Mathematics and Management (with a year in industry) BSc
AAB at A level, A in Mathematics; graduates earn a median £28,500 three years on.
Typical A level offer
AAB
A in Mathematics · 97% of entrants came with A levels · 95% stay on after year 1 · York course page, Discover Uni
Year 13 · applying
What do you need to get in?
AABtypical A level offer; aab including a in mathematics
Typical offer — the grades most applicants are asked for. Contextual offer: ABB, for applicants from under-represented backgrounds.
York course page, read 19 September 2026 · Discover Uni entry profile
- 1A in Mathematics
Who actually got in: 97% with A levels, 1% with other higher education. 6% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 265 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 |
|---|---|---|
| BTEC | DDDDDD plus grade A in A level Mathematics (or equivalent qualification) We consider a range of BTEC qualifications equivalent to 3 A Levels, or in combination with A Levels or other qualifications. For | Mathematics Distinction, Distinction in 2 BTEC Level 3 National Extended Certificates plus A in A Level Mathematics |
| IB | 35 points35 points including Higher Level 6 in Mathematics (either Analysis and Approaches or Applications and Interpretations) | Higher Level 6 in Mathematics |
| Scottish Highers | BBBB | — |
| Advanced Highers | B in Mathematics plus | — |
Years 1–4
What do you study?
6compulsory modules in year 1
Compulsory modules this year: mathematics & statistics 42% · business & administration 38% · digital & ict skills 11% · transversal skills 6%.
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.
York course page · optional modules vary by year
Year 1 · 6 core · 120 credits ≈ 1,200 hours
- Business Economics
What you learn (10)
- explain how competitive markets operate
- analyse factors influencing consumer demand
- analyse firm output decisions under perfect competition
- analyse firm output decisions under imperfect competition
- analyse pricing strategies used by firms with market power
- describe how governments intervene in markets
- discuss how macroeconomic conditions affect businesses
- apply basic economic reasoning to business situations
- identify trade-offs involved in typical business decisions
- apply critical thinking to evaluate economic decision-making issues
- Finance Fundamentals
What you learn (11)
- apply time value of money calculations to financial problems
- describe the structure of markets where financial instruments are traded
- identify different sources of business finance
- calculate the value of bonds using standard valuation methods
- apply introductory models to estimate share value
- explain the relationship between risk and expected return
- apply finance principles to personal or household financial decisions
- manage own learning and time to meet study deadlines
- resolve financial problems using appropriate analytical approaches
- collaborate effectively with peers on shared tasks
- produce written work following academic conventions
- Introduction to Accounting
What you learn (9)
- identify core concepts of financial accounting
- explain how financial statements are prepared
- construct a set of financial statements
- apply financial analysis techniques to business data
- interpret financial analysis results to support business decisions
- describe the role and structure of financial markets
- identify core concepts of management accounting
- explain the purpose of various management accounting tools
- apply management accounting tools to assess business operations
- Introduction to Probability and Statistics
What you learn (7)
- represent simple random experiments using probability models
- compute standard probability values using core formulae
- analyse relationships between independent and correlated random variables
- apply basic statistical techniques to data
- interpret results obtained from statistical techniques
- perform introductory data analysis using statistical software
- run simple simulations using a statistical computing package
- Foundations and Calculus
What you learn (8)
- prove elementary results concerning sets and their properties
- prove elementary results concerning functions and their properties
- apply differentiation techniques to solve calculus problems
- apply integration techniques to solve calculus problems
- perform calculations with vectors and complex numbers to solve problems
- prove rigorous statements about the behaviour of limits
- solve problems relating limits to derivatives and integrals
- use a computer algebra system to solve module-related mathematical problems
- Multivariable Calculus and Matrices
What you learn (9)
- solve second-order differential equations using standard techniques
- construct a Fourier series expansion of a function
- construct a Taylor series expansion of a function
- differentiate functions of several variables
- integrate functions of several variables
- perform algebraic operations on matrices
- apply matrix methods to solve systems of linear equations
- use computer algebra software to solve multivariable calculus problems
- use computer algebra software to solve matrix algebra problems
Year 2 · 6 core · 120 credits ≈ 1,200 hours
- Strategic Management
What you learn (6)
- explain key theories underpinning strategic management as a discipline
- identify skills and techniques needed for effective strategic management
- evaluate strategic management processes across varied organisational contexts
- evaluate strategic management techniques within the global economy
- apply strategic management theories to different organisational settings
- critically analyse the effects of strategic management on organisations
- People, Management and Professional Development
What you learn (2)
- describe challenges in managing operations and human resources
- explain the relevance of management theories to practice
- Financial Modelling with R
What you learn (13)
- identify statistical patterns characteristic of financial data
- interpret statistical properties observed in financial datasets
- explain the core concepts underlying ARMA models
- explain the core concepts underlying ARIMA models
- estimate time series model parameters from financial data
- perform stochastic simulations to estimate financial indicators
- explain the structure of financial factor models
- fit factor models to financial data
- interpret results obtained from fitted factor models
- use R software to perform statistical analysis of financial data
- present analytical findings clearly and concisely
- derive conclusions logically from stated assumptions
- evaluate the assumptions underpinning analytical conclusions
- Probability and Markov Chains
What you learn (12)
- calculate joint and conditional probability distributions
- compute expectations derived from joint and conditional distributions
- derive generating functions for standard probability distributions
- use generating functions to find expectation and variance
- apply the weak law of large numbers to analyse random variable sequences
- apply the central limit theorem to deduce asymptotic behaviour of estimators
- assess unbiasedness and consistency of a sequence of estimators
- estimate distribution parameters using maximum likelihood methods
- estimate distribution parameters using the method of moments
- evaluate the quality of parameter estimators
- calculate absorption probabilities in discrete Markov chains
- interpret the meaning of a stationary distribution in context
- Linear Algebra
What you learn (13)
- identify whether a given subset forms a linear subspace
- apply subspace properties to solve algebraic problems
- determine a basis for a given vector space
- calculate the dimension of a vector space from a basis
- recognise whether a given map is linear
- determine the kernel and image of a linear map
- apply the rank-nullity theorem to solve problems
- calculate eigenvalues of a linear map or matrix
- calculate eigenvectors corresponding to given eigenvalues
- define real and Hermitian inner products
- apply inner products to determine orthogonality of vectors
- derive consequences of orthogonality in vector spaces
- perform matrix factorisation using computer algebra software
- Statistical Inference and Linear Models
What you learn (11)
- calculate confidence intervals for population parameters
- perform hypothesis tests using frequentist methods
- interpret results of confidence intervals and hypothesis tests
- adapt statistical procedures to new scenarios using underlying theory
- derive new statistical tools from first principles
- describe methods for fitting linear regression models
- explain techniques for assessing linear model adequacy
- explain procedures used for selecting variables in regression models
- justify the rationale behind variable selection approaches
- apply statistical methodology to analyse real datasets
- communicate statistical findings in a written report
Year 4 · 3 core, 6 optional · 60 credits ≈ 600 hours
- Operations Research
What you learn (15)
- state key terminology used in linear programming
- describe fundamental results of linear programming theory
- describe the stages of the simplex algorithm
- apply the simplex method to solve a linear program
- state the fundamental theorem of linear programming
- prove the fundamental theorem of linear programming
- state the duality theorem of linear programming
- formulate transportation problems as mathematical models
- solve transportation problems using appropriate methods
- model and solve network optimisation problems
- describe key results from game theory
- apply game theory results to operations research problems
- translate real-world problems into mathematical formulations
- solve mathematical models using suitable operations research techniques
- interpret mathematical solutions in the context of original problems
- Mathematical Finance in Discrete Time
What you learn (11)
- describe the concepts underlying Markowitz portfolio theory
- analyse portfolio risk and return using Markowitz theory
- apply Markowitz theory to construct an efficient portfolio
- describe the structure and assumptions of the Capital Asset Pricing Model
- analyse asset pricing relationships derived from the Capital Asset Pricing Model
- apply the Capital Asset Pricing Model to estimate expected returns
- describe basic methods of compounding interest
- analyse the effects of different compounding methods on growth
- apply compounding calculations to financial problems
- analyse pricing outcomes under no-arbitrage conditions
- apply arbitrage pricing theory to value financial instruments
- Decision Theory and Bayesian Statistics
What you learn (6)
- describe fundamental concepts underlying Bayesian inference
- explain how Bayesian reasoning differs from classical inference
- apply Bayesian methods to standard statistical inference models
- use numerical techniques to compute Bayesian estimates
- apply credibility theory to estimate insurance risk
- apply Bayesian decision theory using loss functions
6 optional modules
- Company Law
What you learn (20)
- identify the different sources of law
- distinguish case law from statutory legislation
- distinguish between different forms of business organisation
- advise a client on the suitable form of business organisation
- explain the concept of legal personality
- analyse the principal duties owed by company directors
- apply directors' duties to given company scenarios
- evaluate the legal protections available to shareholders
- evaluate the legal protections available to creditors
- analyse different types of employment relationship
- advise on the appropriate choice of employment relationship
- advise an employer on approaches to terminating employment
- describe different forms of civil obligation
- apply civil obligation principles to factual scenarios
- identify the essential principles of a binding contract
- assess the effectiveness of common law and statutory controls on contracting parties
- identify the core principles of negligence in tort law
- analyse cases of negligent misstatement
- assess potential liability arising from negligent misstatements
- evaluate how criminal law affects commercial organisations
- Supply Chain Management
What you learn (4)
- explain material, finance and information flows in a supply chain
- analyse evidence against supply-chain models for a real-world case
- evaluate a chosen organisation's supply-chain management approach
- construct a supply-chain argument supported by evidence
- Sustainability and Business
What you learn (9)
- evaluate historical and contemporary sustainability theories
- describe methods of communicating sustainability performance
- critique corporate sustainability reporting and marketing practices
- analyse the roles of stakeholders in sustainability decision-making
- critique how corporate purpose evolves through stakeholder influence
- explain sustainability practices across different business functions
- critique sustainability approaches used in various industry sectors
- develop sustainability-focused strategies for business scenarios
- present evaluated sustainability strategies to an audience
- Contingencies
What you learn (12)
- define assurance and annuity benefits
- derive expressions for present values of insurance contracts
- calculate means and variances of contract present values
- calculate net premiums and reserves for insurance contracts
- derive gross premiums and reserves for assurance and annuity contracts
- calculate gross premiums and reserves for assurance and annuity contracts
- define actuarial functions for contracts involving two lives
- value cashflows related to two-life contracts
- project future cashflows for insurance products
- present analyses in a clear and rigorous structure
- apply logical reasoning to reach conclusions from assumptions
- evaluate assumptions underlying analyses and conclusions
- Further Statistics for Actuarial Science
What you learn (12)
- explain the theory underpinning generalised linear models
- apply generalised linear models to data sets
- explain the importance of generalised linear models in actuarial work
- describe key concepts of machine learning
- explain the relevance of machine learning techniques to actuarial science
- apply extreme value theory to actuarial loss modelling problems
- explain approaches to modelling dependence beyond simple correlation
- apply copula methods to dependence modelling problems
- apply mortality projection models, including the Lee-Carter model, to data
- present statistical analyses clearly and rigorously
- construct logical arguments from assumptions to conclusions
- evaluate the assumptions required to support given conclusions
- Actuarial Mathematics
What you learn (5)
- describe basic fixed income financial instruments
- explain how compound interest determines the time value of money
- explain how discounting determines the time value of money
- apply discounted cash flow methods to appraise investment projects
- analyse variations in the term structure of interest rates
“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?
£28,500median pay three years after graduating
Lower quarter£22k
Median£29k
Upper quarter£37k
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 190 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 13, 23
Half of graduates earned between £22,000 and £36,500. Five years on, the median is £36,000. 85% were in work or further study 15 months after finishing; 75% of those in work were in professional or managerial jobs.
All outcome figures7
| Measure | Value | Based on |
|---|---|---|
| Continue after year 13% left | 95% | 200 |
| In work or study, 15 months on13% studying | 85% | 65 |
| Professional or managerial job | 75% | 45 |
| Pay 15 months on, median | £31,500 | 30 |
| Pay 3 years on, median | £28,500 | 190 |
| Pay 5 years on, median | £36,000 | 215 |
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.
York fees page · source
About £40,200 in fees over 4 years, covered by a tuition fee loan you repay only when earning above the threshold. International students: £28,600 a year (2027/28).
National Student Survey 2025-26
What do students say?
82%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 60% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 125 students answered
Highest: 92% agreed on question 3; lowest: 54% on question 24.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 83% |
| Question 2 | 68% |
| Question 3 | 92% |
| Question 4 | 91% |
| Question 5 | 86% |
| Question 6 | 78% |
| Question 7 | 79% |
| Question 8 | 80% |
| Question 9 | 77% |
| Question 10 | 75% |
| Question 11 | 85% |
| Question 12 | 69% |
| Question 13 | 90% |
| Question 14 | 63% |
| Question 15 | 92% |
| Question 16 | 81% |
| Question 17 | 74% |
| Question 18 | 75% |
| Question 19 | 88% |
| Question 20 | 91% |
| Question 21 | 92% |
| Question 22 | 90% |
| Question 23 | 71% |
| Question 24 | 54% |
| Question 25 | 67% |
| Question 26 | 82% |
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 York’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 York is a trade mark of the university; sch.today is independent of it.