UCAS Code
GG1K
Entry Year
2018
also available in 2017
A Level Requirements
AAA
see all requirements
see all requirements
Duration
Full time 4 Year(s)
Studying for a combined honours degree in Computer Science and Mathematics at Lancaster gives you the opportunity to learn in two of the country’s leading teaching and research departments. Lancaster’s Department of Mathematics and Statistics is ninth in the UK, as ranked by The Times Good University Guide, 2016.
Your degree unites the fundamentals of Computer Science - including languages and logic, systems, communications and software engineering - with pure mathematics concepts, including algebra and analysis. Lectures and tutorials are combined with projects and practical laboratory sessions where you can put theory into practice.
You’ll start your degree with courses covering Calculus, Discrete Mathematics and Probability. In your second year, you’ll move on to subjects such as Software Project Management and
Linear Algebra before completing your degree with modules including Hilbert Space, Number Theory, Bayesian Inference, and Geometry of Curves and Surfaces.
Additionally, MSci students write a substantial dissertation in their fourth year under the supervision of a member of staff from one of the departments. On completion of this degree, you will have Masters level proficiency in mathematics, computer science, research methods and professional skills.
A Level AAA including A level Mathematics or Further Mathematics OR AAB including A level Mathematics and Further Mathematics
IELTS 6.0 overall with at least 5.5 in each component. For other English language qualifications we accept, please see our English language requirements webpages.
International Baccalaureate 36 points overall with 16 points from the best 3 Higher Level subjects including 6 in Mathematics HL
BTEC May be accepted alongside A level Mathematics grade A and Further Mathematics grade A
Access to HE Diploma May occasionally be accepted
STEP Paper or the Test of Mathematics for University Admission Please note it is not a compulsory entry requirement to take these tests, but for applicants who are taking any of the papers alongside Mathematics and/or Further Mathematics we may be able to make a more favourable offer. Full details can be found on the Mathematics and Statistics webpage.
We welcome applications from students with a range of alternative UK and international qualifications, including combinations of qualification. Further guidance on admission to the University, including other qualifications that we accept, frequently asked questions and information on applying, can be found on our general admissions webpages.
Contact Admissions Team + 44 (0) 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.
Students are introduced to the basic ideas and notations involved in describing sets and their functions. The module helps students to formalise the idea of the size of a set and what it means to be finite, countably infinite or uncountably finite. For finite sets, we can say that one is bigger than another if it contains more elements. What about infinite sets? Are some infinite sets bigger than others? We develop the tools to answer these questions and other counting problems, such as those involving recurrence relations, e.g. the Fibonacci numbers.
Rather than counting objects, we might be interested in connections between them, leading to the study of graphs and networks – collections of nodes joined by edges. There are many applications of this theory in designing or understanding properties of systems, such as the infrastructure powering the internet, social networks, the London Underground and the global ecosystem.
This module provides students with an insight into the importance and relevance of the principles of computer science. Gaining the essential knowledge needed for analysing and characterising the efficiency of algorithms and computer programs, students learn how to make the right design choice when implementing computer programs to optimise efficiency for given design parameters.
Students also study the role and characteristics of data structures, and they gain an understanding of the continuing importance of classical algorithms in computer science.
The lectures in this module are supported by seminars and practicals, so that students can develop the ability to work efficiently both independently and in small groups.
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.
A vast number of naturally occurring phenomena are modelled by differential equations, for which solutions are required to explain the behaviour of these phenomena. This module provides the student with techniques for solving a number of standard types of differential equation.
Students will apply these methods to naturally occurring phenomena, such as bacterial-population growth, tumour expansion and oscillating systems subject to forcing and friction, in order to explain their behaviour and seek solutions. The method of solution by Laplace transforms is also introduced.
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.
This module introduces the student to logic and mathematical proofs, with emphasis placed on proving general theorems than on performing calculations. This is because a result which can be applied to many different cases is clearly more powerful than a calculation that deals only with a single specific case.
We take a look at the language and structure of mathematical proofs in general, emphasising how logic can be used to express mathematical arguments in a concise and rigorous manner. These ideas are then applied to the study of number theory, establishing several fundamental results such as Bezout’s Theorem on highest common factors and the Fundamental Theorem of Arithmetic on prime factorisations.
The concept of congruence of integers is introduced to students and they study the idea that a highest common factor can be generalised from the integers to polynomials.
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.
Computer programming is a highly practical skill in our quickly developing world. In this module students develop the skills expected of a principled computer programmer as they learn how to write, analyse, debug, test and document computer programs. Students will be introduced to both the C and Java programming languages, two of the most widely used languages in the world. They will learn about best practice of day-to-day techniques associated with software development and gain an understanding of the software development cycle. Learning about the challenges faced by software developers in addressing scalability and complexity in computer software, students will be able to work independently to develop moderately complex computer programs.
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.
Core
SCC210 gives students the opportunity to build upon their skills and knowledge from Year 1 to create a real-world system in a group context. As part of a group, students will work effectively to gather system requirements, design and then implement the project in addition to accurately evaluating it. The results of the Group Project will be showcased to IBM, who award prizes for the best project every year. The course aims to increase theoretical knowledge and practical skills in prototyping, project planning, project management, management and execution, games design, systems design and testing strategies.
Throughout the Michaelmas and Lent terms, each group will receive 25 hours of contact time with their supervisor, whilst working together. By the end of the module, students will be able to successfully co-ordinate working together, skilfully resolving any problems or conflicts. Students will advance their abilities in writing reports, communication and presenting projects as part of a group. Groups will present their project to IBM in written, graphical and verbal forms.
Students will learn theoretical and practical topics in Human-Computer Interaction, with lab work offering hands on experience of design, implementation and the ability to evaluate interactive systems through practical case studies. The course explores the underpinnings of human perception, user-centred design and participatory design processes, with students learning multiple design techniques. The module leads to an understanding of how internal system design impacts external user interface behaviour and highlights the importance of accessibility for all users.
By the end of the module, students will be able to successfully integrate diverse information to form a coherent understanding of Human-Computer Interaction; critically reflect on technical advancements in HCI and demonstrate the independent learning abilities needed for continual professional development and effective written and verbal skills.
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.
Students will gain the essential skills and knowledge to operate within the professional, legal and ethical frameworks of their profession. Techniques for breaking down a project into manageable parts and efficient time allocation are taught, leading to a fundamental understanding of the skills and methods required to pursue scientific inquiry and the fundamental concepts and tools for statistical analysis to measure and explain data. Exemplars and guidelines on producing concise and structured scientific reports are offered and students receive additional lectures on presentation skills, professional ethics in relation to computing and communications. Finally, lectures provide an awareness of fundamental legal aspects related to a profession in computing and communications, including intellectual property rights and patent law.
Throughout this course, students will gain a high level of awareness of subject specific skills and general competence needed to gain employment in their field. The module develops academic writing and research skills in a computing context, complimenting students’ other modules.
Software Design offers the opportunity to gain an understanding of the importance of software architecture design, different styles of architecture and the meaning of quality attributes for software design such as maintainability, performance and scalability. Students will gain knowledge of systematic approaches to developing software design using a set of graphical models. The design process involved in developing several modes of the system at different levels of abstraction is explained and they will be introduced to object oriented design with UML.
Throughout the module, students will appreciate the broader context of the role of computer science in the workplace, and the key role it plays in implementing software. The course also looks at understanding the meaning of quality attributes for software design as well as architectural models for specific software systems. Students will gain an insight into the main quality attributes for deciding classes. Students will be able to interpret and construct UML models of software and implement a design expressed as a UML mode as well as understanding how to use various design patterns to address certain problems.
Optional
This module gives you the opportunity to enhance your project skills, including both subject-related and transferable skills. You’ll work on mathematical document preparation and presentation, scientific writing, and working with a statistical software package.
You’ll revisit LaTeX and R, and work on your oral communication skills, scientific writing, a written group project, and a group presentation.
This module builds on the binary operations studies in previous modules, such as addition or multiplication of numbers and composition of functions. Here you’ll select a small number of properties which these and other examples have in common, and use them to define a group.
You’ll also consider the elementary properties of groups. It turns out that several surprisingly elegant results can be proved fairly simply! By looking at maps between groups which 'preserve structure' you’ll discover a way of formalizing (and extending) the natural concept of what it means for two groups to be 'the same'.
Ring theory provides a framework for studying sets with two binary operations: addition and multiplication. This gives us a way to abstractly model various number systems, proving results that can be applied in many different situations, such as number theory and geometry. Familiar examples of rings include the integers, the integers modulation, the rational numbers, matrices and polynomials, but you’ll meet several less familiar examples too.
Complex Analysis has its origins in differential calculus and the study of polynomial equations.
In this module you’ll consider the differential calculus of functions of a single complex variable and study power series and mappings by complex functions. You’ll use integral calculus of complex functions to find elegant and important results, including the fundamental theorem of algebra, and you’ll also use classical theorems to evaluate real integrals.
The module ends with basic discussion of harmonic functions, which play a significant role in physics.
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.
In this module you’ll take a thorough look at the limits of sequences and convergence of series. You’ll learn to extend the notion of a limit to functions, leading to the analysis of differentiation, including proper proofs of techniques learned at A-level.
You’ll spend time studying the Intermediate Value Theorem and the Mean Value Theorem, and will discover that they have many applications of widely differing kinds. The next topic is new: sequences and series of functions (rather than just numbers), which again has many applications and is central to more advanced analysis.
Next we put the notion of integration under the microscope. Once it’s properly defined (via limits), you’ll learn how to get from this definition to the familiar technique of evaluating integrals by reverse differentiation. You’ll also explore some applications of integration that are quite different from the ones in A-level, such as estimations of discrete sums of series.
Further possible topics include Stirling's Formula, infinite products and Fourier series.
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.
Core
Students will gain an introduction to fundamental concepts in artificial intelligence and learn about current trends and issues. Topics such as Knowledge Representation and Reasoning, Decision Making (DM) and Decision Making Under Uncertainties, and Probability Theory are all explored throughout the course. Artificial Intelligence offers experience in supervised and unsupervised machine learning, neural networks and decision trees. Multivariate methods, and clustering and classification approaches are taught and there is an introduction to evolutionary algorithms, phenotypes, genotypes and fundamental genetic operators. Programming languages suitable for intelligent systems, such as Scheme and Prolog are investigated and students are made familiar with the applications of artificial intelligence.
This module sees an awareness of the requirements of artificial intelligence systems in general, and in the context of computing and communications systems. Through knowledge based, probabilistic and logical systems, the module provides students with an awareness of competing approaches and a broad grounding in artificial intelligence. Additionally they will understand and critically analyse artificial intelligence techniques used in modern computers and mobile devices.
Students taking this module will be exposed to a small number of current computer science related topics from different subject areas. These areas will be introduced through a lecture covering the general aspects of the area, however the main topical exposure will happen through seminar style in-depth study of specific parts of the subject area. This will include guided reading, a written seminar essay, presentation of the results in plenary and a discussion of the findings within the seminar group. Through this the students will further deepen their skills in carrying out research, present their work in written, graphical and verbal form (which is related to SCC205 Professional Skills & Research Methods) and to discuss the research work with experts and their own peers.
The subject areas contributing to the seminars will come from SCC’s different thematic strands and will include natural language engineering, policy based network resilience, eye-tracking for ubiquitous computing applications, and a focus on energy aware control and sensing in home environments. Students will conduct independent and in-depth research into an advanced topic of computing or communications reflecting current topical and research issues, and will be required to apply research skills to specific subject areas, present the results of their finding in front of a peer group and supervisor, as well as lead a guided discussion on their chosen subject area. The module aims to familiarise students with a variety of current and topical areas in computer science and communications that provide an additional and more in-depth perspective on subject areas taught throughout second and third year.
During the course of the module, students will be expected to research a complex technical topic related to their studies, including analysing, structuring, summarising, documenting and presenting their findings in front of a large group. They will gain topical knowledge and skills related to the subject areas of the seminars and will learn with and from their peers the specific topical areas of the seminars. The module will enable students to produce a coherent document describing their research findings on a complex technical or research topic, present technically intricate issues in a coherent manner, and discuss and defend their position on a specific topic within a seminar group.
Providing an introduction to formal languages, grammars, automata and how these concepts relate to programming in terms of compilers and the compilation process, students will learn about syntax and semantics, phrase structure grammars and the Chomsky Hierarchy as well as processes such as derivation and parsing. The module focuses on grammar equivalence and ambiguity in context free grammars and its implications. There is exploration of the relationship between languages and abstract machines. Students are presented with the concept of computation alongside Turing’s thesis, alternative models of computation and applications of abstract machine representations. There are further introductions to the compilation process including lexical analysis and syntactic analysis.
By the end of this module, students will understand the relation of programming languages and the theory of formal languages. They will possess an essential understanding of the compilation process for a high-level programming language. Students are encouraged to engage with theoretical aspects of computer science to compliment practical skills in other parts of their degree. There are links to other disciplines such as linguistics, and the course explains the challenges of compilation in the context of software development and computer science.
This module gives you the opportunity to enhance your project skills, including both subject-related and transferable skills. You’ll work on mathematical document preparation and presentation, scientific writing, and working with a statistical software package.
You’ll revisit LaTeX and R, and work on your oral communication skills, scientific writing, a written group project, and a group presentation.
Covering a range of topics, including asset identification and assessment, threat analysis and management tools and frameworks, students will become familiar with attack lifecycle and processes, as well as risk management and assessment processes, tools and frameworks. The module covers mitigation strategies and the most appropriate mitigation technologies and offers knowledge on assurance frameworks and disaster recovery planning. There is also an opportunity to learn about infrastructure design and implementation technologies and attack tree and software design evaluation.
Students will gain an understanding of the different ways in which an IT professional can make effective decisions when securing an IT infrastructure. The course will make them aware of the tools, frameworks and models that can be used to identify assets, threats and risks, before selecting the most appropriate strategies to manage the exposure that IT infrastructure faces in the light of this analysis. The module builds on their skills with a practical examination of the mechanisms by which IT infrastructures are attacked.
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.
Combinatorics is the core subject of discrete mathematics which refers to the study of mathematical structures that are discrete in nature rather than continuous (for example graphs, lattices, designs and codes). While combinatorics is a huge subject - with many important connections to other areas of modern mathematics - it is a very accessible one.
In this module you’ll be introduced to the fundamental topics of combinatorial enumeration (sophisticated counting methods), graph theory (graphs, networks and algorithms), and combinatorial design theory (Latin squares and block designs). You’ll also explore important practical applications of the results and methods.
This module considers questions relating to linear ordinary differential equations. While explicit solutions can only be found for special types of equations, some of the ideas of real analysis allow us to answer questions about the existence and uniqueness of solutions to more general equations as well as allowing us to study certain properties of these solutions.
This module gives you a solid foundation in the basics of algebraic geometry. You’ll explore how curves can be described by algebraic equations, and learn how to understand and use abstract groups in dealing with geometrical objects (curves).
You’ll also gain an understanding of the notions and the main results pertaining to elliptic curves, and the way that algebra and geometry are linked via polynomial equations. Finally you’ll learn to perform algebraic computations with elliptic curves.
This module is an introduction to smooth curves and surfaces in three-dimensional space. You’ll encounter various geometrical properties of these objects, such as length, area, torsion and curvature, and will have the opportunity to explore the meaning of these quantities. You’ll use a variety of examples to calculate their values, and will use them to apply techniques from calculus and linear algebra.
In this module you’ll develop the knowledge of groups that you’ve gained in second year. You’ll study ‘direct products’ which are used to construct new groups, while any finite group is shown to ‘factor’ into ‘simple’ pieces. You’ll also consider situations in which a group ‘acts’ on a set by permuting its elements; this powerful idea is used to identify the symmetries of the Platonic solids, and to help study the structure of groups themselves.
In this module you’ll examine the notion of a norm, which introduces a generalized notion of ‘distance’ to the purely algebraic setting of vector spaces. You’ll learn several quite natural ways to do this, both for vectors of any dimension and for functions. You’ll then focus on the more special notion of an inner product which generalizes angles at the same time as distances.
Once we’ve established these concepts, you’ll have the opportunity to study geometrical ideas like orthogonality, which can be seen to apply to much more general spaces than Euclidean spaces of three (or even n) dimensions, notably to infinite dimensional spaces of functions. For example, Hilbert space theory shows how Fourier series are really another case of expressing an element in terms of a basis, and how you can use orthogonality to find best approximations to a given function by functions of a prescribed type. Finally, you’ll look at some of the main results of linear algebra, which generalize very nicely to linear operators between Hilbert spaces.
Introducing the Lebesgue integral for functions on the real line, this module features a classical approach to the construction of Lebesgue measure on the line and to the definition of the integral. The bounded convergence theorem is used to prove the monotone and dominated convergence theorems, and the results are illustrated in classical convergence problems including Fourier integrals.
Among the range of topics addressed on this module, students will become familiar with Lebesgue's definition of the integral, and the integral of a step function. There will be an introduction to subsets of the real line, including open sets and countable sets. Students will measure of an open set, and will discover measurable sets and null sets. Additionally, the module will focus on integral functions, along with Lebesgue's integral of a bounded measurable function, his bounded convergence theorem and the integral of an unbounded function. Dominated convergence theorem; monotone convergence theorem.
Other topics on the module will include applications of the convergence theorems and Wallis's product for P. Gaussian integral, along with some classical limit inversion results and the Fourier cosine integral. Students will develop an understanding of Dirichlet's comb function, Archimedes' axiom and Cantor's uncountability theorem, and will learn to prove the structure theorem for open sets. In addition, students will be able to prove covering lemmas for open sets, as well as understanding the statement of Heine—Bore theorem, as well as understanding the concept and proving basic properties of outer measure. As well as understanding inner measure. Finally, students will be expected to prove Lebesgue's theorem on countable additivity of measure.
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 is designed to give you an opportunity to consider key issues in the teaching and learning of mathematics. Whilst it is an academic study of mathematics education and not a training course for teachers, it does provide an excellent foundation for a PGCE especially in preparing students to write academically.
Having studies Mathematics for many years, you are well-placed to reflect upon that experience and attempt to make sense of it in the light of theoretical frameworks developed by researchers in the field. Within this course we hope to help you with this process so that as a Mathematics graduate you will be able to contribute knowledgeably to future debate about the ways in which your subject is treated within the education system.
This module is run as a partnership between the Department of Mathematics and Statistics, the University of Cumbria and the Students’ Union’s volunteering unit.
Designed with employability in mind, this module is based on the Students’ Union’s Schools Partnership Scheme, which supports Lancaster students on 10-week placements in local primary and secondary schools.
You’ll have the opportunity to take part in classroom observation and assistance, the development of classroom resources, the provision of one-on-one or small group support and possibly even teaching sections of lessons to the class as a whole.
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 gives an introduction to the key concepts and methods of metric space theory, a core topic for pure mathematics and its applications. Studying this module will give you a deeper understanding of continuity as well as a basic grounding in abstract topology.
You’ll also gain a firm foundation for further study of many topics including geometry, Lie groups and Hilbert space, and learn to apply your knowledge to areas including probability theory, differential equations, mathematical quantum theory and the theory of fractals.
Number theory is the study of the fascinating properties of the natural number system.
Many numbers are special in some sense, eg. primes or squares. Which numbers can be expressed as the sum of two squares? What is special about the number 561? Are there short cuts to factorizing large numbers or determining whether they are prime (this is important in cryptography)? The number of divisors of an integer fluctuates wildly, but is there a good estimation of the ‘average’ number of divisors in some sense?
Questions like these are easy to ask, and to describe to the non-specialist, but vary hugely in the amount of work needed to answer them. An extreme example is Fermat’s last theorem, which is very simple to state, but was proved by Taylor and Wiles 300 years after it was first stated. To answer questions about the natural numbers, we sometimes use rational, real and complex numbers, as well as any ideas from algebra and analysis that help, including groups, integration, infinite series and even infinite products.
This module introduces some of the central ideas and problems of the subject, and some of the methods used to solve them, while constantly illustrating the results with exercises and examples involving actual numbers.
This module is ideal for students who want to develop an analytical and axiomatic approach to the theory of probabilities.
First you’ll examine the notion of a probability space through simple examples featuring both discrete and continuous sample spaces. You’ll then use random variables and the expectation to develop a probability calculus, which you can apply to achieve laws of large numbers for sums of independent random variables.
You’ll also use the characteristic function to study the distributions of sums of independent variables, which have applications to random walks and to statistical physics.
This module covers the basics of ordinary representation theory. You’ll learn the concepts of R-module and of group representations, the main results pertaining to group representations, and will handle basic applications in the study of finite groups. You’ll also learn to perform computations with representations and morphisms in a selection of finite groups.
This module furthers your knowledge of commutative rings from your second year study.
You’ll be introduced to two new classes of integral domains called Euclidean domains, where you have a counterpart of the division algorithm, and unique factorization domains, in which an analogue of the Fundamental Theorem of Arithmetic holds.
You’ll also explore how well-known concepts from the integers such as the highest common factor, the Euclidean algorithm, and factorization of polynomials, carry over to this new setting.
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.
Core
Students will gain a formal understanding of research and will develop the ability to critically reflect on research approaches and practices in the field of computing. Research Methods will also encourage an appreciation of the different ways that other disciplines, academic communities and industries all conduct research. There will be an opportunity to plan a research project and develop a convincing study design to address a challenge or problem. This module explores ethical and data management issues associated with research as well as research and innovation practices in industry.
The module covers the fundamentals of research such as sampling and design, before considering strategies and research methods. Furthermore, the module offers greater insight into research design, such as how to structure and frame research studies, choosing a research strategy and selecting the best research method. Students will learn about ethical issues in research and approval processes before understanding the opportunities and expectations from their industrial placements.
Optional
Explore advanced topics in experimental Human-Computer Interaction (HCI), such as understanding users and their requirements, investigating design spaces and prototyping and developing innovative interaction techniques. The module offers increased experience in HCI literature and design methods both with and without users, as well as practical experience of using supporting tools. Students will learn about modelling techniques and design space techniques as part of the module.
Upon completion of the module, students will have the knowledge to conduct experimental HCI research and have the motivation, experience and tools for understanding users and their requirements for interaction. The module helps to develop scientific writing skills and analytical thinking and prepares students for further postgraduate study, or for a successful career in IT or computing.
This module covers advanced topics in experimental Human-Computer Interaction (HCI) with an emphasis on experimental design, evaluation methodologies, statistical analysis and result interpretation. Whilst engaging with a number of key topics, students will be asked to explore the evaluation process. Students will learn to recognise when HCI is required and which forms of evaluation is necessary in a given situation, for example making appropriate selections from systems vs user and qualitative vs quantitative evaluations.
Practical sessions will enable students to develop skills with statistical analysis packages. They will also receive guidance on the application of appropriate tests and result reporting.
This module provides students with up-to-date information on current applications of data in both industry and research. Expanding on the module ‘Fundamentals of Data’, students will gain a more detailed level of understanding about how data is processed and applied on a large scale across a variety of different areas.
Students will develop knowledge in different areas of science and will recognise their relation to big data, in addition to understanding how large-scale challenges are being addressed with current state-of-the-art techniques. The module will provide recommendations on the Social Web and their roots in social network theory and analysis, in addition their adaption and extension to large-scale problems, by focusing on primer, user-generated content and crowd-sourced data, social networks (theories, analysis), recommendation (collaborative filtering, content recommendation challenges, and friend recommendation/link prediction).
On completion of this module, students will be able to create scalable solutions to problems involving data from the semantic, social and scientific web, in addition to abilities gained in processing networks and performing of network analysis in order to identify key factors in information flow.
In this module you’ll study topics related to the understanding of special models to describe the extreme values of a financial times series, and you’ll learn to fit appropriate extreme value models to data which are maxima or threshold exceedance. You’ll also learn to use extreme value models to evaluate Value at Risk and gain an understanding of the impact of heavy tailed data on standard statistical diagnostic tools.
Bayesian statistics is a framework for rational decision making using imperfect knowledge, expressed through probability distributions. Bayesian principles are applied in the fields of navigation, control, automation and artificial intelligence. The aim of decision makers is to make rational decisions that maximise some personal utility function which may represent quantities such as money which are related to the wealth of an individual.
Within the Bayesian framework, knowledge of the world, (the prior) is updated as fresh observations arrive to yield a posterior distribution which shows the revised knowledge. The evidence for the model is expressed by calculating a marginal likelihood. Future behaviour and the fit of the model are assessed using a predictive distribution. This includes sampling uncertainty and uncertainty of our knowledge.
In this module you’ll look at the posterior, the marginal and the predictive distributions for several one parameter conjugate models, and two families of multi-parameter fully conjugate models. You’ll extend the range of belief types that can be modelled by using mixtures of conjugate priors, and will also explore the use of non-conjugate formulations of models and use Monte-Carlo integration, importance sampling and rejection sampling for calculating and simulating from these distributions.
Clinical trials are planned experiments on human beings designed to assess the relative benefits of one or more forms of treatment. For instance, we might be interested in studying whether aspirin reduces the incidence of pregnancy-induced hypertension; or we may wish to assess whether a new immunosuppressive drug improves the survival rate of transplant recipients. Treatments may be procedural, for example, surgery or methods of care.
This module combines the study of technical methodology with discussion of more general research issues. First we’ll discuss the relative advantages and disadvantages of different types of medical studies. We’ll then explore the basic aspects of clinical trials as experimental designs, looking in particular at the definition and estimation of treatment effects. We’ll also cover cross-over trials, concepts of sample size determination, and equivalence trials. The module also includes a brief introduction to sequential trial designs and meta-analysis.
In this module you’ll be introduced to Markov chain Monte Carlo methods and how to use them as a powerful technique for performing Bayesian inference on complex stochastic models.
The first part of the module looks in detail at the necessary concepts and theory for finite state-space Markov chains, before introducing analogous concepts and theory for continuous state-space Markov chains. In the second part of the course you’ll investigate the Metropolis-Hastings algorithm for sampling from a distribution known up to a constant of proportionality.
In the third (and largest) part, you’ll take this knowledge and apply it to Bayesian inference as well as studying the Gibbs sampler. You’ll also examine the two most common Metropolis-Hastings algorithms (the random walk and the independence sampler). Examples will include hierarchical models, random effects models, and mixture models.
This module provides a comprehensive coverage of the problems related to data representation, manipulation and processing in terms of extracting information from data, including big data.
Practical laboratory sessions will offer students an opportunity to gain a fundamental theoretical level of knowledge and skills in computer science. Students will then apply their working understanding to the data primer, data processing and classification. The module will enhance students’ familiarity with dynamic data space partitioning, using evolving, clustering and data clouds, and monitoring the quality of the self-learning system online.
Further skills provided on this module include the ability to develop software scripts that implement advanced data representation and processing and demonstrate their impact on the performance, as well as a working knowledge in listing, explaining and generalising the trade-offs of performance and complexity in designing practical solutions for problems of data representation and processing in terms of storage, time and computing power. Transferrable skills include development in time management and effective report writing.
This module will equip students with the ability to develop and apply an understanding of fundamental principles, techniques and technologies that underpin today's global IT infrastructure. It is designed to be studied in combination with its module namesake, SCC.401-DS, in order to form the systems stream of the Computer Science MSc. Students will learn to assess new systems technologies, to know where technologies fit in a comprehensive schema, and to know what to read in order to develop a deeper level of understanding.
Students will focus on the properties of system components, and will become familiar with the strengths, weaknesses, scalability and bottlenecks of systems components, with the aim to make intelligent and well-reasoned trade-offs between fundamental building blocks of distributed systems in today’s IT infrastructure. The techniques and principles element of the module addresses caching, tiering, replication, synchronisation, failure and reliability, whilst the technologies section covers topics such as interaction paradigms in distributed systems, peer-to-peer architecture and scalable and high-performance networking.
In this module you’ll have the opportunity to learn the basics of algebraic geometry.
You’ll look at how curves can be described by algebraic equations, and will develop an understanding of abstract groups, learning how to use them to deal with geometrical objects (curves). You’ll also study the notions and the main results pertaining to elliptic curves, and will investigate the way that algebra and geometry are linked via polynomial equations, performing algebraic computations with elliptic curves.
This module focuses on the kinds of statistical methods commonly used by statisticians to investigate the relationship between risk of disease and environmental factors.
You’ll explore methods for the analysis of spatial data, including spatial point-process models, spatial case-control methods, spatially aggregated data, point source problems and geostatistics, and will spend time developing your skills performing similar analyses using the statistical package R.
The Fourth Year Project will focus on a significant specification, design, implementation and/or evaluation project at the suitable level for MSci Computer Science. The project sees students tackle a real-world problem by applying their knowledge in computer science. The project is usually achieved in conjunction with an industry placement; however it can be completed at the University. Suggestions made by industry will be vetted by a team of academics to ensure appropriate depth, and if no suitable project with industry can be found, one will be provided by academic staff.
Weekly guidance is given from a member of academic staff from the department to ensure the necessary level of academic content and rigour is being maintained. There are also Business Development mentors in the KBC to provide students with an insight in the day to day expectations and responsibilities of working with industry. The Fourth Year Project is designed to challenge students and develop their existing knowledge, understanding and skills from their undergraduate degree to produce a significant piece of academically rigorous project work.
Galois Theory is, in essence, the systematic study of properties of roots of polynomials. Starting with such a polynomial f over a field k (e.g. the rational numbers), one associates a ‘smallest possible’ field L containing k and the roots of f; and a finite group G which describes certain ‘allowed’ permutations of the roots of f. The Fundamental Theorem of Galois Theory says that under the right conditions, the fields which lie between k and L are in 1-to-1 correspondence with the subgroups of G.
In this module you’ll see two applications of the Fundamental Theorem. The first is the proof that in general a polynomial of degree 5 or higher cannot be solved via a formula in the way that quadratic polynomials can; the second is the fact that an angle cannot be trisected using only a ruler and compasses. These two applications are among the most celebrated results in the history of mathematics.
In this module you’ll learn techniques for formulating sensible models for data, enabling you to tackle problems such as the probability of success for a particular treatment, and how this depends on the patient's age, weight, blood pressure, and so on.
You’ll be introduced to a large family of models, called the generalised linear models (GLMs), including the standard linear regression model as a special case, and will have the opportunity to discuss and investigate the theoretical properties of these models.
You’ll also study a common algorithm called iteratively reweighted least squares algorithm for the estimation of parameters. Using the statistical package ‘R’, you’ll fit and check these models, and will produce confidence intervals and tests corresponding to questions of interest.
In this module you’ll have the opportunity to learn about Hilbert space, consolidating your understanding of linear algebra and enabling you to study applications of Hilbert space such as quantum mechanics and stochastic processes.
You’ll learn how to use inner products in analytical calculations, to use the concept of an operator on an infinite dimensional Hilbert space, to recognise situations in which Hilbert space methods are applicable and to understand concepts of linear algebra and analysis that apply in infinite dimensional vector spaces.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
Students complete a 10 week industrial placement in the Lent term of their 4th year. The University has a range of businesses from SMEs to large corporates for students to be placed in. There are no taught elements in this module, but students have access to an academic supervisor to guide and assist them during the placement. Placements are assigned to students in the Michaelmas term.
Students will gain first-hand experience of working in a contemporary ICT related environment, developing an appreciation and understanding of professional practices and codes of conduct in industrial, commercial and professional settings. Companies will set tasks that are related to students’ knowledge and experience gained throughout their degree, allowing them to apply it in a professional setting. Placements are offered by a variety of companies with different topics. Through an initial matching and application process, we ensure the biggest possible overlap between student interest and company requirements.
In this module you’ll construct Lebesgue measure on the line, extending the idea of the length of an interval. You’ll use this to define an integral which is shown to have good properties under pointwise convergence. Looking at some basic results about the set of real numbers, you will explore properties of countable sets, open sets and algebraic numbers.
You’ll also have the opportunity to illustrate the power of the convergence theorems in applications to some classical limit problems and analysis of Fourier integrals, which are fundamental to probability theory and differential equations.
In this module you'll be introduced to the theory of Lie groups and Lie algebras. You'll also explore the relationship between the two, and will develop an understanding of the way that this forms an important and enduring part of modern mathematics and a great number of fields including theoretical physics. You'll learn to appreciate the subtle and pervasive interplay between algebra and geometry, and to appreciate the unified nature of mathematics. The abstract nature of the course will give you a taste of modern research in pure mathematics.
In this module you’ll learn how to use the likelihood function to obtain and summarise information about unknown parameters. You’ll calculate the likelihood function for statistical models which do not assume independent identically distributed data, and will learn to evaluate point estimates and make statements about the variability of these estimates.
Using the statistical package ‘R’, you’ll use computational methods to calculate maximum likelihood estimates. You’ll spend time developing an understanding of the inter-relationships between parameters, and the concept of orthogonality, and will perform hypothesis tests using the generalised likelihood ratio statistic.
Longitudinal data arise when a time-sequence of measurements is made on a response variable for each of a number of subjects in an experiment or observational study. For example, a patient’s blood pressure may be measured daily following administration of one of several medical treatments for hypertension.
Typically, the practical objective of most longitudinal studies is to find out how the average value of the response varies over time, and how this average response profile is affected by different experimental treatments. This module presents an approach to the analysis of longitudinal data, based on statistical modelling and likelihood methods of parameter estimation and hypothesis testing.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
In this module you’ll construct Lebesgue measure on the line, extending the idea of the length of an interval. You’ll use this to define an integral which is shown to have good properties under pointwise convergence. Looking at some basic results about the set of real numbers, you will explore properties of countable sets, open sets and algebraic numbers.
You’ll also have the opportunity to illustrate the power of the convergence theorems in applications to some classical limit problems and analysis of Fourier integrals, which are fundamental to probability theory and differential equations.
Operator theory is a modern mathematical topic in analysis which provides powerful general methods for the analysis of linear problems, and possibly even infinite dimensional problems.
Early successes were in the solution of differential and integral equations. Now operator theory is also an extensive subject in its own right in the general area of functional analysis.
First you’ll review Hilbert spaces, before spending some time studying infinite-dimensional operators, notably the unilateral shift and multiplication operators, as well as basic concepts. You’ll then consider the criteria for invertibility of self adjoint operators, leading to the spectral theory of such operators.
This module focuses on the basic principles of epidemiology, including its methodology and application to prevention and control of disease.
You’ll examine the concepts and strategies used in epidemiologic studies, and will develop an understanding of the role of epidemiology in preventive medicine and disease investigation. You’ll also develop your knowledge of basic epidemiologic methods and how to apply them, and will develop confidence in assessing the validity of epidemiologic studies with respect to their design and inferences.
The aim of this course is to develop an analytical and axiomatic approach to the theory of probabilities.
You’ll consider the notion of a probability space, illustrated by simple examples featuring both discrete and continuous sample spaces. You’ll then use random variables and the expectation to develop a probability calculus, which is applied to achieve laws of large numbers for sums of independent random variables. You’ll also use the characteristic function to study the distributions of sums of independent variables, applying the results to random walks and to statistical physics.
In this module you’ll learn the basics of ordinary representation theory.
You’ll have the opportunity to explore the concepts of R-module and group representations, and the main results pertaining to group representations, as well as learning to handle basic applications in the study of finite groups. You’ll also develop your skills in performing computations with representations and morphisms in a selection of finite groups.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
This module shows how the rules of probability can be used to formulate simple models describing processes, such as the length of a queue, which can change in a random manner, and how the properties of the processes, such as the mean queue size, can be deduced.
In Stochastic Processes you’ll learn how to use conditioning arguments and the reflection principle to calculate probabilities and expectations of random variables. You’ll also learn to calculate the distribution of a Markov Process at different time points and to calculate expected hitting times, as well as how to determine whether a Markov process has an asymptotic distribution and how to calculate it. You’ll then develop an understanding of how stochastic processes are used as models.
Introducing a range of architectural approaches, techniques and technologies that underpin today’s global IT infrastructure, this module combines with other modules to form the systems stream of the programme. It is designed to enhance students’ knowledge of how building blocks are composed to create systems of systems.
Students will gain a detailed understanding and an ability to critique contemporary systems’ architecture in terms of scalability, resilience, performance and other shortcomings.
The principal ethos of this module is to focus on the principles, emergent properties and the application of systems elements as used in large-scale and high performance systems. Detailed studies and invited industrial speakers will be used to provide supporting real-world context and a basis for seminar discussions. Students are also offered ‘hands-on’ measurement-based coursework that focuses on the scalability of a significant technology.
Fractals, roughly speaking, are strange and exotic sets in the plane (and in higher dimensions) which are often generated as limits of quite simple repeated procedures. The 'middle thirds Cantor set' in [0,1] is one such set. Another, the Sierpinski sieve, arises by repeated removal of diminishing internal triangles from a solid equilateral triangle.
This analysis module will explore a variety of fractals, partly for fun for their own sake but also to illustrate fundamental ideas of metric spaces, compactness, disconnectedness and fractal dimension. The discussion will be kept at a straightforward level and you’ll consider topological ideas of open and closed sets in the setting of R2.
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.
Lancaster’s Computer Science graduates have the skills required to work in a professional software and systems development environment. Our Mathematics graduates are valued by employers recruiting for a range of positions in technology, computing, financial services or management.
Combining both disciplines with transferable skills such as communication and teamwork, you will be prepared for a wide variety of positions in all sectors – from business, government, health and education, among others.
The Department of Mathematics and Statistics organises talks throughout the year on suitable careers, which help to inform students about potential career options and create useful links with employers.
Many graduates also choose to go on to further study and you’ll find Lancaster offers excellent graduate opportunities on Masters and PhD programmes.
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with the relevant life and work based skills. We are unique in that every student is eligible to participate in The Lancaster Award which offers you the opportunity to complete key activities such as work experience, employability/career development, campus community and social development. Visit our Employability section for full details.
We set our fees on an annual basis and the 2018/19 entry fees have not yet been set.
As a guide, our fees in 2017 were:
UK/EU | Overseas |
---|---|
£9,250 | £18,168 |
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. 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:
Continuation of the Access Scholarship is subject to satisfactory academic progression.
Students may be eligible for both the Academic and Access Scholarship if they meet the requirements for both.
Bursaries for life, living and learning:
Students from the UK eligible for a bursary package will also be awarded our Academic Scholarship and/or Access Scholarship if they meet the criteria detailed above.
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
Please note that this information relates to the funding arrangements for 2017, which may change for 2018.
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 for a combined honours degree in Computer Science and Mathematics at Lancaster gives you the opportunity to learn in two of the country’s leading teaching and research departments. Lancaster’s Department of Mathematics and Statistics is ninth in the UK, as ranked by The Times Good University Guide, 2016.
Your degree unites the fundamentals of Computer Science - including languages and logic, systems, communications and software engineering - with pure mathematics concepts, including algebra and analysis. Lectures and tutorials are combined with projects and practical laboratory sessions where you can put theory into practice.
You’ll start your degree with courses covering Calculus, Discrete Mathematics and Probability. In your second year, you’ll move on to subjects such as Software Project Management and
Linear Algebra before completing your degree with modules including Hilbert Space, Number Theory, Bayesian Inference, and Geometry of Curves and Surfaces.
Additionally, MSci students write a substantial dissertation in their fourth year under the supervision of a member of staff from one of the departments. On completion of this degree, you will have Masters level proficiency in mathematics, computer science, research methods and professional skills.
A Level AAA including A level Mathematics or Further Mathematics OR AAB including A level Mathematics and Further Mathematics
IELTS 6.0 overall with at least 5.5 in each component. For other English language qualifications we accept, please see our English language requirements webpages.
International Baccalaureate 36 points overall with 16 points from the best 3 Higher Level subjects including 6 in Mathematics HL
BTEC May be accepted alongside A level Mathematics grade A and Further Mathematics grade A
Access to HE Diploma May occasionally be accepted
STEP Paper or the Test of Mathematics for University Admission Please note it is not a compulsory entry requirement to take these tests, but for applicants who are taking any of the papers alongside Mathematics and/or Further Mathematics we may be able to make a more favourable offer. Full details can be found on the Mathematics and Statistics webpage.
We welcome applications from students with a range of alternative UK and international qualifications, including combinations of qualification. Further guidance on admission to the University, including other qualifications that we accept, frequently asked questions and information on applying, can be found on our general admissions webpages.
Contact Admissions Team + 44 (0) 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.
Students are introduced to the basic ideas and notations involved in describing sets and their functions. The module helps students to formalise the idea of the size of a set and what it means to be finite, countably infinite or uncountably finite. For finite sets, we can say that one is bigger than another if it contains more elements. What about infinite sets? Are some infinite sets bigger than others? We develop the tools to answer these questions and other counting problems, such as those involving recurrence relations, e.g. the Fibonacci numbers.
Rather than counting objects, we might be interested in connections between them, leading to the study of graphs and networks – collections of nodes joined by edges. There are many applications of this theory in designing or understanding properties of systems, such as the infrastructure powering the internet, social networks, the London Underground and the global ecosystem.
This module provides students with an insight into the importance and relevance of the principles of computer science. Gaining the essential knowledge needed for analysing and characterising the efficiency of algorithms and computer programs, students learn how to make the right design choice when implementing computer programs to optimise efficiency for given design parameters.
Students also study the role and characteristics of data structures, and they gain an understanding of the continuing importance of classical algorithms in computer science.
The lectures in this module are supported by seminars and practicals, so that students can develop the ability to work efficiently both independently and in small groups.
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.
A vast number of naturally occurring phenomena are modelled by differential equations, for which solutions are required to explain the behaviour of these phenomena. This module provides the student with techniques for solving a number of standard types of differential equation.
Students will apply these methods to naturally occurring phenomena, such as bacterial-population growth, tumour expansion and oscillating systems subject to forcing and friction, in order to explain their behaviour and seek solutions. The method of solution by Laplace transforms is also introduced.
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.
This module introduces the student to logic and mathematical proofs, with emphasis placed on proving general theorems than on performing calculations. This is because a result which can be applied to many different cases is clearly more powerful than a calculation that deals only with a single specific case.
We take a look at the language and structure of mathematical proofs in general, emphasising how logic can be used to express mathematical arguments in a concise and rigorous manner. These ideas are then applied to the study of number theory, establishing several fundamental results such as Bezout’s Theorem on highest common factors and the Fundamental Theorem of Arithmetic on prime factorisations.
The concept of congruence of integers is introduced to students and they study the idea that a highest common factor can be generalised from the integers to polynomials.
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.
Computer programming is a highly practical skill in our quickly developing world. In this module students develop the skills expected of a principled computer programmer as they learn how to write, analyse, debug, test and document computer programs. Students will be introduced to both the C and Java programming languages, two of the most widely used languages in the world. They will learn about best practice of day-to-day techniques associated with software development and gain an understanding of the software development cycle. Learning about the challenges faced by software developers in addressing scalability and complexity in computer software, students will be able to work independently to develop moderately complex computer programs.
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.
Core
SCC210 gives students the opportunity to build upon their skills and knowledge from Year 1 to create a real-world system in a group context. As part of a group, students will work effectively to gather system requirements, design and then implement the project in addition to accurately evaluating it. The results of the Group Project will be showcased to IBM, who award prizes for the best project every year. The course aims to increase theoretical knowledge and practical skills in prototyping, project planning, project management, management and execution, games design, systems design and testing strategies.
Throughout the Michaelmas and Lent terms, each group will receive 25 hours of contact time with their supervisor, whilst working together. By the end of the module, students will be able to successfully co-ordinate working together, skilfully resolving any problems or conflicts. Students will advance their abilities in writing reports, communication and presenting projects as part of a group. Groups will present their project to IBM in written, graphical and verbal forms.
Students will learn theoretical and practical topics in Human-Computer Interaction, with lab work offering hands on experience of design, implementation and the ability to evaluate interactive systems through practical case studies. The course explores the underpinnings of human perception, user-centred design and participatory design processes, with students learning multiple design techniques. The module leads to an understanding of how internal system design impacts external user interface behaviour and highlights the importance of accessibility for all users.
By the end of the module, students will be able to successfully integrate diverse information to form a coherent understanding of Human-Computer Interaction; critically reflect on technical advancements in HCI and demonstrate the independent learning abilities needed for continual professional development and effective written and verbal skills.
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.
Students will gain the essential skills and knowledge to operate within the professional, legal and ethical frameworks of their profession. Techniques for breaking down a project into manageable parts and efficient time allocation are taught, leading to a fundamental understanding of the skills and methods required to pursue scientific inquiry and the fundamental concepts and tools for statistical analysis to measure and explain data. Exemplars and guidelines on producing concise and structured scientific reports are offered and students receive additional lectures on presentation skills, professional ethics in relation to computing and communications. Finally, lectures provide an awareness of fundamental legal aspects related to a profession in computing and communications, including intellectual property rights and patent law.
Throughout this course, students will gain a high level of awareness of subject specific skills and general competence needed to gain employment in their field. The module develops academic writing and research skills in a computing context, complimenting students’ other modules.
Software Design offers the opportunity to gain an understanding of the importance of software architecture design, different styles of architecture and the meaning of quality attributes for software design such as maintainability, performance and scalability. Students will gain knowledge of systematic approaches to developing software design using a set of graphical models. The design process involved in developing several modes of the system at different levels of abstraction is explained and they will be introduced to object oriented design with UML.
Throughout the module, students will appreciate the broader context of the role of computer science in the workplace, and the key role it plays in implementing software. The course also looks at understanding the meaning of quality attributes for software design as well as architectural models for specific software systems. Students will gain an insight into the main quality attributes for deciding classes. Students will be able to interpret and construct UML models of software and implement a design expressed as a UML mode as well as understanding how to use various design patterns to address certain problems.
Optional
This module gives you the opportunity to enhance your project skills, including both subject-related and transferable skills. You’ll work on mathematical document preparation and presentation, scientific writing, and working with a statistical software package.
You’ll revisit LaTeX and R, and work on your oral communication skills, scientific writing, a written group project, and a group presentation.
This module builds on the binary operations studies in previous modules, such as addition or multiplication of numbers and composition of functions. Here you’ll select a small number of properties which these and other examples have in common, and use them to define a group.
You’ll also consider the elementary properties of groups. It turns out that several surprisingly elegant results can be proved fairly simply! By looking at maps between groups which 'preserve structure' you’ll discover a way of formalizing (and extending) the natural concept of what it means for two groups to be 'the same'.
Ring theory provides a framework for studying sets with two binary operations: addition and multiplication. This gives us a way to abstractly model various number systems, proving results that can be applied in many different situations, such as number theory and geometry. Familiar examples of rings include the integers, the integers modulation, the rational numbers, matrices and polynomials, but you’ll meet several less familiar examples too.
Complex Analysis has its origins in differential calculus and the study of polynomial equations.
In this module you’ll consider the differential calculus of functions of a single complex variable and study power series and mappings by complex functions. You’ll use integral calculus of complex functions to find elegant and important results, including the fundamental theorem of algebra, and you’ll also use classical theorems to evaluate real integrals.
The module ends with basic discussion of harmonic functions, which play a significant role in physics.
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.
In this module you’ll take a thorough look at the limits of sequences and convergence of series. You’ll learn to extend the notion of a limit to functions, leading to the analysis of differentiation, including proper proofs of techniques learned at A-level.
You’ll spend time studying the Intermediate Value Theorem and the Mean Value Theorem, and will discover that they have many applications of widely differing kinds. The next topic is new: sequences and series of functions (rather than just numbers), which again has many applications and is central to more advanced analysis.
Next we put the notion of integration under the microscope. Once it’s properly defined (via limits), you’ll learn how to get from this definition to the familiar technique of evaluating integrals by reverse differentiation. You’ll also explore some applications of integration that are quite different from the ones in A-level, such as estimations of discrete sums of series.
Further possible topics include Stirling's Formula, infinite products and Fourier series.
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.
Core
Students will gain an introduction to fundamental concepts in artificial intelligence and learn about current trends and issues. Topics such as Knowledge Representation and Reasoning, Decision Making (DM) and Decision Making Under Uncertainties, and Probability Theory are all explored throughout the course. Artificial Intelligence offers experience in supervised and unsupervised machine learning, neural networks and decision trees. Multivariate methods, and clustering and classification approaches are taught and there is an introduction to evolutionary algorithms, phenotypes, genotypes and fundamental genetic operators. Programming languages suitable for intelligent systems, such as Scheme and Prolog are investigated and students are made familiar with the applications of artificial intelligence.
This module sees an awareness of the requirements of artificial intelligence systems in general, and in the context of computing and communications systems. Through knowledge based, probabilistic and logical systems, the module provides students with an awareness of competing approaches and a broad grounding in artificial intelligence. Additionally they will understand and critically analyse artificial intelligence techniques used in modern computers and mobile devices.
Students taking this module will be exposed to a small number of current computer science related topics from different subject areas. These areas will be introduced through a lecture covering the general aspects of the area, however the main topical exposure will happen through seminar style in-depth study of specific parts of the subject area. This will include guided reading, a written seminar essay, presentation of the results in plenary and a discussion of the findings within the seminar group. Through this the students will further deepen their skills in carrying out research, present their work in written, graphical and verbal form (which is related to SCC205 Professional Skills & Research Methods) and to discuss the research work with experts and their own peers.
The subject areas contributing to the seminars will come from SCC’s different thematic strands and will include natural language engineering, policy based network resilience, eye-tracking for ubiquitous computing applications, and a focus on energy aware control and sensing in home environments. Students will conduct independent and in-depth research into an advanced topic of computing or communications reflecting current topical and research issues, and will be required to apply research skills to specific subject areas, present the results of their finding in front of a peer group and supervisor, as well as lead a guided discussion on their chosen subject area. The module aims to familiarise students with a variety of current and topical areas in computer science and communications that provide an additional and more in-depth perspective on subject areas taught throughout second and third year.
During the course of the module, students will be expected to research a complex technical topic related to their studies, including analysing, structuring, summarising, documenting and presenting their findings in front of a large group. They will gain topical knowledge and skills related to the subject areas of the seminars and will learn with and from their peers the specific topical areas of the seminars. The module will enable students to produce a coherent document describing their research findings on a complex technical or research topic, present technically intricate issues in a coherent manner, and discuss and defend their position on a specific topic within a seminar group.
Providing an introduction to formal languages, grammars, automata and how these concepts relate to programming in terms of compilers and the compilation process, students will learn about syntax and semantics, phrase structure grammars and the Chomsky Hierarchy as well as processes such as derivation and parsing. The module focuses on grammar equivalence and ambiguity in context free grammars and its implications. There is exploration of the relationship between languages and abstract machines. Students are presented with the concept of computation alongside Turing’s thesis, alternative models of computation and applications of abstract machine representations. There are further introductions to the compilation process including lexical analysis and syntactic analysis.
By the end of this module, students will understand the relation of programming languages and the theory of formal languages. They will possess an essential understanding of the compilation process for a high-level programming language. Students are encouraged to engage with theoretical aspects of computer science to compliment practical skills in other parts of their degree. There are links to other disciplines such as linguistics, and the course explains the challenges of compilation in the context of software development and computer science.
This module gives you the opportunity to enhance your project skills, including both subject-related and transferable skills. You’ll work on mathematical document preparation and presentation, scientific writing, and working with a statistical software package.
You’ll revisit LaTeX and R, and work on your oral communication skills, scientific writing, a written group project, and a group presentation.
Covering a range of topics, including asset identification and assessment, threat analysis and management tools and frameworks, students will become familiar with attack lifecycle and processes, as well as risk management and assessment processes, tools and frameworks. The module covers mitigation strategies and the most appropriate mitigation technologies and offers knowledge on assurance frameworks and disaster recovery planning. There is also an opportunity to learn about infrastructure design and implementation technologies and attack tree and software design evaluation.
Students will gain an understanding of the different ways in which an IT professional can make effective decisions when securing an IT infrastructure. The course will make them aware of the tools, frameworks and models that can be used to identify assets, threats and risks, before selecting the most appropriate strategies to manage the exposure that IT infrastructure faces in the light of this analysis. The module builds on their skills with a practical examination of the mechanisms by which IT infrastructures are attacked.
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.
Combinatorics is the core subject of discrete mathematics which refers to the study of mathematical structures that are discrete in nature rather than continuous (for example graphs, lattices, designs and codes). While combinatorics is a huge subject - with many important connections to other areas of modern mathematics - it is a very accessible one.
In this module you’ll be introduced to the fundamental topics of combinatorial enumeration (sophisticated counting methods), graph theory (graphs, networks and algorithms), and combinatorial design theory (Latin squares and block designs). You’ll also explore important practical applications of the results and methods.
This module considers questions relating to linear ordinary differential equations. While explicit solutions can only be found for special types of equations, some of the ideas of real analysis allow us to answer questions about the existence and uniqueness of solutions to more general equations as well as allowing us to study certain properties of these solutions.
This module gives you a solid foundation in the basics of algebraic geometry. You’ll explore how curves can be described by algebraic equations, and learn how to understand and use abstract groups in dealing with geometrical objects (curves).
You’ll also gain an understanding of the notions and the main results pertaining to elliptic curves, and the way that algebra and geometry are linked via polynomial equations. Finally you’ll learn to perform algebraic computations with elliptic curves.
This module is an introduction to smooth curves and surfaces in three-dimensional space. You’ll encounter various geometrical properties of these objects, such as length, area, torsion and curvature, and will have the opportunity to explore the meaning of these quantities. You’ll use a variety of examples to calculate their values, and will use them to apply techniques from calculus and linear algebra.
In this module you’ll develop the knowledge of groups that you’ve gained in second year. You’ll study ‘direct products’ which are used to construct new groups, while any finite group is shown to ‘factor’ into ‘simple’ pieces. You’ll also consider situations in which a group ‘acts’ on a set by permuting its elements; this powerful idea is used to identify the symmetries of the Platonic solids, and to help study the structure of groups themselves.
In this module you’ll examine the notion of a norm, which introduces a generalized notion of ‘distance’ to the purely algebraic setting of vector spaces. You’ll learn several quite natural ways to do this, both for vectors of any dimension and for functions. You’ll then focus on the more special notion of an inner product which generalizes angles at the same time as distances.
Once we’ve established these concepts, you’ll have the opportunity to study geometrical ideas like orthogonality, which can be seen to apply to much more general spaces than Euclidean spaces of three (or even n) dimensions, notably to infinite dimensional spaces of functions. For example, Hilbert space theory shows how Fourier series are really another case of expressing an element in terms of a basis, and how you can use orthogonality to find best approximations to a given function by functions of a prescribed type. Finally, you’ll look at some of the main results of linear algebra, which generalize very nicely to linear operators between Hilbert spaces.
Introducing the Lebesgue integral for functions on the real line, this module features a classical approach to the construction of Lebesgue measure on the line and to the definition of the integral. The bounded convergence theorem is used to prove the monotone and dominated convergence theorems, and the results are illustrated in classical convergence problems including Fourier integrals.
Among the range of topics addressed on this module, students will become familiar with Lebesgue's definition of the integral, and the integral of a step function. There will be an introduction to subsets of the real line, including open sets and countable sets. Students will measure of an open set, and will discover measurable sets and null sets. Additionally, the module will focus on integral functions, along with Lebesgue's integral of a bounded measurable function, his bounded convergence theorem and the integral of an unbounded function. Dominated convergence theorem; monotone convergence theorem.
Other topics on the module will include applications of the convergence theorems and Wallis's product for P. Gaussian integral, along with some classical limit inversion results and the Fourier cosine integral. Students will develop an understanding of Dirichlet's comb function, Archimedes' axiom and Cantor's uncountability theorem, and will learn to prove the structure theorem for open sets. In addition, students will be able to prove covering lemmas for open sets, as well as understanding the statement of Heine—Bore theorem, as well as understanding the concept and proving basic properties of outer measure. As well as understanding inner measure. Finally, students will be expected to prove Lebesgue's theorem on countable additivity of measure.
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 is designed to give you an opportunity to consider key issues in the teaching and learning of mathematics. Whilst it is an academic study of mathematics education and not a training course for teachers, it does provide an excellent foundation for a PGCE especially in preparing students to write academically.
Having studies Mathematics for many years, you are well-placed to reflect upon that experience and attempt to make sense of it in the light of theoretical frameworks developed by researchers in the field. Within this course we hope to help you with this process so that as a Mathematics graduate you will be able to contribute knowledgeably to future debate about the ways in which your subject is treated within the education system.
This module is run as a partnership between the Department of Mathematics and Statistics, the University of Cumbria and the Students’ Union’s volunteering unit.
Designed with employability in mind, this module is based on the Students’ Union’s Schools Partnership Scheme, which supports Lancaster students on 10-week placements in local primary and secondary schools.
You’ll have the opportunity to take part in classroom observation and assistance, the development of classroom resources, the provision of one-on-one or small group support and possibly even teaching sections of lessons to the class as a whole.
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 gives an introduction to the key concepts and methods of metric space theory, a core topic for pure mathematics and its applications. Studying this module will give you a deeper understanding of continuity as well as a basic grounding in abstract topology.
You’ll also gain a firm foundation for further study of many topics including geometry, Lie groups and Hilbert space, and learn to apply your knowledge to areas including probability theory, differential equations, mathematical quantum theory and the theory of fractals.
Number theory is the study of the fascinating properties of the natural number system.
Many numbers are special in some sense, eg. primes or squares. Which numbers can be expressed as the sum of two squares? What is special about the number 561? Are there short cuts to factorizing large numbers or determining whether they are prime (this is important in cryptography)? The number of divisors of an integer fluctuates wildly, but is there a good estimation of the ‘average’ number of divisors in some sense?
Questions like these are easy to ask, and to describe to the non-specialist, but vary hugely in the amount of work needed to answer them. An extreme example is Fermat’s last theorem, which is very simple to state, but was proved by Taylor and Wiles 300 years after it was first stated. To answer questions about the natural numbers, we sometimes use rational, real and complex numbers, as well as any ideas from algebra and analysis that help, including groups, integration, infinite series and even infinite products.
This module introduces some of the central ideas and problems of the subject, and some of the methods used to solve them, while constantly illustrating the results with exercises and examples involving actual numbers.
This module is ideal for students who want to develop an analytical and axiomatic approach to the theory of probabilities.
First you’ll examine the notion of a probability space through simple examples featuring both discrete and continuous sample spaces. You’ll then use random variables and the expectation to develop a probability calculus, which you can apply to achieve laws of large numbers for sums of independent random variables.
You’ll also use the characteristic function to study the distributions of sums of independent variables, which have applications to random walks and to statistical physics.
This module covers the basics of ordinary representation theory. You’ll learn the concepts of R-module and of group representations, the main results pertaining to group representations, and will handle basic applications in the study of finite groups. You’ll also learn to perform computations with representations and morphisms in a selection of finite groups.
This module furthers your knowledge of commutative rings from your second year study.
You’ll be introduced to two new classes of integral domains called Euclidean domains, where you have a counterpart of the division algorithm, and unique factorization domains, in which an analogue of the Fundamental Theorem of Arithmetic holds.
You’ll also explore how well-known concepts from the integers such as the highest common factor, the Euclidean algorithm, and factorization of polynomials, carry over to this new setting.
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.
Core
Students will gain a formal understanding of research and will develop the ability to critically reflect on research approaches and practices in the field of computing. Research Methods will also encourage an appreciation of the different ways that other disciplines, academic communities and industries all conduct research. There will be an opportunity to plan a research project and develop a convincing study design to address a challenge or problem. This module explores ethical and data management issues associated with research as well as research and innovation practices in industry.
The module covers the fundamentals of research such as sampling and design, before considering strategies and research methods. Furthermore, the module offers greater insight into research design, such as how to structure and frame research studies, choosing a research strategy and selecting the best research method. Students will learn about ethical issues in research and approval processes before understanding the opportunities and expectations from their industrial placements.
Optional
Explore advanced topics in experimental Human-Computer Interaction (HCI), such as understanding users and their requirements, investigating design spaces and prototyping and developing innovative interaction techniques. The module offers increased experience in HCI literature and design methods both with and without users, as well as practical experience of using supporting tools. Students will learn about modelling techniques and design space techniques as part of the module.
Upon completion of the module, students will have the knowledge to conduct experimental HCI research and have the motivation, experience and tools for understanding users and their requirements for interaction. The module helps to develop scientific writing skills and analytical thinking and prepares students for further postgraduate study, or for a successful career in IT or computing.
This module covers advanced topics in experimental Human-Computer Interaction (HCI) with an emphasis on experimental design, evaluation methodologies, statistical analysis and result interpretation. Whilst engaging with a number of key topics, students will be asked to explore the evaluation process. Students will learn to recognise when HCI is required and which forms of evaluation is necessary in a given situation, for example making appropriate selections from systems vs user and qualitative vs quantitative evaluations.
Practical sessions will enable students to develop skills with statistical analysis packages. They will also receive guidance on the application of appropriate tests and result reporting.
This module provides students with up-to-date information on current applications of data in both industry and research. Expanding on the module ‘Fundamentals of Data’, students will gain a more detailed level of understanding about how data is processed and applied on a large scale across a variety of different areas.
Students will develop knowledge in different areas of science and will recognise their relation to big data, in addition to understanding how large-scale challenges are being addressed with current state-of-the-art techniques. The module will provide recommendations on the Social Web and their roots in social network theory and analysis, in addition their adaption and extension to large-scale problems, by focusing on primer, user-generated content and crowd-sourced data, social networks (theories, analysis), recommendation (collaborative filtering, content recommendation challenges, and friend recommendation/link prediction).
On completion of this module, students will be able to create scalable solutions to problems involving data from the semantic, social and scientific web, in addition to abilities gained in processing networks and performing of network analysis in order to identify key factors in information flow.
In this module you’ll study topics related to the understanding of special models to describe the extreme values of a financial times series, and you’ll learn to fit appropriate extreme value models to data which are maxima or threshold exceedance. You’ll also learn to use extreme value models to evaluate Value at Risk and gain an understanding of the impact of heavy tailed data on standard statistical diagnostic tools.
Bayesian statistics is a framework for rational decision making using imperfect knowledge, expressed through probability distributions. Bayesian principles are applied in the fields of navigation, control, automation and artificial intelligence. The aim of decision makers is to make rational decisions that maximise some personal utility function which may represent quantities such as money which are related to the wealth of an individual.
Within the Bayesian framework, knowledge of the world, (the prior) is updated as fresh observations arrive to yield a posterior distribution which shows the revised knowledge. The evidence for the model is expressed by calculating a marginal likelihood. Future behaviour and the fit of the model are assessed using a predictive distribution. This includes sampling uncertainty and uncertainty of our knowledge.
In this module you’ll look at the posterior, the marginal and the predictive distributions for several one parameter conjugate models, and two families of multi-parameter fully conjugate models. You’ll extend the range of belief types that can be modelled by using mixtures of conjugate priors, and will also explore the use of non-conjugate formulations of models and use Monte-Carlo integration, importance sampling and rejection sampling for calculating and simulating from these distributions.
Clinical trials are planned experiments on human beings designed to assess the relative benefits of one or more forms of treatment. For instance, we might be interested in studying whether aspirin reduces the incidence of pregnancy-induced hypertension; or we may wish to assess whether a new immunosuppressive drug improves the survival rate of transplant recipients. Treatments may be procedural, for example, surgery or methods of care.
This module combines the study of technical methodology with discussion of more general research issues. First we’ll discuss the relative advantages and disadvantages of different types of medical studies. We’ll then explore the basic aspects of clinical trials as experimental designs, looking in particular at the definition and estimation of treatment effects. We’ll also cover cross-over trials, concepts of sample size determination, and equivalence trials. The module also includes a brief introduction to sequential trial designs and meta-analysis.
In this module you’ll be introduced to Markov chain Monte Carlo methods and how to use them as a powerful technique for performing Bayesian inference on complex stochastic models.
The first part of the module looks in detail at the necessary concepts and theory for finite state-space Markov chains, before introducing analogous concepts and theory for continuous state-space Markov chains. In the second part of the course you’ll investigate the Metropolis-Hastings algorithm for sampling from a distribution known up to a constant of proportionality.
In the third (and largest) part, you’ll take this knowledge and apply it to Bayesian inference as well as studying the Gibbs sampler. You’ll also examine the two most common Metropolis-Hastings algorithms (the random walk and the independence sampler). Examples will include hierarchical models, random effects models, and mixture models.
This module provides a comprehensive coverage of the problems related to data representation, manipulation and processing in terms of extracting information from data, including big data.
Practical laboratory sessions will offer students an opportunity to gain a fundamental theoretical level of knowledge and skills in computer science. Students will then apply their working understanding to the data primer, data processing and classification. The module will enhance students’ familiarity with dynamic data space partitioning, using evolving, clustering and data clouds, and monitoring the quality of the self-learning system online.
Further skills provided on this module include the ability to develop software scripts that implement advanced data representation and processing and demonstrate their impact on the performance, as well as a working knowledge in listing, explaining and generalising the trade-offs of performance and complexity in designing practical solutions for problems of data representation and processing in terms of storage, time and computing power. Transferrable skills include development in time management and effective report writing.
This module will equip students with the ability to develop and apply an understanding of fundamental principles, techniques and technologies that underpin today's global IT infrastructure. It is designed to be studied in combination with its module namesake, SCC.401-DS, in order to form the systems stream of the Computer Science MSc. Students will learn to assess new systems technologies, to know where technologies fit in a comprehensive schema, and to know what to read in order to develop a deeper level of understanding.
Students will focus on the properties of system components, and will become familiar with the strengths, weaknesses, scalability and bottlenecks of systems components, with the aim to make intelligent and well-reasoned trade-offs between fundamental building blocks of distributed systems in today’s IT infrastructure. The techniques and principles element of the module addresses caching, tiering, replication, synchronisation, failure and reliability, whilst the technologies section covers topics such as interaction paradigms in distributed systems, peer-to-peer architecture and scalable and high-performance networking.
In this module you’ll have the opportunity to learn the basics of algebraic geometry.
You’ll look at how curves can be described by algebraic equations, and will develop an understanding of abstract groups, learning how to use them to deal with geometrical objects (curves). You’ll also study the notions and the main results pertaining to elliptic curves, and will investigate the way that algebra and geometry are linked via polynomial equations, performing algebraic computations with elliptic curves.
This module focuses on the kinds of statistical methods commonly used by statisticians to investigate the relationship between risk of disease and environmental factors.
You’ll explore methods for the analysis of spatial data, including spatial point-process models, spatial case-control methods, spatially aggregated data, point source problems and geostatistics, and will spend time developing your skills performing similar analyses using the statistical package R.
The Fourth Year Project will focus on a significant specification, design, implementation and/or evaluation project at the suitable level for MSci Computer Science. The project sees students tackle a real-world problem by applying their knowledge in computer science. The project is usually achieved in conjunction with an industry placement; however it can be completed at the University. Suggestions made by industry will be vetted by a team of academics to ensure appropriate depth, and if no suitable project with industry can be found, one will be provided by academic staff.
Weekly guidance is given from a member of academic staff from the department to ensure the necessary level of academic content and rigour is being maintained. There are also Business Development mentors in the KBC to provide students with an insight in the day to day expectations and responsibilities of working with industry. The Fourth Year Project is designed to challenge students and develop their existing knowledge, understanding and skills from their undergraduate degree to produce a significant piece of academically rigorous project work.
Galois Theory is, in essence, the systematic study of properties of roots of polynomials. Starting with such a polynomial f over a field k (e.g. the rational numbers), one associates a ‘smallest possible’ field L containing k and the roots of f; and a finite group G which describes certain ‘allowed’ permutations of the roots of f. The Fundamental Theorem of Galois Theory says that under the right conditions, the fields which lie between k and L are in 1-to-1 correspondence with the subgroups of G.
In this module you’ll see two applications of the Fundamental Theorem. The first is the proof that in general a polynomial of degree 5 or higher cannot be solved via a formula in the way that quadratic polynomials can; the second is the fact that an angle cannot be trisected using only a ruler and compasses. These two applications are among the most celebrated results in the history of mathematics.
In this module you’ll learn techniques for formulating sensible models for data, enabling you to tackle problems such as the probability of success for a particular treatment, and how this depends on the patient's age, weight, blood pressure, and so on.
You’ll be introduced to a large family of models, called the generalised linear models (GLMs), including the standard linear regression model as a special case, and will have the opportunity to discuss and investigate the theoretical properties of these models.
You’ll also study a common algorithm called iteratively reweighted least squares algorithm for the estimation of parameters. Using the statistical package ‘R’, you’ll fit and check these models, and will produce confidence intervals and tests corresponding to questions of interest.
In this module you’ll have the opportunity to learn about Hilbert space, consolidating your understanding of linear algebra and enabling you to study applications of Hilbert space such as quantum mechanics and stochastic processes.
You’ll learn how to use inner products in analytical calculations, to use the concept of an operator on an infinite dimensional Hilbert space, to recognise situations in which Hilbert space methods are applicable and to understand concepts of linear algebra and analysis that apply in infinite dimensional vector spaces.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
Students complete a 10 week industrial placement in the Lent term of their 4th year. The University has a range of businesses from SMEs to large corporates for students to be placed in. There are no taught elements in this module, but students have access to an academic supervisor to guide and assist them during the placement. Placements are assigned to students in the Michaelmas term.
Students will gain first-hand experience of working in a contemporary ICT related environment, developing an appreciation and understanding of professional practices and codes of conduct in industrial, commercial and professional settings. Companies will set tasks that are related to students’ knowledge and experience gained throughout their degree, allowing them to apply it in a professional setting. Placements are offered by a variety of companies with different topics. Through an initial matching and application process, we ensure the biggest possible overlap between student interest and company requirements.
In this module you’ll construct Lebesgue measure on the line, extending the idea of the length of an interval. You’ll use this to define an integral which is shown to have good properties under pointwise convergence. Looking at some basic results about the set of real numbers, you will explore properties of countable sets, open sets and algebraic numbers.
You’ll also have the opportunity to illustrate the power of the convergence theorems in applications to some classical limit problems and analysis of Fourier integrals, which are fundamental to probability theory and differential equations.
In this module you'll be introduced to the theory of Lie groups and Lie algebras. You'll also explore the relationship between the two, and will develop an understanding of the way that this forms an important and enduring part of modern mathematics and a great number of fields including theoretical physics. You'll learn to appreciate the subtle and pervasive interplay between algebra and geometry, and to appreciate the unified nature of mathematics. The abstract nature of the course will give you a taste of modern research in pure mathematics.
In this module you’ll learn how to use the likelihood function to obtain and summarise information about unknown parameters. You’ll calculate the likelihood function for statistical models which do not assume independent identically distributed data, and will learn to evaluate point estimates and make statements about the variability of these estimates.
Using the statistical package ‘R’, you’ll use computational methods to calculate maximum likelihood estimates. You’ll spend time developing an understanding of the inter-relationships between parameters, and the concept of orthogonality, and will perform hypothesis tests using the generalised likelihood ratio statistic.
Longitudinal data arise when a time-sequence of measurements is made on a response variable for each of a number of subjects in an experiment or observational study. For example, a patient’s blood pressure may be measured daily following administration of one of several medical treatments for hypertension.
Typically, the practical objective of most longitudinal studies is to find out how the average value of the response varies over time, and how this average response profile is affected by different experimental treatments. This module presents an approach to the analysis of longitudinal data, based on statistical modelling and likelihood methods of parameter estimation and hypothesis testing.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
In this module you’ll construct Lebesgue measure on the line, extending the idea of the length of an interval. You’ll use this to define an integral which is shown to have good properties under pointwise convergence. Looking at some basic results about the set of real numbers, you will explore properties of countable sets, open sets and algebraic numbers.
You’ll also have the opportunity to illustrate the power of the convergence theorems in applications to some classical limit problems and analysis of Fourier integrals, which are fundamental to probability theory and differential equations.
Operator theory is a modern mathematical topic in analysis which provides powerful general methods for the analysis of linear problems, and possibly even infinite dimensional problems.
Early successes were in the solution of differential and integral equations. Now operator theory is also an extensive subject in its own right in the general area of functional analysis.
First you’ll review Hilbert spaces, before spending some time studying infinite-dimensional operators, notably the unilateral shift and multiplication operators, as well as basic concepts. You’ll then consider the criteria for invertibility of self adjoint operators, leading to the spectral theory of such operators.
This module focuses on the basic principles of epidemiology, including its methodology and application to prevention and control of disease.
You’ll examine the concepts and strategies used in epidemiologic studies, and will develop an understanding of the role of epidemiology in preventive medicine and disease investigation. You’ll also develop your knowledge of basic epidemiologic methods and how to apply them, and will develop confidence in assessing the validity of epidemiologic studies with respect to their design and inferences.
The aim of this course is to develop an analytical and axiomatic approach to the theory of probabilities.
You’ll consider the notion of a probability space, illustrated by simple examples featuring both discrete and continuous sample spaces. You’ll then use random variables and the expectation to develop a probability calculus, which is applied to achieve laws of large numbers for sums of independent random variables. You’ll also use the characteristic function to study the distributions of sums of independent variables, applying the results to random walks and to statistical physics.
In this module you’ll learn the basics of ordinary representation theory.
You’ll have the opportunity to explore the concepts of R-module and group representations, and the main results pertaining to group representations, as well as learning to handle basic applications in the study of finite groups. You’ll also develop your skills in performing computations with representations and morphisms in a selection of finite groups.
At the end of Year 3 you’ll fill in a form stating your mathematical or statistical interests and based on that you will be assigned a dissertation supervisor (a member of staff) and a topic. The dissertation may be in mathematics (MATH491), statistics (MATH492), or on an industrial project (MATH493), which is in cooperation with an external industrial partner. This depends on your degree scheme and your choice.
During the first term you’ll meet your supervisor weekly and will be guided into your in-depth study of a specific topic. During the second term you’ll have to produce a written dissertation on what you have learned and give an oral presentation. You will hand in your dissertation in the first week after the Easter recess. The grade is based 70% on your final written product, 10% on your oral presentation, and 20% on the initiative and effort that you demonstrated during the entire two terms of the module.
Further information is available from the Year 4 Director of Studies and will be communicated to every Year 4 student at the beginning of Term 1.
This module shows how the rules of probability can be used to formulate simple models describing processes, such as the length of a queue, which can change in a random manner, and how the properties of the processes, such as the mean queue size, can be deduced.
In Stochastic Processes you’ll learn how to use conditioning arguments and the reflection principle to calculate probabilities and expectations of random variables. You’ll also learn to calculate the distribution of a Markov Process at different time points and to calculate expected hitting times, as well as how to determine whether a Markov process has an asymptotic distribution and how to calculate it. You’ll then develop an understanding of how stochastic processes are used as models.
Introducing a range of architectural approaches, techniques and technologies that underpin today’s global IT infrastructure, this module combines with other modules to form the systems stream of the programme. It is designed to enhance students’ knowledge of how building blocks are composed to create systems of systems.
Students will gain a detailed understanding and an ability to critique contemporary systems’ architecture in terms of scalability, resilience, performance and other shortcomings.
The principal ethos of this module is to focus on the principles, emergent properties and the application of systems elements as used in large-scale and high performance systems. Detailed studies and invited industrial speakers will be used to provide supporting real-world context and a basis for seminar discussions. Students are also offered ‘hands-on’ measurement-based coursework that focuses on the scalability of a significant technology.
Fractals, roughly speaking, are strange and exotic sets in the plane (and in higher dimensions) which are often generated as limits of quite simple repeated procedures. The 'middle thirds Cantor set' in [0,1] is one such set. Another, the Sierpinski sieve, arises by repeated removal of diminishing internal triangles from a solid equilateral triangle.
This analysis module will explore a variety of fractals, partly for fun for their own sake but also to illustrate fundamental ideas of metric spaces, compactness, disconnectedness and fractal dimension. The discussion will be kept at a straightforward level and you’ll consider topological ideas of open and closed sets in the setting of R2.
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.
Lancaster’s Computer Science graduates have the skills required to work in a professional software and systems development environment. Our Mathematics graduates are valued by employers recruiting for a range of positions in technology, computing, financial services or management.
Combining both disciplines with transferable skills such as communication and teamwork, you will be prepared for a wide variety of positions in all sectors – from business, government, health and education, among others.
The Department of Mathematics and Statistics organises talks throughout the year on suitable careers, which help to inform students about potential career options and create useful links with employers.
Many graduates also choose to go on to further study and you’ll find Lancaster offers excellent graduate opportunities on Masters and PhD programmes.
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with the relevant life and work based skills. We are unique in that every student is eligible to participate in The Lancaster Award which offers you the opportunity to complete key activities such as work experience, employability/career development, campus community and social development. Visit our Employability section for full details.
We set our fees on an annual basis and the 2018/19 entry fees have not yet been set.
As a guide, our fees in 2017 were:
UK/EU | Overseas |
---|---|
£9,250 | £18,168 |
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. 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:
Continuation of the Access Scholarship is subject to satisfactory academic progression.
Students may be eligible for both the Academic and Access Scholarship if they meet the requirements for both.
Bursaries for life, living and learning:
Students from the UK eligible for a bursary package will also be awarded our Academic Scholarship and/or Access Scholarship if they meet the criteria detailed above.
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
Please note that this information relates to the funding arrangements for 2017, which may change for 2018.
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.