Mathematics and Finance BSc
AAA at A level, Mathematics; graduates earn a median £28,500 three years on.
Typical A level offer
AAA
Mathematics · 85% of entrants came with A levels · 85% 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: 85% with A levels, 15% with other higher education. 6% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 10 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 | 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% · green & sustainability 20% · business & administration 17% · digital & ict skills 14%.
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
- Introduction to Finance
What you learn (11)
- explain the time value of money and compound interest
- define the internal rate of return
- apply the perpetual growth model to value financial instruments
- apply the net present value rule to appraise investments
- describe the distinguishing features of bonds and stocks
- explain how financial instruments are priced
- describe common uses of financial instruments
- explain the concept of market efficiency
- analyse portfolio risk and diversification benefits
- describe the characteristic line of a security
- define the equity risk premium
- 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
Year 2 · 4 core, 2 optional · 80 credits ≈ 800 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
- Corporate Finance
What you learn (32)
- identify key activities of the accountancy profession
- describe the duties and responsibilities of company auditors
- discuss problems associated with the auditor's role
- identify main legal and accounting entity types in the private sector
- compare advantages and obligations of different business entities
- describe the typical content of a public company's annual report
- explain the process of setting accounting standards in the uk
- describe desirable qualities of financial statements
- identify main sources of company capital
- explain the components of a company balance sheet
- discuss valuation issues relating to inventory and other balance sheet items
- differentiate accruals accounting from cash-flow accounting
- describe entries in an income statement and statement of changes in equity
- explain the purpose of a company cash-flow statement
- distinguish between solvency and profitability
- identify users of company accounts and their information needs
- apply ratio and trend analysis to evaluate company performance
- evaluate limitations of ratio analysis including inflation effects
- identify main sources of tax revenue and tax types
- explain the accountant's role in tax compliance and tax planning
- prepare basic financial statements from company transaction data
- perform ratio analysis on company financial statements
- calculate basic corporation tax from company data
- evaluate advantages and disadvantages of different securities for project funding
- perform capital budgeting analysis for investment decisions
- critically assess theories of capital structure and dividend policy
- solve agency models applied to corporate financing decisions
- describe incentive problems caused by asymmetric information
- explain contract design solutions to information asymmetry
- explain motivations for mergers and acquisitions
- evaluate theoretical explanations for corporate events
- explain corporate finance issues in an international context
- 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
2 optional modules
- Microeconomic Theory or Macroeconomic Analysis and Policy
What you learn (2)
- explain core concepts used in microeconomic analysis
- apply standard analytical tools to economic problems
- 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 · 4 core, 23 optional · 80 credits ≈ 800 hours
- Commodity Markets and Derivative Securities
What you learn (13)
- evaluate the valuation of financial derivatives such as options and futures
- describe basic approaches to valuing derivative securities
- describe the structure and features of forward contracts, futures and options
- apply valuation methods to different derivative contracts
- explain hedging policies and risk management techniques
- identify key problems and controversies in primary commodity trading
- explain theoretical and institutional aspects of commodity markets
- explain how commodity market mechanisms serve social interests
- describe distinctive features of financial futures markets
- analyse the interaction between spot and forward or futures markets for commodities
- analyse static and dynamic market models theoretically
- model the structure of financial markets
- evaluate empirical methods and findings in commodity market research
- The Structure and Regulation of Financial Markets
What you learn (6)
- explain the role of banks and financial markets in the economy
- describe the concept of asymmetric information
- explain the consequences of asymmetric information for financial markets
- explain how financial crises develop
- justify the need for financial regulation
- explain the function of central banks in financial markets
- 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
- 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
23 optional modules
- 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
- 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
- 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
- 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 & 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
- Financial and Time-Series Econometrics
What you learn (10)
- describe key models for analysing stationary time series
- describe key models for analysing nonstationary time series
- interpret empirical research papers in macroeconomics and finance
- apply time-series econometric techniques using EViews software
- forecast future interest rates using term structure information
- evaluate market efficiency and potential for abnormal profits
- estimate the value at risk of an investment portfolio
- model asset returns using single-factor and multi-factor models
- analyse asset return behaviour using factor-based models
- analyse portfolio risk using principal component methods
- 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
- 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
- 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 | 85% | 15 |
| 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 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.