Mathematics and Statistics BSc
A*A*A at A level, A* A* in Mathematics and Further Mathematics Or A*AA to include A* A; graduates earn a median £39,500 three years on.
Typical A level offer
A*A*A
A* A* in Mathematics and Further Mathematics Or A*AA to include A* A · 95% of entrants came with A levels · 89% stay on after year 1 · Warwick course page, Discover Uni
Year 13 · applying
What do you need to get in?
A*A*Atypical A level offer; the typical offer can be met by achieving any one of the following: a*a*a to include a* a* in mathematics and further mathematics or a*aa to include a* a (in any order) in mathematics and further math
Typical offer — the grades most applicants are asked for. Contextual offer: A*AB, for applicants from under-represented backgrounds.
Warwick course page, read 19 September 2026 · Discover Uni entry profile
- 1A* A* in Mathematics and Further Mathematics Or A*AA to include A* A
- 2A* A
- 3Useful subjects: A* in Mathematics and a in AS Further Mathematics Or A*AA to include A* in Mathematics and one of the following: STEP
Who actually got in: 95% with A levels. 45% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 50 entrants.
UCAS tariff points — a points scale for grades: A* = 56, A = 48, so A*A*A = 160. Entrants’ totals include extra subjects.
Other qualifications4
| Qualification | Typical offer | Required subjects |
|---|---|---|
| IB | 39 pointsThe typical offer can be met by achieving any ONE of the following: 39 overall to include 7 in Higher Level Mathematics 'Analysis and Approaches' Or 38 overall to include 6 in Higher Level Mathematics | 6 in Higher Level Mathematics 'Analysis and Approaches' and one of the following: STEP; 6 in Higher Level Mathematics 'Applications and Interpretations' and one of the following: STEP |
| BTEC | —Level 3 BTECs will be considered alongside two A Levels including A Level Mathematics. | — |
| Advanced Highers | A1 in Advanced Higher Mathematics, A in one additional subject at Advanced HigheA1 in Advanced Higher Mathematics, A in one additional subject at Advanced Higher, and AAA in three additional Highers subjects. | — |
| Access | 45 Credits at Level 3, including Distinction in 33 Level 3 credits and Merit in We will consider applicants returning to study who are presenting a QAA-recognised Access to Higher Education Diploma on a case-by-case basis. Typically, we require 45 Credits at Level 3, including Di | — |
Years 1–3
What do you study?
8compulsory modules in year 1, 8 optional
Compulsory modules this year: mathematics & statistics 93% · transversal skills 5% · digital & ict skills 2%.
Module — one course unit, usually a term or a year long; “core” means everyone takes it. One UK credit is about ten hours of study; a full year is 120 credits.
Warwick course page · optional modules vary by year
Year 1 · 8 core, 8 optional · 90 credits ≈ 900 hours
- Sets and Numbers
What you learn (4)
- apply properties of standard number systems
- use set notation and quantifiers accurately
- describe functions and relations using formal notation
- apply different proof techniques to mathematical statements
- Mathematical Analysis 1
What you learn (7)
- describe the structure and properties of the real number system
- explain the concept of infinity and its use in mathematical analysis
- identify limits of sequences and series
- distinguish between conditional and absolute convergence of series
- apply convergence tests to determine series behaviour
- describe the properties of continuous functions
- explain the characteristics of absolutely continuous functions
- Linear Algebra
What you learn (13)
- define vector spaces and their properties
- determine whether a set of vectors is linearly independent
- identify a basis and calculate the dimension of a vector space
- explain the concept of a linear transformation
- apply linear transformations to vectors and spaces
- perform matrix manipulations using row and column operations
- apply matrix reduction techniques to solve systems of linear equations
- calculate determinants of n by n matrices
- compute cofactors and adjoint matrices
- explain how determinants and adjoints relate to solving linear systems
- compute eigenvalues and eigenvectors of a matrix
- interpret the geometric meaning of eigenvalues and eigenvectors
- describe linear transformations between euclidean spaces
- Mathematical Analysis 2
What you learn (10)
- determine whether a function is differentiable at a point
- compute higher-order derivatives of a function
- explain key properties of differentiable functions
- construct the Taylor series of a given function
- apply Taylor's theorem to estimate approximation error
- determine when a function can be represented by a power series
- construct the Riemann integral from first principles
- explain the theoretical basis of the Riemann integral's construction
- explain conditions guaranteeing integrability of continuous functions
- apply the Fundamental Theorem of Calculus to evaluate integrals
- Refresher Mathematics
What you learn (4)
- apply core A-level mathematical techniques to solve problems
- apply selected further mathematics concepts in problem solving
- explain the structure of mathematical definitions, theorems and proofs
- construct logical mathematical proofs using standard methods
- Introduction to Statistical Modelling
What you learn (14)
- formulate a probabilistic model for a given dataset
- derive a likelihood function corresponding to a simple model
- compute parameter estimates from observed data using a model
- quantify the uncertainty associated with estimated parameters
- compare predicted and observed data using graphical methods
- explain the assumptions underlying a statistical model
- interpret statistical model output to guide decisions
- write scripts in R to perform data analysis tasks
- produce data visualisations using the r environment
- discuss ethical issues in data collection and analysis
- evaluate ethical implications of communicating statistical results
- collaborate with peers to integrate diverse ideas into a shared task
- divide a complex task into manageable components for a team
- critique contributions from team members constructively
- Probability 1
What you learn (8)
- interpret standard mathematical notation
- apply problem solving techniques to mathematical tasks
- use visualisation methods to support mathematical reasoning
- construct concise and rigorous mathematical arguments
- calculate probabilities using appropriate methods
- interpret results of probability calculations
- explain key concepts of random variables
- interpret properties of probability distributions
- Probability 2
What you learn (5)
- calculate probabilities for random variables
- compute expectations of random variables
- explain fundamental concepts of probability theory
- explain theoretical concepts underlying probability distributions
- describe how randomness is incorporated into mathematical models
8 optional modules
- Design of Information Structures
What you learn (9)
- identify standard abstract data types used in programming
- describe how standard ADTs support common programming tasks
- assess the time and space complexity of simple algorithms
- evaluate the correctness of simple algorithms
- select appropriate algorithms to solve simple problems
- design user-defined abstract data types for a specific application
- develop algorithms associated with custom data structures
- apply software engineering principles within a larger software project
- use data structures as the foundation for software project design
- Introduction to Economics
What you learn (7)
- describe key economic behaviours and outcomes
- identify economic institutions and policies
- apply theoretical models to simplify economic problems
- explain core economic concepts and theories
- interpret economic data to identify trends and patterns
- select appropriate analytical tools based on data interpretation
- propose recommendations to support economic policy formulation
- Mathematical Programming 1
What you learn (8)
- identify business problems suited to optimisation modelling
- formulate a mathematical model representing a business optimisation problem
- solve mathematical optimisation problems using appropriate techniques
- list the underlying assumptions of key optimisation models
- critique the validity of assumptions made in optimisation models
- evaluate the limitations of different optimisation models
- explain the business implications of an optimal solution
- communicate quantitative results clearly to a business audience
- Programming for Scientists
What you learn (5)
- describe stages of software development from problem definition to tested code
- write programs in a high-level language using imperative constructs
- organise complex programs using encapsulation and abstraction techniques
- justify correctness of algorithms using informal reasoning arguments
- justify termination properties of a given program
- Differential Equations
What you learn (5)
- solve first order differential equations using standard methods
- solve linear second order differential equations
- solve coupled systems of first order differential equations
- solve simple difference equations using standard techniques
- interpret the qualitative behaviour of solutions to difference equations
- Mathematical Methods and Modelling 2
What you learn (7)
- describe scalar-valued and vector-valued functions of several variables
- apply coordinate transformations to mathematical problems
- explain the relationships between line, surface and volume integrals
- evaluate line, surface and volume integrals using integral theorems
- describe the Riemann integral of a multivariable function and its geometric meaning
- calculate integrals of functions over simple regions
- justify changes to area and volume elements under coordinate conversion
- Mathematical Methods and Modelling 1
What you learn (11)
- describe the stages of the modelling cycle
- formulate a simple mathematical model from a given problem
- analyse a simple mathematical model to assess its behaviour
- apply scaling and non-dimensionalisation to simplify a model
- apply linear stability analysis to identify dominant dynamics
- explain how underlying factors drive a system's dynamics
- solve first and second order ordinary differential equations
- interpret the qualitative behaviour of ode solutions
- solve simple difference equations
- relate difference equations to their continuous ode counterparts
- describe basic methods of numerical approximation
- Logic 1: Introduction to Symbolic Logic
What you learn (6)
- define what makes an argument logically valid
- distinguish methods for determining argument validity or invalidity
- formulate and assess sentences in propositional and first-order logic
- apply truth tables to evaluate argument validity
- construct formal proofs to establish argument validity
- devise counterexamples to disprove invalid arguments
Year 2 · 6 core, 19 optional · 60 credits ≈ 600 hours
- Mathematical Analysis 3
What you learn (7)
- compute contour integrals using cauchy's integral formulas
- apply cauchy's integral formulas to solve related problems
- distinguish uniform convergence from pointwise convergence of functions
- describe properties of the limit function of a convergent sequence
- apply the cauchy-riemann equations to test complex differentiability
- manipulate complex power series to represent analytic functions
- represent derivatives of multivariable functions as linear maps
- Stochastic Processes
What you learn (7)
- describe the concept of a markov chain
- apply conditional probability and matrix methods to discrete-time markov chains
- analyse long-run behaviour of markov chains including transience and recurrence
- determine equilibrium distributions of markov chains
- apply markov chain concepts to solve basic applied problems
- evaluate the suitability of markov chains as models for given situations
- present mathematical solutions using clear and logical argumentation
- Mathematical Methods for Statistics and Probability
What you learn (7)
- evaluate multiple integrals using suitable mathematical techniques
- solve optimisation problems by selecting appropriate methods
- examine properties of matrices and vector spaces
- apply spectral theory to identify structural features of matrices
- compare strategies for solving mathematical and probabilistic problems
- assess the effectiveness of methods used in multidimensional problems
- present mathematical solutions using clear and logical reasoning
- Probability for Mathematical Statistics
What you learn (7)
- derive properties of averages of random variables
- explain the significance of the central limit theorem in statistics
- describe joint and conditional probability distributions
- calculate conditional expectation and conditional variance
- derive properties of the multivariate normal distribution
- apply properties of distributions related to the normal distribution
- present mathematical solutions using clear and logical arguments
- Mathematical Statistics
What you learn (9)
- describe core concepts of statistical inference such as models, estimators and tests
- explain the uses and limitations of statistical inference techniques
- calculate maximum likelihood estimators for given statistical models
- apply likelihood ratios to compare competing statistical models
- construct hypothesis tests using likelihood ratio methods
- derive properties of sampling distributions for various estimators
- evaluate models using frequentist model selection criteria
- evaluate models using Bayesian model selection criteria
- present mathematical solutions using clear and logically structured arguments
- Linear Statistical Modelling with R
What you learn (11)
- define a normal linear model
- describe the assumptions underlying linear models
- derive statistical properties of estimators in linear models
- calculate confidence intervals for linear model parameters
- conduct hypothesis tests on linear model parameters
- fit regression models using R software
- check diagnostic adequacy of a fitted regression model
- improve and compare alternative regression models
- interpret the output and parameters of linear models
- evaluate the appropriateness of different linear models
- present statistical solutions using clear structured arguments
19 optional modules
- Programming for Scientists
What you learn (5)
- describe stages of software development from problem definition to tested code
- write programs in a high-level language using imperative constructs
- organise complex programs using encapsulation and abstraction techniques
- justify correctness of algorithms using informal reasoning arguments
- justify termination properties of a given program
- Metric Spaces
What you learn (11)
- define key properties of normed, metric and topological spaces
- apply theorems about metric and topological spaces to solve problems
- perform calculations involving distances and norms in metric spaces
- compare the open-set definition of continuity with other definitions
- apply continuity concepts to analyse open and closed sets
- describe fundamental concepts of compactness, connectedness and completeness
- apply theorems relating to compactness, connectedness and completeness
- perform calculations involving compactness, connectedness and completeness
- analyse relationships between compactness, connectedness and completeness
- evaluate how continuous maps preserve compactness, connectedness and completeness
- apply compactness, connectedness and completeness concepts to simple problems
- Modelling Nature's Nonlinearity
What you learn (4)
- demonstrate sensitivity of solutions to small changes in initial conditions or parameters
- explain how nonlinearity accounts for natural phenomena using mathematical models
- analyse nonlinear systems using analytical methods
- analyse nonlinear systems using numerical methods
- Combinatorics
What you learn (7)
- solve novel combinatorial problems using learned methods
- describe core definitions of enumerative combinatorics
- describe core definitions and theorems of graph theory
- describe core definitions and theorems of ramsey theory
- construct bijective proofs for numerical identities
- apply module concepts to analyse unfamiliar combinatorial structures
- adapt proof techniques from the module to new mathematical contexts
- Geometry
What you learn (8)
- perform analytic and algebraic calculations in euclidean geometry
- describe the geometric structure of the sphere
- describe the geometric structure of the hyperbolic plane
- compare metric properties of different geometric systems
- compare trigonometric relations across different geometries
- compare the behaviour of parallel lines across different geometries
- analyse abstract properties of lines and their incidence relations
- relate incidence properties to affine and projective geometry concepts
- Combinatorial Optimisation
What you learn (4)
- apply combinatorial structures to solve optimisation problems
- apply algorithmic techniques to solve specific combinatorial problems
- prove key results from combinatorial optimisation theory
- adapt optimisation methods to new combinatorial contexts
- Theory of ODEs
What you learn (7)
- identify conditions for existence and uniqueness of ODE solutions
- draw phase portraits for two-dimensional ODE systems
- classify critical points and trajectories of planar systems
- categorise orbit types and qualitative behaviours of non-linear odes
- describe solution behaviour near a critical point
- apply linearization techniques to non-linear differential equations
- apply ode methods to model physical or biological systems
- Introduction to Mathematical Biology
What you learn (5)
- construct simple models of biological phenomena from first principles
- analyse simple biological models using mathematical techniques
- derive biologically meaningful conclusions from mathematical analysis
- reconstruct standard models and key results for various biological systems
- describe the biological background of the systems studied
- Introduction to Number Theory
What you learn (5)
- decompose an integer into its prime factors
- use prime factorisations to deduce properties of integers
- solve linear and simultaneous congruence equations
- assess whether a number is a quadratic residue modulo another integer
- apply geometry of numbers techniques to solve Diophantine equations
- Multivariable Calculus
What you learn (10)
- define key concepts of functions of several variables
- explain core theorems of multivariable analysis
- perform calculations involving multivariable functions
- explain the Implicit Function Theorem and its conditions
- explain the Inverse Function Theorem and its conditions
- apply these theorems to solve multivariable problems
- describe properties and operations of vector fields
- apply the integral theorems of vector calculus to solve problems
- construct Taylor expansions for multivariable functions
- classify critical points using second order analysis
- Multilinear Algebra
What you learn (7)
- compute the jordan canonical form of a matrix
- explain the theoretical basis of jordan canonical form
- define functions of matrices
- calculate functions of matrices
- apply properties of bilinear forms to solve problems
- explain the concept of a tensor
- manipulate tensors using standard operations
- Groups and Rings
What you learn (4)
- describe core constructions in group theory
- describe core constructions in ring theory
- classify groups and rings according to given properties
- identify different types of ring from their defining properties
- Asymptotics and Integral Transforms
What you learn (13)
- describe the formal definition of an asymptotic series
- explain typical applications of asymptotic series
- identify regular and singular perturbation problems
- construct asymptotic series for algebraic equations using standard methods
- construct asymptotic approximations for integrals using standard methods
- construct asymptotic series for differential equations using standard methods
- apply matched asymptotic expansions to complex perturbation problems
- apply contour deformation to evaluate asymptotic integrals
- describe properties of fourier transforms
- apply fourier transform properties to solve differential and integral equations
- describe properties of laplace transforms
- apply laplace transform properties to solve differential and integral equations
- compare laplace and fourier transforms
- Numerical Methods and Computing
What you learn (6)
- derive fundamental numerical methods from mathematical principles
- analyse the accuracy and stability of numerical methods
- implement numerical algorithms using a scripting language
- test numerical implementations for correctness and performance
- design problem solving strategies for practical numerical problems
- evaluate the effectiveness of chosen solution strategies
- Games and Decisions
What you learn (7)
- compare mathematical foundations of alternative probability theories
- analyse philosophical basis of different probability approaches
- describe key characteristics of normative decision theory
- analyse decision-making models applied to real-world examples
- apply game theory to solve simplified mathematical games
- evaluate limitations of game theory in real-world scenarios
- present problem solutions using clear structured arguments
- Finance & Financial Reporting
What you learn (14)
- describe key principles underlying corporate governance
- explain the role of governance in corporate finance decisions
- prepare a statement of profit or loss for an organisation
- prepare a statement of financial position for an organisation
- prepare a cashflow statement incorporating basic tax principles
- analyse financial statements to identify key trends and issues
- draw conclusions from financial data for different stakeholder groups
- evaluate capital investment projects using appraisal techniques
- recommend an investment decision based on appraisal results
- describe the organisational structure of a company
- identify financial instruments used to raise finance and manage risk
- analyse how cost of capital relates to investment project characteristics
- analyse factors influencing a company's capital structure choices
- analyse factors influencing a company's dividend policy decisions
- Python for data-analytic tasks
What you learn (11)
- design a python program to solve a given problem
- write readable and valid python code
- build modular code structures for reuse
- verify code produces reliable results
- use python tools to manage and store data
- create data visualisations using python libraries
- apply python techniques to analyse datasets
- apply python techniques to solve optimisation problems
- document python code clearly for others to use
- produce reproducible outputs as python modules or packages
- collaborate with others to share coding work
- Visualisation and Communication of Data
What you learn (4)
- evaluate the quality and effectiveness of existing data visualisations
- produce improved versions of poorly designed visualisations using R
- select suitable visualisation types for specific data and tasks
- design original visualisations in R based on a dataset and specification
Year 3 · 0 core, 18 optional
18 optional modules
- Measure Theory
What you learn (4)
- explain the foundational concepts of abstract measure theory
- construct Lebesgue measure on the real line
- construct Lebesgue measure in n-dimensional Euclidean space
- outline advanced topics within measure theory
- Markov Processes and Percolation Theory
What you learn (3)
- explain core concepts of markov processes and percolation theory
- apply standard techniques used in stochastic process analysis
- conduct independent research into a mathematical topic
- Probability in Many Variables
What you learn (8)
- identify standard methods for bounding tail probabilities of random variables
- explain typical applications of tail-bounding techniques
- define Shannon entropy for discrete random variables
- apply entropy concepts to coding theorems
- apply Bernstein's and McDiarmid's inequalities to bound deviations
- derive the Poincare inequality for Gaussian random vectors
- apply exponential concentration bounds to Lipschitz functions of Gaussian vectors
- explain how Gaussian concentration supports slicing results for high-dimensional norms
- Mathematics of Machine Learning
What you learn (5)
- identify a suitable optimisation and modelling approach for a machine learning problem
- analyse optimisation algorithms by computational time and space complexity
- evaluate optimisation algorithms in terms of statistical accuracy
- implement a basic neural network architecture
- apply a neural network to a pattern recognition task
- Bayesian Statistics and Decision Theory
What you learn (11)
- describe the construction and evaluation process for Bayesian models
- explain naive Bayes model assumptions and calibration issues
- perform prior to posterior updating for basic statistical models
- apply discrete prior to posterior inference to simple problems
- carry out beta and Dirichlet conjugate analysis
- explain the theoretical basis of utility theory
- apply utility theory to multi-attribute decision problems
- elicit a utility function from decision-maker preferences
- model complex systems using conditional independence structures
- explain how directed acyclic graphs structure probabilistic models
- estimate probabilities in DAGs using product Dirichlet conjugate distributions
- Designed Experiments
What you learn (8)
- differentiate between types of experimental design
- judge the efficiency and usefulness of a given design
- explain the fundamental concepts underlying designed experiments
- construct a design matrix for a simple experiment
- calculate parameter estimates from an experimental design matrix
- carry out an analysis of variance for standard experimental designs
- apply diagnostic checks to results of a designed experiment
- formulate an optimal or robust solution to a practical design problem
- Probability Theory
What you learn (8)
- identify different modes of convergence for sequences of random variables
- explain the relationships between different modes of stochastic convergence
- prove the Central Limit Theorem using characteristic functions
- apply the Central Limit Theorem to analyse practical problems
- describe basic properties of discrete-time martingales
- derive characteristics of simple random walks using martingale results
- explain concepts of independence and zero-one laws in probability
- apply zero-one laws to solve simple probability problems
- Multivariate Statistics
What you learn (12)
- construct graphical displays of multivariate datasets
- interpret graphical displays to describe multivariate data patterns
- apply principal component analysis to reduce data dimensionality
- summarise high-dimensional datasets using derived components
- perform cluster analysis on multivariate data
- characterise identified subgroups within a population
- apply classification methods to assign individuals to groups
- evaluate discrimination techniques for group assignment accuracy
- conduct statistical inference for multivariate mean vectors
- construct confidence regions for multivariate parameters
- explain the use of confidence regions in comparing groups
- describe advanced multivariate statistical techniques covered in lectures
- Medical Statistics
What you learn (5)
- describe the contribution of statistical methods to medical research
- identify principles of good statistical practice in clinical studies
- apply generalised linear models to analyse medical data
- apply survival analysis techniques to medical data
- interpret results from generalised linear models and survival analysis
- Applied Stochastic Processes
What you learn (5)
- formulate continuous-time Markov chain models for real-world problems
- apply fundamental theory to derive key properties of Markov chains
- calculate transition probabilities for finite-state Markov chains
- apply Markov chain methods to model queueing systems
- apply Markov chain methods to model epidemic spread
- Bayesian Forecasting and Intervention
What you learn (7)
- explain recurrence relations used in forecasting models
- calculate forecasting recurrences for specific special cases
- select a suitable forecasting model for a given simple scenario
- describe key dynamic linear models used for business forecasting
- apply intervention techniques to adjust forecasts using external information
- describe diagnostic methods for assessing dynamic linear models
- explain estimation techniques used within this model class
- Introduction to Mathematical Finance
What you learn (8)
- solve mean-variance portfolio optimisation problems
- describe the capital market line concept
- describe preference orders used by financial investors
- solve simple utility maximisation problems
- explain monetary measures of financial risk
- calculate value at risk for a given distribution
- calculate expected shortfall for a given distribution
- describe self-financing strategies and no-arbitrage conditions
- Topics in Data Science
What you learn (4)
- explain key concepts of the selected data science topics
- identify computational challenges arising in data analysis
- apply techniques designed to address data analysis challenges
- evaluate the application of selected data science topics
- Professional Practice of Data Analysis
What you learn (9)
- describe the stages of a statistical investigative cycle
- carry out a statistical investigation on a straightforward problem
- generate numerical summaries in R to support data analysis
- create graphical visualisations in R to support data analysis
- write a report presenting the findings of a statistical analysis
- present analysis results through an online format
- explain key approaches to teamwork and leadership
- identify common issues arising in team communication
- collaborate productively within a team on a shared task
- Generalised Linear Models for Regression and Classification
What you learn (9)
- identify a suitable regression model for a given response variable type
- select and fit a statistical model following a systematic procedure
- check whether a fitted model's assumptions hold
- quantify uncertainty in estimated model parameters
- quantify uncertainty in model predictions
- explain the core assumptions underlying generalised linear models
- extend a standard GLM to handle overdispersion
- explain the theoretical foundations of generalised linear models
- apply computational methods to fit generalised linear models
- Risk Theory
What you learn (7)
- describe how probability models represent general insurance risk
- explain methods of reinsurance used to hedge risk
- apply mathematical and statistical techniques to financial and insurance problems
- calculate claim distributions adjusted for policy limits and risk sharing
- calculate insurance premiums and reserves
- derive Bayesian estimators for insurance claim parameters
- derive credibility premiums for insurance pricing
- Machine Learning frameworks
What you learn (7)
- select appropriate statistical learning approach for a given problem
- design data-analytic pipelines for statistical learning tasks
- build machine learning and deep learning models using python frameworks
- write well-documented and reproducible python code
- share code and results collaboratively with others
- interpret results from a statistical analysis
- communicate analytical findings to diverse audiences
- Measure Theory for Probability
What you learn (7)
- construct probabilistic models for random experiments using probability space properties
- evaluate measure-theoretic techniques for analysing probability problems
- calculate probabilities of events using measure-theoretic methods
- formulate formal definitions of probability using rigorous mathematical reasoning
- compute probabilities, independence and expectation in varied problems
- apply modes of convergence of random variable sequences to probability problems
- justify the use of convergence results when computing integrals and expectations
“What you learn” restates each module’s published learning outcomes in our own words, as short skill statements in the same form as the GCSE and A level curriculum, with the help of an AI model; fields of knowledge follow the twelve fields of the knowledge sphere. The university’s module page, linked from each title, is the authoritative description.
After graduating
Where does it lead?
£39,500median pay three years after graduating
Lower quarter£29k
Median£40k
Upper quarter£51k
Median — the middle earner: half earned more, half less. Pay is for graduates of this subject at this university who finished in 2016-18, not adjusted for inflation; based on 325 graduates.
Discover Uni dataset (HESA and Office for Students, CC BY 4.0, contains HESA data); earnings from the Longitudinal Education Outcomes data (Department for Education) · aggregation level 24, 14, 13, 23
Half of graduates earned between £29,000 and £51,000. Five years on, the median is £53,000. 90% were in work or further study 15 months after finishing; 90% of those in work were in professional or managerial jobs.
All outcome figures7
| Measure | Value | Based on |
|---|---|---|
| Continue after year 19% left | 89% | 55 |
| In work or study, 15 months on0% studying | 90% | 10 |
| Professional or managerial job | 90% | 15 |
| Pay 15 months on, median | £35,500 | 120 |
| Pay 3 years on, median | £39,500 | 325 |
| Pay 5 years on, median | £53,000 | 365 |
Money
What does it cost?
£10,050a year for UK students, 2027/28 entry
Tuition fee loan — paid straight to the university; repayments start the April after you leave, at 9% of income above the threshold. UK fees are capped by government and rise with inflation; placement and study-abroad years are usually charged at a lower rate.
Warwick fees page · source
About £30,150 in fees over 3 years, covered by a tuition fee loan you repay only when earning above the threshold.
National Student Survey 2025-26
What do students say?
97%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 76% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 40 students answered
Highest: 100% agreed on question 1; lowest: 74% on question 25.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 100% |
| Question 2 | 97% |
| Question 3 | 100% |
| Question 4 | 92% |
| Question 5 | 89% |
| Question 6 | 97% |
| Question 7 | 89% |
| Question 8 | 92% |
| Question 9 | 87% |
| Question 10 | 84% |
| Question 11 | 89% |
| Question 12 | 97% |
| Question 13 | 86% |
| Question 14 | 89% |
| Question 15 | 89% |
| Question 16 | 95% |
| Question 17 | 95% |
| Question 18 | 89% |
| Question 19 | 100% |
| Question 20 | 86% |
| Question 21 | 97% |
| Question 22 | 92% |
| Question 23 | 97% |
| Question 24 | 92% |
| Question 25 | 74% |
| Question 26 | 97% |
Question texts are published by the Office for Students; the dataset carries only the question numbers.
Modules, entry requirements and fees are facts read from University of Warwick’s course page on 2026-09-19 and may change; the university’s page is authoritative. Descriptions are not reproduced. Outcome figures are from the Discover Uni dataset. University of Warwick is a trade mark of the university; sch.today is independent of it.