UCAS Code
NG21
Entry Year
2017
A Level Requirements
AAB
see all requirements
see all requirements
Duration
Full time 3 Year(s)
Studying Lancaster’s combined Management Mathematics degree gives you the benefit of top-quality teaching in our Department of Management Science and the Department of Mathematics and Statistics, where staff carry out world-leading research and many are actively involved with business and government organisations.
Your degree explores how Mathematics can be used to inform decision-making processes in business, industry and government. You will use mathematical and computer-based models alongside visual or verbal representations to analyse the development and application of solutions to real managerial problems.
You can choose from an extensive range of options, starting your degree with introductory courses covering Probability, Statistics, Management Science and Operational Research. In the second and third years you will develop your skills by taking modules from a wide range of core and optional modules including Stochastic Processes, Statistical Models, Bayesian Inference, Project Management and Consultancy Skills, Optimisation and Business Modelling and Simulation.
A four-year work-experience variant of this degree is also available: please indicate your proposed intention to take the work-experience variant in Section 3 of your UCAS application.
Grade Requirements
A Level AAB including A in Mathematics or Statistics
International Baccalaureate 35 points overall with 16 points from the best 3 Higher Level subjects including Grade 6 in Higher Level Mathematics or Statistics
BTEC Distinction, Distinction, Distinction
Access to HE Diploma in a relevant subject including Distinctions in the majority of units
Other Qualifications We welcome applications from students with other internationally recognised qualifications. For more information please visit the international qualifications webpage or contact the Undergraduate Admissions Office directly.
Essential Subjects
Mathematics or Statistics
GCSE Mathematics (B); English Language (C)
IELTS 6.5 (with at least 5.5 in each component)
Further Information
General Studies Offers normally include General Studies if it is taken as a fourth A level
Combination of Qualifications Applications from students with a combination of qualifications are welcomed, for further advice please contact the Undergraduate Admissions Office directly.
Taking a gap year Applications for deferred entry welcomed
Contact Undergraduate Admissions Office + 44 1524 592028 or via ugadmissions@lancaster.ac.uk
Many of Lancaster's degree programmes are flexible, offering students the opportunity to cover a wide selection of subject areas to complement their main specialism. You will be able to study a range of modules, some examples of which are listed below.
Core
This module provides the student with an understanding of functions, limits, and series, and knowledge of the basic techniques of differentiation and integration. We introduce examples of functions and their graphs, and techniques for building new functions from old. We then consider the notion of a limit and introduce the main tools of calculus and Taylor Series. Students will also learn how to add, multiply and divide polynomials, and be introduced to rational functions and their partial fractions.
The exponential function is defined by means of a power series which is subsequently extended to the complex exponential function of an imaginary variable, so that students understand the connection between analysis, trigonometry and geometry. The trigonometric and hyperbolic functions are introduced in parallel with analogous power series so that students understand the role of functional identities. Such functional identities are later used to simplify integrals and to parametrise geometrical curves.
This module extends the theory of calculus from functions of a single real variable to functions of two real variables. Students will learn more about the notions of differentiation and integration and how they extend from functions defined on a line to functions defined on the plane. We see how partial derivatives help us to understand surfaces, while repeated integrals enable us to calculate volumes. Students will also investigate complex polynomials and use De Moivre’s theorem to calculate complex roots.
In mathematical models, it is common to use functions of several variables. For example, the speed of an airliner can depend upon the air pressure, temperature and wind direction. To study functions of several variables, we introduce rates of change with respect to several quantities. We learn how to find maxima and minima. Applications include the method of least squares.
Management science is used in all major organisations in industry, commerce, finance and government. Its application might involve well-defined problems, such as reducing the cost of a complex goods distribution network, or more nebulous problems, such as improving patient care in hospital. Techniques based on mathematics and statistics can be extremely powerful in helping to solve these organisational problems.
Five such techniques will be introduced:
The module emphasises not only how to apply techniques, but also when (and when not) to apply them. There is a stress on practical examples of using the techniques.
You will work on two challenging case studies based on real problems. These provide the opportunity to apply concepts and techniques of problem solving, making recommendations and reporting results. To take this module you must also be taking one of MSCI 101, 100 or MNGT130.
To enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems, the module begins with a brief overview of statistics in science and society and then moves on to the selection of appropriate probability models to describe systematic and random variations of discrete and continuous real data sets. Students will learn to implement statistical techniques and to draw clear and informative conclusions.
The module will be supported by the statistical software package ‘R’, which forms the basis of weekly lab sessions. Students will develop a strategic understanding of statistics and the use of associated software, and this underpins the skills needed for all subsequent statistical modules of the degree.
Introducing the theory of matrices together with some basic applications, students will learn essential techniques such as arithmetic rules, row operations and computation of determinants by expansion about a row or a column.
The second part of the module covers a notable range of applications of matrices, such as solving systems of simultaneous linear equations, linear transformations, characteristic equation and eigenvectors and eigenvalues.
Probability theory is the study of chance phenomena, the concepts of which are fundamental to the study of statistics. This module will introduce students to some simple combinatorics, set theory and the axioms of probability.
Students will become aware of the different probability models used to characterise the outcomes of experiments that involve a chance or random component. The module covers ideas associated with the axioms of probability, conditional probability, independence, discrete random variables and their distributions, expectation and probability models.
This module is designed to give you an introduction to probability and statistics and to make you familiar with some useful computer tools.
The statistical topics covered are sampling, introductory data analysis and presentation, index numbers, probability, the use of some important probability distributions, confidence intervals and hypothesis tests for means and proportions, regression analysis with two variables. The computing side of the module introduces the use of word processing, spreadsheet software for statistical calculations, PowerPoint for presentations and management reports.
Available to students taking degrees in the departments of Accounting and Finance, Economics, Management Science and Mathematics, and to incoming exchange students with college-level Mathematics.
Core
The overall objective of this module is to introduce you to some of the key communication and technical skills needed in project work and to give you the opportunity to practise those skills in a progressive way.
Via a group simulated consultancy study, it covers some of the key communication and technical skills needed in project work, such as teamwork, interviewing for information, oral presentations and writing a management report.
This module will give you the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.
You’ll consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of your study will also involve looking at the concepts of length and angle with regard to vector spaces.
Probability provides the theoretical basis for statistics and is of interest in its own right.
You’ll revisit basic concepts from the first year probability module, and extend these to encompass continuous random variables, investigating several important continuous probability distributions.
You’ll then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.
Designed as a complete introduction to the theory and practice of managing business projects, this module introduces project management methods in a way which links to the life cycle of a typical project – from the early project identification and definition stages, through project execution and control, to issues of implementation and change.
The coverage of the early stages of the project cycle uses methods emerging from the systems movement and stresses the strategic relevance of project management.
The operational management of the project is covered by introducing techniques for planning, scheduling and controlling projects. Attention is also given to the people management aspects of this process, especially to leadership, team-working, motivation and direction.
Statistics is the science of understanding patterns of population behaviour from data.
In this module we approach this problem by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.
You’ll focus on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and also considering linear regression techniques within the statistical modelling framework.
Optional
This module gives you an introduction to statistical techniques and their applications in the context of business and management problems. In addition, it is designed to develop your ability to make effective use of computer software for data analysis.
The following topics are covered:
At the heart of many real management problems are data that needs to be described, analysed and interpreted. Statistical methods are important across the range of Management School subject areas (e.g. accounting and finance, marketing, economics, operations management and operational research). This module develops your ability to describe, analyse and interpret data soundly, making effective use of computer software.
Developing these skills will also help you demonstrate to prospective employers that you have practical skills that can immediately be put to good use to solve problems for organisations either in the public or private sector.
The lecture materials, and the problems you are asked to solve in workshops, reflect the problems that organisations have to solve in practical situations where data analysis skills are required.
Core
This module covers the skills needed to improve business process by modelling and simulation.
Computer simulation methods are among the most commonly used approaches within operational research and management science. This module teaches you the skills required to apply simulation successfully to help improve the running of a business, and it shows how companies can find good solutions by predicting the effects of changes before implementing them.
Modern simulation packages are a valuable aid in building a simulation model, and this module uses the Witness simulation package, which is widely used commercially. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each task required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.
Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.
This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.
You’ll also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?
The approach taken in this course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.
This module describes a variety of optimisation algorithms and how business problems can be modelled using these techniques.
Optimisation is one of the primary techniques associated with management science/operational research. Linear programming models are used routinely in many industries, including petroleum refining and the food industry. Integer linear programming models are increasingly being used in practice for complex scheduling problems such as those that arise in the airline industry where such models have saved large amounts of money. Skills in formulating and solving applied optimisation problems are valuable for anybody interested in a career in operational research or business modelling and consultancy.
This module is designed to enable you to apply optimisation techniques to business problems.
Four main topics are covered:
Linear programming
Specially-structured linear programs
Integer and mixed-integer programming
Heuristics for large-scale problems
Optional
Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.
Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.
This module aims to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, study design, causality and confounding.
You’ll look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems you’re investigating as well as the mathematical and statistical concepts underpinning inference.
This module explores the concept of generalized linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal). You’ll also become familiar with the programme R, which you’ll have the opportunity to use in weekly workshops.
This module covers important examples of stochastic processes, and how these processes can be analysed.
As an introduction to stochastic processes you’ll look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).
You’ll then focus on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). You’ll discover how to analyse Markov processes, and how they are used to model queues and populations.
Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module you’ll become familiar with topics from classical statistics as well as some from emerging areas.
You’ll explore time series data through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. You’ll also study time series and volatility modelling, where we’ll discuss the techniques for the analysis of such data with emphasis on financial application.
Another area you’ll focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis. Lastly you’ll spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.
Lancaster University offers a range of programmes, some of which follow a structured study programme, and others which offer the chance for you to devise a more flexible programme. We divide academic study into two sections - Part 1 (Year 1) and Part 2 (Year 2, 3 and sometimes 4). For most programmes Part 1 requires you to study 120 credits spread over at least three modules which, depending upon your programme, will be drawn from one, two or three different academic subjects. A higher degree of specialisation then develops in subsequent years. For more information about our teaching methods at Lancaster visit our Teaching and Learning section.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
The logical and analytical skills you’ll develop by studying Management Science and Mathematics are appreciated by employers in a wide variety of occupations and can be transferred easily to a range of situations.
Lancaster graduates find our Management Mathematics degree is an ideal foundation for careers in the private sector, such as consultancy firms or manufacturing companies, as well as in the service and public sectors, for example, the NHS.
Some of our former alumni are now based in a management science group, while others are part of multi-functional teams. Many are now in senior positions.
We set our fees on an annual basis and the 2017/18 entry fees have not yet been set.
As a guide, our fees in 2016 were:
UK/EU | Overseas |
---|---|
£9,000 | £16,860 |
Lancaster University's priority is to support every student to make the most of their life and education and we have committed £3.7m in scholarships and bursaries. 400 students each year will be entitled to bursaries or scholarships to help them with the cost of fees and/or living expenses. Our financial support depends on your circumstances and how well you do in your A levels (or equivalent academic qualifications) before starting study with us.
Scholarships recognising academic talent:
Bursaries for life, living and learning
Any financial support that you receive from Lancaster University will be in addition to government support that might be available to you (eg fee loans) and will not affect your entitlement to these.
For full details of the University's financial support packages including eligibility criteria, please visit our fees and funding page
Students also need to consider further costs which may include books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation it may be necessary to take out subscriptions to professional bodies and to buy business attire for job interviews.
Studying Lancaster’s combined Management Mathematics degree gives you the benefit of top-quality teaching in our Department of Management Science and the Department of Mathematics and Statistics, where staff carry out world-leading research and many are actively involved with business and government organisations.
Your degree explores how Mathematics can be used to inform decision-making processes in business, industry and government. You will use mathematical and computer-based models alongside visual or verbal representations to analyse the development and application of solutions to real managerial problems.
You can choose from an extensive range of options, starting your degree with introductory courses covering Probability, Statistics, Management Science and Operational Research. In the second and third years you will develop your skills by taking modules from a wide range of core and optional modules including Stochastic Processes, Statistical Models, Bayesian Inference, Project Management and Consultancy Skills, Optimisation and Business Modelling and Simulation.
A four-year work-experience variant of this degree is also available: please indicate your proposed intention to take the work-experience variant in Section 3 of your UCAS application.
Grade Requirements
A Level AAB including A in Mathematics or Statistics
International Baccalaureate 35 points overall with 16 points from the best 3 Higher Level subjects including Grade 6 in Higher Level Mathematics or Statistics
BTEC Distinction, Distinction, Distinction
Access to HE Diploma in a relevant subject including Distinctions in the majority of units
Other Qualifications We welcome applications from students with other internationally recognised qualifications. For more information please visit the international qualifications webpage or contact the Undergraduate Admissions Office directly.
Essential Subjects
Mathematics or Statistics
GCSE Mathematics (B); English Language (C)
IELTS 6.5 (with at least 5.5 in each component)
Further Information
General Studies Offers normally include General Studies if it is taken as a fourth A level
Combination of Qualifications Applications from students with a combination of qualifications are welcomed, for further advice please contact the Undergraduate Admissions Office directly.
Taking a gap year Applications for deferred entry welcomed
Contact Undergraduate Admissions Office + 44 1524 592028 or via ugadmissions@lancaster.ac.uk
Many of Lancaster's degree programmes are flexible, offering students the opportunity to cover a wide selection of subject areas to complement their main specialism. You will be able to study a range of modules, some examples of which are listed below.
Core
This module provides the student with an understanding of functions, limits, and series, and knowledge of the basic techniques of differentiation and integration. We introduce examples of functions and their graphs, and techniques for building new functions from old. We then consider the notion of a limit and introduce the main tools of calculus and Taylor Series. Students will also learn how to add, multiply and divide polynomials, and be introduced to rational functions and their partial fractions.
The exponential function is defined by means of a power series which is subsequently extended to the complex exponential function of an imaginary variable, so that students understand the connection between analysis, trigonometry and geometry. The trigonometric and hyperbolic functions are introduced in parallel with analogous power series so that students understand the role of functional identities. Such functional identities are later used to simplify integrals and to parametrise geometrical curves.
This module extends the theory of calculus from functions of a single real variable to functions of two real variables. Students will learn more about the notions of differentiation and integration and how they extend from functions defined on a line to functions defined on the plane. We see how partial derivatives help us to understand surfaces, while repeated integrals enable us to calculate volumes. Students will also investigate complex polynomials and use De Moivre’s theorem to calculate complex roots.
In mathematical models, it is common to use functions of several variables. For example, the speed of an airliner can depend upon the air pressure, temperature and wind direction. To study functions of several variables, we introduce rates of change with respect to several quantities. We learn how to find maxima and minima. Applications include the method of least squares.
Management science is used in all major organisations in industry, commerce, finance and government. Its application might involve well-defined problems, such as reducing the cost of a complex goods distribution network, or more nebulous problems, such as improving patient care in hospital. Techniques based on mathematics and statistics can be extremely powerful in helping to solve these organisational problems.
Five such techniques will be introduced:
The module emphasises not only how to apply techniques, but also when (and when not) to apply them. There is a stress on practical examples of using the techniques.
You will work on two challenging case studies based on real problems. These provide the opportunity to apply concepts and techniques of problem solving, making recommendations and reporting results. To take this module you must also be taking one of MSCI 101, 100 or MNGT130.
To enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems, the module begins with a brief overview of statistics in science and society and then moves on to the selection of appropriate probability models to describe systematic and random variations of discrete and continuous real data sets. Students will learn to implement statistical techniques and to draw clear and informative conclusions.
The module will be supported by the statistical software package ‘R’, which forms the basis of weekly lab sessions. Students will develop a strategic understanding of statistics and the use of associated software, and this underpins the skills needed for all subsequent statistical modules of the degree.
Introducing the theory of matrices together with some basic applications, students will learn essential techniques such as arithmetic rules, row operations and computation of determinants by expansion about a row or a column.
The second part of the module covers a notable range of applications of matrices, such as solving systems of simultaneous linear equations, linear transformations, characteristic equation and eigenvectors and eigenvalues.
Probability theory is the study of chance phenomena, the concepts of which are fundamental to the study of statistics. This module will introduce students to some simple combinatorics, set theory and the axioms of probability.
Students will become aware of the different probability models used to characterise the outcomes of experiments that involve a chance or random component. The module covers ideas associated with the axioms of probability, conditional probability, independence, discrete random variables and their distributions, expectation and probability models.
This module is designed to give you an introduction to probability and statistics and to make you familiar with some useful computer tools.
The statistical topics covered are sampling, introductory data analysis and presentation, index numbers, probability, the use of some important probability distributions, confidence intervals and hypothesis tests for means and proportions, regression analysis with two variables. The computing side of the module introduces the use of word processing, spreadsheet software for statistical calculations, PowerPoint for presentations and management reports.
Available to students taking degrees in the departments of Accounting and Finance, Economics, Management Science and Mathematics, and to incoming exchange students with college-level Mathematics.
Core
The overall objective of this module is to introduce you to some of the key communication and technical skills needed in project work and to give you the opportunity to practise those skills in a progressive way.
Via a group simulated consultancy study, it covers some of the key communication and technical skills needed in project work, such as teamwork, interviewing for information, oral presentations and writing a management report.
This module will give you the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.
You’ll consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of your study will also involve looking at the concepts of length and angle with regard to vector spaces.
Probability provides the theoretical basis for statistics and is of interest in its own right.
You’ll revisit basic concepts from the first year probability module, and extend these to encompass continuous random variables, investigating several important continuous probability distributions.
You’ll then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.
Designed as a complete introduction to the theory and practice of managing business projects, this module introduces project management methods in a way which links to the life cycle of a typical project – from the early project identification and definition stages, through project execution and control, to issues of implementation and change.
The coverage of the early stages of the project cycle uses methods emerging from the systems movement and stresses the strategic relevance of project management.
The operational management of the project is covered by introducing techniques for planning, scheduling and controlling projects. Attention is also given to the people management aspects of this process, especially to leadership, team-working, motivation and direction.
Statistics is the science of understanding patterns of population behaviour from data.
In this module we approach this problem by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.
You’ll focus on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and also considering linear regression techniques within the statistical modelling framework.
Optional
This module gives you an introduction to statistical techniques and their applications in the context of business and management problems. In addition, it is designed to develop your ability to make effective use of computer software for data analysis.
The following topics are covered:
At the heart of many real management problems are data that needs to be described, analysed and interpreted. Statistical methods are important across the range of Management School subject areas (e.g. accounting and finance, marketing, economics, operations management and operational research). This module develops your ability to describe, analyse and interpret data soundly, making effective use of computer software.
Developing these skills will also help you demonstrate to prospective employers that you have practical skills that can immediately be put to good use to solve problems for organisations either in the public or private sector.
The lecture materials, and the problems you are asked to solve in workshops, reflect the problems that organisations have to solve in practical situations where data analysis skills are required.
Core
This module covers the skills needed to improve business process by modelling and simulation.
Computer simulation methods are among the most commonly used approaches within operational research and management science. This module teaches you the skills required to apply simulation successfully to help improve the running of a business, and it shows how companies can find good solutions by predicting the effects of changes before implementing them.
Modern simulation packages are a valuable aid in building a simulation model, and this module uses the Witness simulation package, which is widely used commercially. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each task required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.
Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.
This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.
You’ll also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?
The approach taken in this course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.
This module describes a variety of optimisation algorithms and how business problems can be modelled using these techniques.
Optimisation is one of the primary techniques associated with management science/operational research. Linear programming models are used routinely in many industries, including petroleum refining and the food industry. Integer linear programming models are increasingly being used in practice for complex scheduling problems such as those that arise in the airline industry where such models have saved large amounts of money. Skills in formulating and solving applied optimisation problems are valuable for anybody interested in a career in operational research or business modelling and consultancy.
This module is designed to enable you to apply optimisation techniques to business problems.
Four main topics are covered:
Linear programming
Specially-structured linear programs
Integer and mixed-integer programming
Heuristics for large-scale problems
Optional
Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.
Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.
This module aims to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, study design, causality and confounding.
You’ll look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems you’re investigating as well as the mathematical and statistical concepts underpinning inference.
This module explores the concept of generalized linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal). You’ll also become familiar with the programme R, which you’ll have the opportunity to use in weekly workshops.
This module covers important examples of stochastic processes, and how these processes can be analysed.
As an introduction to stochastic processes you’ll look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).
You’ll then focus on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). You’ll discover how to analyse Markov processes, and how they are used to model queues and populations.
Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module you’ll become familiar with topics from classical statistics as well as some from emerging areas.
You’ll explore time series data through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. You’ll also study time series and volatility modelling, where we’ll discuss the techniques for the analysis of such data with emphasis on financial application.
Another area you’ll focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis. Lastly you’ll spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.
Lancaster University offers a range of programmes, some of which follow a structured study programme, and others which offer the chance for you to devise a more flexible programme. We divide academic study into two sections - Part 1 (Year 1) and Part 2 (Year 2, 3 and sometimes 4). For most programmes Part 1 requires you to study 120 credits spread over at least three modules which, depending upon your programme, will be drawn from one, two or three different academic subjects. A higher degree of specialisation then develops in subsequent years. For more information about our teaching methods at Lancaster visit our Teaching and Learning section.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
The logical and analytical skills you’ll develop by studying Management Science and Mathematics are appreciated by employers in a wide variety of occupations and can be transferred easily to a range of situations.
Lancaster graduates find our Management Mathematics degree is an ideal foundation for careers in the private sector, such as consultancy firms or manufacturing companies, as well as in the service and public sectors, for example, the NHS.
Some of our former alumni are now based in a management science group, while others are part of multi-functional teams. Many are now in senior positions.
We set our fees on an annual basis and the 2017/18 entry fees have not yet been set.
As a guide, our fees in 2016 were:
UK/EU | Overseas |
---|---|
£9,000 | £16,860 |
Lancaster University's priority is to support every student to make the most of their life and education and we have committed £3.7m in scholarships and bursaries. 400 students each year will be entitled to bursaries or scholarships to help them with the cost of fees and/or living expenses. Our financial support depends on your circumstances and how well you do in your A levels (or equivalent academic qualifications) before starting study with us.
Scholarships recognising academic talent:
Bursaries for life, living and learning
Any financial support that you receive from Lancaster University will be in addition to government support that might be available to you (eg fee loans) and will not affect your entitlement to these.
For full details of the University's financial support packages including eligibility criteria, please visit our fees and funding page
Students also need to consider further costs which may include books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation it may be necessary to take out subscriptions to professional bodies and to buy business attire for job interviews.