Discrete Mathematics BSc
A*A*A at A level, A* in Mathematics; graduates earn a median £39,500 three years on.
Typical A level offer
A*A*A
A* in Mathematics · 95% of entrants came with A levels · 90% 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; a*a*a to include a* in mathematics.
Typical offer — the grades most applicants are asked for. Contextual offer: A*AA, for applicants from under-represented backgrounds.
Warwick course page, read 19 September 2026 · Discover Uni entry profile
- 1A* in Mathematics
Who actually got in: 95% with A levels, 5% with the IB. 30% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 25 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 points39 with 7, 6, 6 in three Higher Level subjects to include 7 in Higher Level Mathematics ('Analysis and Approaches' only) | 7, 6, 6 in three Higher Level subjects to include 7 in Higher Level Mathematics |
| BTEC | —We welcome applications from students taking BTECs alongside A level Mathematics. Applications are considered on an individual basis and subjects with overlapping curricula will only be counted once. | — |
| Advanced Highers | A1 in Advanced Higher Mathematics plus A in one additional subject at Advanced HA1 in Advanced Higher Mathematics plus 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?
10compulsory modules in year 1, 3 optional
Compulsory modules this year: mathematics & statistics 73% · digital & ict skills 26% · engineering & manufacturing 1%.
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 · 10 core, 3 optional · 105 credits ≈ 1,050 hours
- Programming for Computer Scientists
What you learn (8)
- describe the stages of the programming process from problem definition to solution design
- apply integrated testing practices while coding a solution
- write programs in a high-level language using imperative programming constructs
- structure software designs using encapsulation and abstraction concepts
- justify the correctness of a method or program using informal reasoning
- justify that a program terminates as intended
- apply different strategies to test software programs
- manage run-time errors using exception handling techniques
- 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
- Logic and Automata
What you learn (8)
- construct formal proofs using different logical systems
- evaluate the validity of logical proofs
- apply logical formalisms to specify computing system behaviour
- verify computing systems using logical methods
- specify a formal language using appropriate notation
- translate between different representations of formal languages
- argue whether a given language is regular or not
- describe links between logical expressiveness and computational models
- Introduction to Discrete Mathematics
What you learn (5)
- explain the purpose of formal definitions and proofs in mathematics
- construct formal and informal proofs to justify mathematical statements
- apply algorithmic thinking to solve discrete mathematics problems
- describe how discrete mathematics supports computer science applications
- identify core concepts of discrete mathematics such as sets, logic and graphs
- 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
- Calculus 1
What you learn (6)
- describe the structure and properties of the real number system
- explain the concept of infinity as a mathematical symbol
- calculate limits of sequences and series
- distinguish between conditional and absolute convergence of series
- identify properties of continuous functions
- explain the concept of absolute continuity of functions
- Calculus 2
What you learn (6)
- define differentiability and higher-order derivatives of functions
- describe key properties of differentiable functions
- apply Taylor's theorem to approximate functions using series
- explain when a function can be represented by a power series
- apply fundamental properties of integrals to solve problems
- apply the fundamental theorem of calculus to evaluate integrals
- 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
- 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 Probability
What you learn (9)
- apply standard techniques to solve probability problems
- describe experiments involving random outcomes
- interpret random experiments using mathematical probability
- explain the concept of conditional probability
- explain the concept of statistical independence
- apply probability distribution theory to random variables
- calculate expectation, variance and covariance of random variables
- select suitable probability distributions for given problems
- construct probability models to represent real problems
3 optional modules
- Professional Skills
What you learn (13)
- evaluate and revise own written work for clarity and quality
- apply correct referencing and citation conventions in written work
- describe writing as an iterative drafting process
- explain fundamental concepts of computer operating systems
- apply operating system concepts to a networked computing environment
- use standard software applications and system utilities
- deliver a structured oral presentation to an audience
- use university network-based information systems
- explain key aspects of computer law relevant to professional practice
- describe the role of professional bodies in computing
- discuss the social impact of computing technology
- explain the value of continuing professional development
- create a personal development plan reflecting own goals
- Computer Security
What you learn (10)
- define key concepts underpinning computer security
- describe the main challenges facing computer security
- identify common security threats to computer systems
- describe basic measures used to mitigate security threats
- apply numeracy and IT skills to complete module tasks
- communicate findings clearly in written or spoken form
- organise independent study to support learning
- evaluate the security of a given computer system
- assess the security requirements of a computing system
- design a secure computer system to address identified risks
- Functional Programming
What you learn (8)
- compare the strengths and weaknesses of different programming paradigms
- select a suitable programming language for a given task
- apply functional programming techniques to solve coding problems
- identify recurring patterns within program code
- design reusable programming abstractions from identified patterns
- implement programming abstractions in functional code
- prove basic properties of pure functional programs
- evaluate programs using different evaluation strategies
Year 2 · 4 core, 5 optional · 55 credits ≈ 550 hours
- Algorithmic Graph Theory
What you learn (7)
- describe fundamental concepts of graphs, directed graphs and weighted graphs
- relate graph theory concepts to real-world examples
- apply algorithmic techniques to determine basic graph parameters
- analyse structural properties of graphs using algorithmic methods
- design efficient algorithms to solve graph optimisation problems
- apply graph theory techniques to solve networking problems
- apply graph theory techniques to solve telecommunication problems
- Algorithms
What you learn (8)
- apply algorithm design techniques to solve computational problems
- apply data structure design techniques within algorithmic solutions
- describe common data structures and their properties
- use appropriate data structures when designing and implementing algorithms
- describe techniques for designing efficient algorithms
- prove the correctness of an algorithm
- analyse the computational efficiency of an algorithm
- identify fundamental algorithmic problems and their solution 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
- Introduction to Mathematical Statistics
What you learn (15)
- apply conditional probability concepts to solve problems
- calculate conditional expectations for random variables
- use moment generating functions to analyse distributions
- apply properties of multivariate Gaussian distributions to problems
- apply the law of large numbers to probabilistic problems
- apply the central limit theorem to approximate distributions
- describe the components of a parametrized statistical model
- explain the concept of an estimator and its sampling distribution
- describe the structure and purpose of hypothesis tests
- calculate maximum likelihood estimators for given models
- construct confidence intervals from estimator properties
- calculate likelihood ratios for statistical testing problems
- construct hypothesis tests using likelihood ratio methods
- apply t and F tests to statistical inference problems
- present mathematical solutions with structured coherent arguments
5 optional modules
- Data Analytics
What you learn (7)
- explain core methodologies and tools used to derive insights from data
- apply fundamental data analysis principles to extract meaningful insights
- describe data-driven modelling approaches across different disciplines
- apply machine learning methods to analyse datasets
- use big data mining techniques to prepare for advanced project work
- develop programming skills to implement data analytic solutions
- apply theoretical data analytics knowledge to real-world case studies
- 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
- 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
- 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
- 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
Year 3 · 3 core, 1 optional · 60 credits ≈ 600 hours
- Complexity of Algorithms
What you learn (7)
- describe different complexity classes of computational problems
- distinguish between problems belonging to different complexity classes
- apply formal methods to prove a problem is solvable
- apply formal methods to prove a problem is unsolvable
- prove formal bounds on the time required to solve a problem
- prove formal bounds on the memory required to solve a problem
- design simplified versions of computationally hard problems that are easier to solve
- Discrete Mathematics Project
What you learn (7)
- plan resource and schedule allocation for an individual project
- identify professional issues relevant to project management
- design and build a complete software system with documentation
- deliver an oral presentation using audio-visual and interactive aids
- respond effectively to audience questions during a presentation
- write a substantial technical report on project work
- compose reflective writing evaluating own project experience
- Approximation and Randomised Algorithms
What you learn (6)
- design approximation algorithms using appropriate mathematical techniques
- analyse the performance guarantees of approximation algorithms
- design randomised algorithms using appropriate mathematical techniques
- analyse the expected performance of randomised algorithms
- construct faster algorithms offering provable but weaker guarantees than exact methods
- compare trade-offs between exact and approximate algorithmic solutions
1 optional module
- 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
“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 110% left | 90% | 20 |
| In work or study, 15 months on5% studying | 90% | 30 |
| Professional or managerial job | 90% | 25 |
| 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?
90%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 59% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 10 students answered
Highest: 100% agreed on question 5; lowest: 30% on question 14.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 80% |
| Question 2 | 50% |
| Question 3 | 80% |
| Question 4 | 80% |
| Question 5 | 100% |
| Question 6 | 80% |
| Question 7 | 100% |
| Question 8 | 80% |
| Question 9 | 70% |
| Question 10 | 70% |
| Question 11 | 90% |
| Question 12 | 80% |
| Question 13 | 60% |
| Question 14 | 30% |
| Question 15 | 80% |
| Question 16 | 70% |
| Question 17 | 40% |
| Question 18 | 71% |
| Question 19 | 100% |
| Question 20 | 89% |
| Question 21 | 90% |
| Question 22 | 100% |
| Question 23 | 80% |
| Question 24 | 40% |
| Question 25 | 33% |
| Question 26 | 90% |
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.