Economics and Mathematics BSc
AAA at A level, Mathematics; graduates earn a median £28,500 three years on.
Typical A level offer
AAA
Mathematics · 95% 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?
AAAtypical A level offer; aaa including 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
- 1Mathematics
Who actually got in: 95% with A levels, 5% with the IB. 5% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 15 entrants.
UCAS tariff points — a points scale for grades: A* = 56, A = 48, so A*A*A = 160. Entrants’ totals include extra subjects.
Other qualifications3
| Qualification | Typical offer | Required subjects |
|---|---|---|
| IB | 36 points36 points, including 6 in Mathematics at Higher Level (either Analysis and Approaches or Applications and Interpretations). | 6 in Mathematics at Higher Level |
| Scottish Highers | BBBB | — |
| Advanced Highers | A in Mathematics plus | — |
Years 1–3
What do you study?
6compulsory modules in year 1
Compulsory modules this year: mathematics & statistics 46% · digital & ict skills 23% · green & sustainability 20% · transversal skills 9%.
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
- Data, Evidence and Policy
What you learn (17)
- explain why data matter for economic analysis
- identify different types of data used in economics
- locate existing datasets relevant to an economic question
- construct new data suitable for economic analysis
- organise and store data using appropriate management practices
- assess the quality and reliability of a dataset
- apply reliable methods to measure economic variables
- summarise data using descriptive statistics
- interpret patterns in data through exploratory analysis
- create clear graphical representations of data
- use Excel functions to manage and manipulate data
- build pivot tables and charts to summarise data in Excel
- evaluate empirical evidence critically
- apply empirical evidence to analyse policy problems
- present evidence-based findings to a non-specialist audience
- collaborate effectively within a team to complete tasks
- manage time to meet set deadlines
- Principles of Economics
What you learn (4)
- produce written analysis of economic issues
- construct numerical and graphical representations of economic models
- explain key concepts of economic theory
- evaluate real-world economic problems using economic theory
- 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
- Mathematical Programming and Skills
What you learn (12)
- write programs using variables, expressions and assignments
- construct loops and conditional structures in code
- build functions and arrays to perform mathematical operations
- create classes and objects for mathematical applications
- apply inheritance and dynamic methods in program design
- search scientific literature relevant to a mathematical topic
- synthesise findings from scientific sources into a coherent summary
- typeset mathematical documents using LaTeX
- collaborate with peers to produce a shared report
- compile a professional-standard integrated report
- reflect on personal development of programming and research skills
- articulate acquired skills for professional career applications
Year 2 · 5 core, 6 optional · 100 credits ≈ 1,000 hours
- Econometric Analysis
What you learn (12)
- use OLS regression results to test economic hypotheses
- discuss the limitations of OLS regression methods
- describe panel data estimation techniques
- describe instrumental variables estimation techniques
- manage and prepare datasets using Stata
- estimate econometric models using Stata
- test statistical hypotheses using Stata
- interpret the results of an econometric analysis
- explain the methods used in applied economic papers
- evaluate the econometric approach of applied economic papers
- collaborate effectively within a team
- manage time to meet deadlines
- Microeconomic Theory
What you learn (2)
- explain core concepts used in microeconomic analysis
- apply standard analytical tools to economic problems
- Macroeconomic Analysis and Policy
What you learn (15)
- apply macroeconomic models to analyse short-run economic issues
- apply macroeconomic models to analyse medium- and long-run growth issues
- explain causes of inflation and unemployment using macroeconomic theory
- evaluate the effectiveness of monetary policy measures
- evaluate the effectiveness of fiscal policy measures
- contribute actively to problem-based learning tutorials
- design a research strategy to investigate macroeconomic issues
- evaluate macroeconomic theories and policies relevant to a given issue
- adapt problem-solving techniques to tackle unfamiliar economic problems
- communicate economic arguments clearly in written form
- communicate economic arguments clearly in verbal form
- work independently to complete economic analysis tasks
- collaborate effectively within a problem-based learning group
- evaluate personal contribution to team-based learning activities
- evaluate peers' contribution to team-based learning activities
- 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
- 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
6 optional modules
- Environmental Economics
What you learn (6)
- explain how economic theory contributes to understanding environmental problems
- describe alternative policy instruments used to address pollution and resource depletion
- evaluate the effectiveness of different environmental policy approaches
- critique the methods economists use to assign value to environmental goods
- apply economic tools and methods to analyse real environmental issues
- identify key literature and concerns relevant to environmental economics
- Development Economics
What you learn (3)
- identify theories explaining economic development
- discuss theories of economic development
- evaluate empirical methods for determining development factors
- Econometric Theory
What you learn (2)
- apply statistical theory concepts to economic data
- select appropriate statistical models and tests for datasets
- Industrial Organisation and Strategy
What you learn (5)
- define key terms used in industrial economics
- explain core concepts underpinning industrial economics
- apply industrial economics concepts to describe industry structure
- explain firm behaviour within selected industries using economic concepts
- apply theoretical models to analyse real-world industry examples
- Alternative Perspectives in Economics
What you learn (6)
- distinguish between different heterodox schools of economic thought
- compare alternative economic schools in terms of their assumptions and methods
- explain the central arguments underpinning heterodox economic approaches
- evaluate the validity of alternative economic theories and ideas
- apply non-neoclassical frameworks to interpret contemporary economies
- evaluate economic policy using perspectives beyond neoclassical economics
- 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
Year 3 · 1 core, 25 optional · 20 credits ≈ 200 hours
- Contemporary Economic Issues and Analysis
What you learn (8)
- justify why a given economic outcome constitutes a policy problem
- evaluate the social and economic consequences of low social mobility
- identify economic theories relevant to analysing a defined policy problem
- compare alternative theoretical predictions and explanations of an economic outcome
- assess the reliability of different types of empirical evidence used in policy design
- critique flawed analytical approaches unsuitable for informing policy decisions
- propose feasible policy measures to address an identified economic problem
- evaluate the practical limits and trade-offs of proposed economic policies
25 optional modules
- Econometric Methods for Research
What you learn (8)
- describe the structure of a multiple linear regression model
- describe the structure of simultaneous equations models
- describe the structure of nonlinear econometric models
- explain the relevance of key econometric modelling techniques to empirical research
- apply multiple linear regression to a simple empirical example
- apply simultaneous equations and nonlinear modelling techniques to simple examples
- apply maximum likelihood and generalised method of moments estimation to simple examples
- describe the concept of statistical hypothesis testing with an example
- Bubbles, Panics and Crashes
What you learn (7)
- describe the recurring patterns across historical financial crises
- explain the causes and development of speculative asset booms
- evaluate the significance of the lender of last resort function
- analyse how regulation, liquidity and credit constraints influence financial instability
- assess the extent to which monetary expansion drives financial crises
- estimate the economic costs resulting from financial crises
- evaluate the appropriateness of policy responses to historical crises
- Applied Econometrics
What you learn (6)
- interpret econometric evidence reported in academic journals and books
- apply instrumental variable methods and binary choice models to data
- analyse panel data combining cross-section and time series dimensions
- use econometric software packages to analyse provided datasets
- formulate economic hypotheses in a testable form
- select appropriate statistical methods for testing economic hypotheses
- Macroeconomics: Topics in Theory for Policy
What you learn (2)
- explain modern macroeconomic models based on microfoundations
- evaluate current policy debates in macroeconomics
- Labour Economics
What you learn (5)
- explain why inequality and discrimination represent policy challenges
- apply microeconomic models to predict labour market outcomes
- evaluate how microeconomic models and empirical evidence inform policy decisions
- interpret empirical evidence relating to specific labour market questions
- use empirical evidence to assess potential policy solutions
- Game Theory
What you learn (6)
- define core game-theoretic concepts including strategy, best-response and equilibrium
- explain the purpose and relevance of game-theoretic concepts
- evaluate a strategic game using game-theoretic methods
- predict the likely outcome of a strategic game
- apply game-theoretic methods to analyse real-world economic problems
- evaluate economic policy using game-theoretic analysis
- Economics Dissertation
What you learn (7)
- formulate testable research hypotheses using economic theory
- identify and gather relevant economic data sources
- apply econometric methods to test economic hypotheses
- compile a structured report summarising research findings
- explain research findings in accessible non-technical language
- manage own research project independently
- present and discuss research progress with peers
- International Economics
What you learn (17)
- explain why countries engage in international trade
- assess the economic effects of international trade on nations
- describe patterns of trade flows between countries
- analyse how market structure influences trade outcomes between nations
- identify different policy tools used to regulate trade
- evaluate the effects of trade policy instruments on markets
- explain political factors shaping trade policy decisions
- explain short-run determinants of exchange rate movements
- explain long-run determinants of exchange rate movements
- analyse how international transactions influence domestic economic conditions
- evaluate how international transactions affect national policy choices
- evaluate the costs and benefits of alternative exchange rate regimes
- assess implications of exchange rate regime choice for an economy
- identify common characteristics of financial and currency crises
- evaluate policy measures designed to prevent financial crises
- compare economic theories of trade with empirical evidence
- compare international macroeconomic theory with empirical evidence
- Monetary Economics
What you learn (3)
- explain conditions under which money has an economic role
- analyse monetary policy implications for the optimum quantity of money
- describe factors influencing liquidity in financial markets
- International Economic Growth and Development
What you learn (4)
- explain how individual studies contribute to broader development debates
- select a suitable topic for an independent research project
- develop a research idea into an oral presentation
- produce a written research paper from an initial project idea
- Advanced Topics in Microeconomic Theory
What you learn (6)
- construct formal economic models for information and market design problems
- analyse formal models applied to matching and market design problems
- explain how information shapes outcomes across different market structures
- describe how institutional frameworks influence the use of information
- identify core concepts of information economics such as equilibrium and efficiency
- explain techniques used to analyse incentives and stability in institution design
- Health Economics
What you learn (7)
- explain why health economics emerged as a specialised branch of economics
- describe major theories used in health economics
- evaluate empirical studies within health economics
- interpret findings from health economics research
- apply economic frameworks to health and health care problems
- explain how economic models inform health policy debates
- evaluate current health policies using economic analysis
- Economics of Social Policy
What you learn (5)
- evaluate economic objectives underpinning the Welfare State
- evaluate non-economic objectives underpinning the Welfare State
- apply economic analysis methods to social policy issues
- evaluate existing social policies using economic frameworks
- critique social policy issues using independent reasoning
- Experimental and Behavioural Economics
What you learn (5)
- identify economic areas amenable to experimental investigation
- describe the types of insight experiments can generate about economic behaviour
- explain key methodological challenges in designing economic experiments
- evaluate issues arising when running an economic experiment
- conduct a research project applying experimental methods within a team
- Political Economics
What you learn (5)
- construct theoretical models to represent political economy questions
- assess how empirical data can confirm or refute hypotheses
- critically evaluate published research in political economy
- formulate a personal position on political economy issues
- communicate views on political economy topics in speech and writing
- Cryptography
What you learn (8)
- identify weaknesses in classical cryptosystems
- exploit identified weaknesses to break classical ciphers
- apply public key cryptographic protocols to solve numerical problems
- exploit weaknesses of public key protocols in simple scenarios
- perform calculations involving the mathematical structure of AES
- apply quantum information postulates to solve calculations
- describe the sequence of steps in the BB84 protocol
- explain why the BB84 protocol is secure
- Time Series
What you learn (12)
- define key concepts underlying time series analysis
- apply stationarity concepts and transformations to real data
- fit arima and state space models to produce forecasts
- compute a correlogram and sample spectrum from data
- interpret correlogram and spectrum results
- derive mathematical properties of arima models
- select a suitable arima model for given data
- fit a chosen arima model using statistical software
- compute forecasts for various linear time series models
- fit state space models to time series data
- apply the kalman filter to estimate model states
- conduct spectral analysis of stationary time series
- 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
- Advanced Regression and Multivariate Analysis
What you learn (8)
- describe models used for multivariate data analysis
- describe models used for multilevel data analysis
- describe nonparametric statistical models
- apply key techniques for analysing multivariate and multilevel data
- apply nonparametric estimation methods to data sets
- select appropriate statistical techniques for different data types
- use R software to analyse multivariate data sets
- use R software to analyse multilevel data sets
- 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
- Numerical Analysis
What you learn (5)
- select a suitable numerical method for a given mathematical problem
- apply a numerical technique to approximate a solution
- analyse the error behaviour of a numerical method
- determine the stability region of a numerical method
- implement numerical methods using Python
- Mathematical Finance in Continuous Time
What you learn (2)
- explain the Black-Scholes formula for pricing options
- apply basic interest rate models to financial problems
- Statistical Data Science
What you learn (6)
- explain the theoretical basis of key statistical models and tools
- discuss the strengths and limitations of different statistical approaches
- apply statistical techniques to analyse real datasets using R software
- select suitable machine learning or statistical methods for a given problem
- conduct independent statistical analysis to answer a research question
- present statistical findings clearly using tables and graphs
- 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
- Survival Analysis and Generalised Linear Models
What you learn (10)
- explain how exponential families unify models for differently scaled variables
- apply maximum likelihood methods to estimate generalised linear models
- apply logistic regression to analyse binary outcome data
- apply Poisson regression to analyse count data
- produce descriptive statistics summarising survival study data
- create graphical summaries representing survival data from various study designs
- estimate survival model parameters from censored data
- apply hypothesis testing techniques to draw conclusions about survival data
- use R software to carry out generalised linear model analysis
- use R software to carry out survival data analysis
“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, 24, 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 15% left | 95% | 25 |
| 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 £30,150 in fees over 3 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 2024-26
What do students say?
79%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 82% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 15 students answered
Highest: 100% agreed on question 3; lowest: 43% on question 12.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 86% |
| Question 2 | 64% |
| Question 3 | 100% |
| Question 4 | 79% |
| Question 5 | 86% |
| Question 6 | 71% |
| Question 7 | 50% |
| Question 8 | 79% |
| Question 9 | 79% |
| Question 10 | 50% |
| Question 11 | 71% |
| Question 12 | 43% |
| Question 13 | 79% |
| Question 14 | 50% |
| Question 15 | 93% |
| Question 16 | 92% |
| Question 17 | 79% |
| Question 18 | 46% |
| Question 19 | 93% |
| Question 20 | 93% |
| Question 21 | 100% |
| Question 22 | 71% |
| Question 23 | 71% |
| Question 24 | 50% |
| Question 25 | 62% |
| Question 26 | 79% |
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.