individual and group projects, undertaking preparing coursework assignments and presentations, and preparing for exams. Your independent learning is supported by a range of excellent facilities including online resources, the library and CANVAS, the online virtual learning platform.

Academic support

Our academic support team here at Kingston University provides help in a range of areas.

Dedicated personal tutor

When you arrive, we'll introduce you to your personal tutor. This is the member of academic staff who will provide academic guidance, be a support throughout your time at Kingston and who will show you how to make the best use of all the help and resources that we offer at Kingston University.

Your workload

Type of teaching and learning

Year 1

Year 2

Year 3

Year 1
  • Scheduled teaching
  • Guided independent study
Year 2
  • Scheduled teaching
  • Guided independent study
Year 3
  • Scheduled teaching
  • Guided independent study

How you will be assessed

Type of assessment

Year 1

Year 2

Year 3

Year 1
  • Coursework
  • Practical: 7%
Year 2
  • Coursework
  • Practical: 8%
  • Exams
Year 3
  • Coursework
  • Exams

Feedback summary

We aim to provide feedback on assessments within 20 working days.

Your timetable

Your individualised timetable is normally available to students within 48 hours of enrolment. Whilst we make every effort to ensure timetables are as student-friendly as possible, scheduled teaching can take place on any day of the week between 9.00am and 6.00pm. For undergraduate students Wednesday afternoons are normally reserved for sports and cultural activities, but there may be occasions when this is not possible. Timetables for part-time students will depend on the modules selected.

Class sizes

To give you an indication of class sizes, this course normally enrols 40 students and lecture sizes are normally 40­­.  However this can vary by module and academic year.

Who teaches this course

The course is taught at the Faculty of Science, Engineering and Computing. Faculty staff have a wide range of experience across research and industry and continue to practice and research at the cutting edge of their discipline. This ensures that our courses are current and industry informed ensuring you get the most relevant and up to date education possible. 

Staff will use their experience and professional networks to hone your skills and shape you into the next generation of science, technology, engineering and mathematics (STEM) graduates.

Course fees and funding

2019/20 fees for this course

The tuition fee you pay depends on whether you are assessed as a 'Home' (UK or EU), 'Islands' or 'International' student. In 2019/20 the fees for this course are:

 Fee category  Amount
Home (UK and EU students) £9,250*
International Year 1 (2019/20): £14,200
Year 2 (2020/21): £14,600
Year 3 (2021/22): £15,000
Islands (Channel Islands and Isle of Man) To be confirmed by the Island Authorities

 * If your course involves a foundation year, the fee for that year for home and EU students will be £9,250 in 2019/20. The fees shown above apply for year 1 of the degree from 2018/19 onwards (fees may rise in line with inflation for future academic years). These fees are annual and may increase in line with inflation each year subject to the results of the Teaching Excellence Framework (TEF).

Eligible UK and EU students can apply to the Government for a tuition loan, which is paid direct to the University. This has a low interest-rate which is charged from the time the first part of the loan is paid to the University until you have repaid it.

Additional costs

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. Accommodation and living costs are not included in our fees. 

Where a course has additional expenses, we make every effort to highlight them. These may include optional field trips, materials (e.g. art, design, engineering), security checks such as DBS, uniforms, specialist clothing or professional memberships.

Text books

Our libraries are a valuable resource with an extensive collection of books and journals as well as first-class facilities and IT equipment. You may prefer to, or be required to, buy your own copy of key textbooks.

Computer equipment

There are open-access networked computers available across the University, plus laptops available to loan. You may find it useful to have your own PC, laptop or tablet which you can use around campus and in halls of residences.

Free Wi-Fi is available on each of the campuses.

Printing

In the majority of cases coursework can be submitted online. There may be instances when you will be required to submit work in a printed format. Printing and photocopying costs are not included in your tuition fees.

Travel

Travel costs are not included but we do have a free intersite bus service which links the campuses and halls of residence.

Note for EU students: UK withdrawal from the European Union

EU students starting a programme in the 2019/20 academic year will be charged the same fees as those who began in 2018/19 (subject to any annual increase in accordance with the applicable terms and conditions and the Kingston University fees schedule).

They will also be able to access the same financial support for the duration of their course as students who began in 2018/19, even if their degree concludes after the UK's exit from the EU.

No assurances have yet been made regarding 2020/21 and beyond. Updates will be published here as soon as they become available.

Facilities

As modern mathematics and statistics frequently involves computing, your studies will take place in laboratories equipped with fold-flat LCD screens, high-spec processors, the latest networking hardware and data-projection systems. Plus, you will have access to more than 2,000 networked computers across the University, and all our main campus areas have wireless network access.

Professional specialist software

In addition to standard office software and predominantly academic packages such as Autograph, Minitab and Stella, you will also use the latest professional mathematics and statistics software packages:

  • Maple - a symbolic manipulation and numerical package which is used by universities and industries across the world to solve complex mathematical problems in all areas, from financial modelling to automotive engineering.
  • Matlab - a numerical package which is used by some of the largest businesses in the world, for example, Boeing, Ford, Pfizer and Samsung.
  • SAS - statistics-based software used extensively across the world. Of the world's largest 100 companies, 92 employ this software (New York Times, 2009), from finance to pharmaceuticals.

Using these software packages throughout your course will increase your technical mathematics and statistics skills and prepare you for employment in a wide range of industries.

Subject-specific support

In addition to support from your course tutors, you can also access help at our mathematics and statistics drop-in sessions. These sessions are run by academic staff and give you the opportunity to discuss issues that arise during your studies.

Careers and progression

After you graduate

In the past our graduates have found employment in a wide range of areas including IT, pharmaceuticals, retail management, insurance, banking, accountancy, defence industry, the National Health Service, energy industry, transport, local and national government service as well as research, further study and teaching. These opportunities remain open but, with regard to specific areas of employment, some of the content of the programme may be particularly appropriate for the finance sector (currently the largest employment sector for mathematics graduates) or the data analysis field (the area that is expanding most rapidly at present) - ensuring that there should be strong demand for graduates from the programme. 

Recent graduates from our mathematics based courses have found employment with large organisations such as GlaxoSmithKline, Allianz Insurance, Axa Investments, Barclays, BUPA, Ernst & Young, Goldman Sachs, IBM, Office for National Statistics, Oracle, Statistics Canada and in teaching as well as with a host of smaller companies. Of course many graduates have pursued postgraduate study at institutions including LSE, UCL, Manchester, Southampton and Cambridge as well as at Kingston.  

Careers and recruitment advice

The Faculty of Science, Engineering and Computing has a specialist employability team. It provides friendly and high-quality careers and recruitment guidance, including advice and sessions on job-seeking skills such as CV preparation, application forms and interview techniques. Specific advice is also available for international students about the UK job market and employers' expectations and requirements.

The team runs employer events throughout the year, including job fairs, key speakers from industry and interviews on campus. These events give you the opportunity to hear from, and network with, employers in an informal setting.

Employability preparation at Kingston University

In addition to building expertise in your own discipline, our courses will also help you to develop key transferable skills that you'll need for professional life or further study once you graduate.

As well as a range of careers and employability activities at Kingston, we also offer you the chance to apply and develop your skills in live contexts as an integral part of your course. Opportunities include:

  • placements;
  • working or studying abroad;
  • volunteering;
  • peer mentoring roles; and
  • internship opportunities within and outside the University.

In your final year, you'll get the opportunity to complete a major 'capstone' project where you can apply the knowledge and skills you have acquired to a range of real issues in different contexts. This is a great way to learn and is a valuable bridge to employment or further research at masters level.

Courses available after you graduate

If you decide that you would like to go on to postgraduate study after your undergraduate course, we offer a 10 per cent discount on our postgraduate course tuition fees to our alumni.

Accreditation

This course is accredited for 2019 and 2020 entry by the Institute of Mathematics & its Applications (IMA) and therefore meets the educational requirements of the Chartered Mathematician designation 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 a taught masters degree.

What our students say

Find out why our students chose Kingston University:

Mathematics at Kingston University:

View our current students talking about why they chose a mathematics course at Kingston University:

What our graduates say

I graduated from Kingston University in 2014 with a first in Mathematical Sciences. I studied statistics, mathematical models and computing in my course, all of which has defiantly helped my success at getting my dream job.

I had a brilliant time studying the course. The course is interesting and has a variety of different aspects of mathematics to study so you will find your favourites. Along with this the lecturers made all the difference. They make the lectures as fun (as much as they can be) and are always so supportive (unlike many universities my friends went to).

My key bit of advice for anyone studying at Kingston would be to listen to and follow what the lecturers say; they are there to help and they really care about you.

Claire Gibson – Mathematical Sciences BSc

I began studying as a mature student when my youngest child was two. After achieving a maths A-level, I wanted to do a maths degree. Initially I studied mathematics with economics, but decided that economics wasn't for me. I considered other maths courses and selected mathematical sciences. Once I made this change, I enjoyed every aspect of the degree. I particularly liked learning assembly language and writing my own computer programs.

I did the course because I wanted to teach maths and soon after graduating I went on the Graduate Training Programme, which enabled me to become a qualified teacher by training on the job. Without the degree I would not have been able to do this. 

I now teach maths, statistics and computing at a mixed comprehensive school in greater London. Teaching is wonderful fun. I really enjoy working with young people and sharing their pleasure when they achieve the GCSE grade they need for their own further education. I also love being able to explain something to a pupil who doesn't understand.

My advice is to do your degree while you are young - you can take part in all the social activities that Kingston University has to offer and have loads of fun! Although, if you are older, the staff are very supportive and the sense of achievement is immense.

Jane Weller – Mathematical Sciences BSc

Links with business and industry

General business links

Kingston University lecturers are frequently involved in collaborations with industry and can illustrate their teaching with relevant up-to-date experience. 

This also ensures that the course includes the latest technological innovations and that it is continually refined to meet the latest employment market needs. For example, when internet business started, an e-commerce module was set up. Now it's established, fraud is an issue so a module in computer forensics has been introduced.

There are also long-established relationships between the University and locally based electronics, instrumentation and IT companies, many of which have employed Kingston graduates.

Employment opportunities

We have a wealth of industrial contacts provide placement, project and graduate employment opportunities. We also run an annual placement and careers fair, giving you the opportunity to meet some of the top employers of IT placement students and graduates. 

Our excellent links with employers also mean that employers with graduate positions often contact us direct.

Research

The Faculty of Science, Engineering and Computing has an active research community made up of highly motivated academic staff, bright and highly imaginative research staff and students, and excellent technical and administrative support. A large proportion of the faculty's staff is research-active, well-known and respected in their fields. This is good news for students because it helps keep the courses relevant and up to date and to make the University known to a wider pool of employers. It also gives students the opportunity to get involved in projects and case studies and to interact with staff working at the forefront of their subject.

Research activities are organised into a number of interest areas including:

  • digital imaging;
  • information systems;
  • numerical analysis; and
  • mobile information and network technologies.

Research groups often work with other universities and with outside organisations including recent collaborations with:

  • Motorola;
  • Empower Interactive;
  • Metropolitan Police; and
  • Toshiba.

Find out more about research in the Faculty of Science, Engineering and Computing.

Work placement year

How you can work in industry during your course

Why take a placement? Work placements: 

  • provide work experience that is relevant to your course and future career; 
  • improve your chances of graduating with a higher grade degree; 
  • enhance your CV; 
  • lead to a graduate job;  
  • enable you to earn a year's salary whilst studying (the vast majority of placements are paid); and 
  • help you to select your final-year project. 

"To be successful, tomorrow's leaders will need to be far more rounded individuals than ever before. They will collaborate in pursuit of shared goals. They will guide, challenge and support...They will have an appetite for change and a hunger for continuous improvement, and they will have an ethos of learning and development..." 
Jeremy Darroch, Former Chief Executive, Sky  

"Doing a placement year effectively gives you one foot in the door of a future job and to stand out from the crowd... as well as enhancing my CV... and future interviews. It's a great motivator to be successful in my studies as it only serves to open even more doors and gain more skills." 
Placement student at Jagex Games Studios Ltd

  • 81% placement students and 34% non-placement students got a first or 2.1 (Faculty of Computing, Information Systems and Mathematics, 2008). 
  • 100% of placement students during 2008 recommend doing a placement (Faculty of Computing, Information Systems and Mathematics, 2008). 
  • Many employers offer a graduate job to their successful placement students. 

There is a lot of support available for students looking to secure a placement (eg a jobs board with placement vacancies, help with writing CVs and mock interviews). Getting a placement and passing the placement year are ultimately the student's responsibility. 

For further information please contact the Placements Team by telephone 020 8417 2969 or email secplace@kingston.ac.uk

Examples of placements  

Placements can be with large multinational companies, international companies, local companies and small start ups; offering a diverse range of posts. Here are some examples of employers and roles: 

Construction-based placement employers 

Construction-based placement roles 

RG Group 
Multiplex 
Costain 
Willmott Dixon  
Fluor 

Assistant site manager 
Assistant trades package manager 
Assistant logistics manager 
Health and safety officer 
Construction engineer

Science-based placement employers 

Science-based placement roles 

Reckitt and Benckiser 
GSK 
Drug Control Centre 
Minton Treharne and Davies Ltd  
Various local and international hospitals 

Bioanalytical sciences 
Lab assistant 
Pharmacy assistant 
Sports coach 

Engineering-based placement employers 

Engineering-based placement roles 

Airbus 
BAM Nuttall 
Nissan 
Bosch 
Wozair

Analysis of aircraft structure 
Construction resources specialist 
Site engineer assistant

Computing and IS based placement employers 

Computing and IS based placement roles 

Disney 
Sony Interactive Entertainment Europe 
IBM 
McKinsey 
Intel

Database co-ordinator 
Software developer 
Website developer 
App developer

Mathematics-based placement employers 

Mathematics-based placement roles 

Lloyds Banking Group 
AXA 
Allianz 
PAU Education, Spain

Analyst 
Investment solutions 
Research analyst 
Accounts assistant

Key information set

The scrolling banner(s) below display some key factual data about this course (including different course combinations or delivery modes of this course where relevant).

Undergraduate study
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