Statistics (Faster Route) MSci
AAB – BBB at A level, A-level Mathematics; graduates earn a median £31,500 three years on.
Typical A level offer
AAB – BBB
A-level Mathematics · 65% of entrants came with A levels · 80% stay on after year 1 · Glasgow course page, Discover Uni
Year 13 · applying
What do you need to get in?
AAB – BBBtypical A level offer
Typical offer — the grades most applicants are asked for. A contextual offer can be lower for applicants from under-represented backgrounds; none is published for this course.
Glasgow course page, read 19 September 2026 · Discover Uni entry profile
- 1A-level Mathematics
Who actually got in: 65% with A levels, 10% via Access courses. 65% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 45 entrants.
UCAS tariff points — a points scale for grades: A* = 56, A = 48, so A*A*A = 160. Entrants’ totals include extra subjects.
Other qualifications2
| Qualification | Typical offer | Required subjects |
|---|---|---|
| Scottish Highers | AAAAA | Higher Mathematics and a Higher Science subject at AA. (AB may be considered). |
| IB | 34 (6,5,5) | HL Mathematics (Analysis & Approaches). |
Years 1–5
What do you study?
1compulsory module in year 1, 2 optional
Compulsory modules this year: mathematics & statistics 83% · transversal skills 11% · language & communication 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.
Glasgow course page · optional modules vary by year
Year 1 · 1 core, 2 optional · 40 credits ≈ 400 hours
- Mathematics 1
What you learn (18)
- communicate mathematical ideas using sets, functions, relations and groups
- identify the components of a mathematical statement such as hypotheses and conclusions
- construct valid proofs using direct argument, induction, contradiction or counterexample
- solve equations and inequalities across number systems and geometric settings
- solve differential equations using algebraic and calculus techniques
- define groups and subgroups and recognise their properties
- perform group operations using cycle notation for permutation groups
- calculate the order of groups and elements using Lagrange's theorem
- derive derivatives and integrals from first principles using limits
- relate differentiation and integration through the fundamental theorem of calculus
- compute derivatives of scalar and vector functions using standard rules
- compute integrals using substitution, parts and other standard methods
- apply calculus techniques to solve optimisation and geometric measurement problems
- perform matrix and vector operations in three-dimensional space
- model real-world problems using mathematical tools and interpret the solutions
- extract key concepts and methods from independent reading of mathematical texts
- present mathematical arguments in writing with precise notation and clear conclusions
- collaborate with peers to discuss and solve mathematical problems
2 optional modules
- Statistics 1Y: Introduction to Statistics: Learning from Data
What you learn (18)
- explain why natural variation occurs in data
- explain basic probability concepts such as sample space and event
- explain conditional probability and statistical independence
- explain the concept of a random variable and its probability distribution
- describe standard discrete probability distributions
- describe standard continuous probability distributions
- identify appropriate applications of standard probability distributions
- use statistical tables to find Normal distribution values
- use statistical tables to find chi-squared distribution values
- define population, sample, parameter and estimate
- distinguish between different common types of statistical data
- display different data types using suitable graphical methods
- explain why interval estimates are useful for unknown parameters
- interpret interval estimates correctly
- interpret the results of hypothesis tests
- check the assumptions underlying basic statistical procedures
- evaluate how violated assumptions affect the reliability of statistical results
- apply statistical methods using a programming language
- Statistics 1Z: Data Modelling in Action
What you learn (17)
- perform hypothesis tests on continuous data
- calculate interval estimates for continuous data
- interpret results of hypothesis tests and interval estimates
- use statistical tables for normal and t-distributions
- distinguish between paired and independent data
- identify paired and independent data in practical examples
- calculate the sample correlation coefficient between two variables
- interpret the sample correlation coefficient
- fit a linear regression line to data
- construct interval estimates for correlation and regression parameters
- carry out hypothesis tests for correlation and regression parameters
- explain correlation and regression results in non-technical language
- explain why checking model assumptions matters
- outline statistical modelling strategies across application domains
- interpret output from simple statistical models
- explain the calculation procedures underlying simple statistical models
- perform calculations for simple statistical model examples
Year 2 · 6 core · 60 credits ≈ 600 hours
- Mathematics 2A: Multivariable Calculus
What you learn (14)
- sketch three-dimensional surfaces using cross-sections and contour lines
- apply the chain rule to differentiate partial derivatives
- determine stationary points of multivariable functions
- apply Lagrange multipliers to solve constrained optimisation problems
- state the definitions of divergence, gradient and curl operators
- apply vector differential operators to solve problems
- compute signed volume under a surface via double integration
- determine integration limits for a given domain
- change the order of integration and apply jacobian-based variable substitution
- compute line integrals along curves
- parametrise surfaces and compute surface integrals
- apply Green's theorem to evaluate closed line integrals
- state key mathematical definitions, results and formulae from lectures
- apply lecture results and formulae to solve new problems
- Mathematics 2B: Linear Algebra
What you learn (18)
- state fundamental properties of matrices and their operations
- apply matrix operations and symbolic manipulation to solve problems
- determine whether a given set forms a vector subspace
- determine whether a set of vectors is linearly independent
- describe the span of a set of vectors
- determine whether a map between vector spaces is linear
- derive the matrix representation of a linear map for given bases
- calculate a matrix determinant using cofactor expansion
- prove properties of determinants under row or column operations
- derive the characteristic polynomial of a square matrix
- compute eigenvalues and eigenvectors of a matrix
- apply eigenvalues and eigenvectors to solve problems
- derive the quadratic form associated with a symmetric matrix
- determine the rank and signature of a symmetric matrix
- diagonalise a symmetric matrix
- apply the Gram-Schmidt process to construct an orthonormal basis
- state key definitions and theorems covered in the module
- apply covered theorems and definitions to solve problems
- Statistics 2R: Probability
What you learn (13)
- state the axioms of probability
- state basic propositions of probability theory
- apply basic propositions of probability theory to problems
- construct rigorous proofs of basic probability propositions
- explain the concept of a probability distribution
- explain the concept of a random variable
- define moments of a random variable including expectation and variance
- derive general properties of moments from their definitions
- calculate moments for specific probability distributions
- derive the distribution of a transformed random variable
- identify standard discrete and continuous distributions in context
- calculate probabilities and moments from standard distributions
- apply probability theory to solve practical problems
- Statistics 2S: Statistical Methods, Models and Computing 1
What you learn (13)
- identify different types of data structures
- summarise data using graphical methods
- summarise data using numerical methods
- manipulate datasets using statistical software
- analyse data using statistical software methods
- describe methods for sampling data
- derive the sampling distribution of a statistic
- define point and interval estimates
- apply maximum likelihood estimation techniques
- construct confidence intervals for parameters
- generate statistical software output for course methods
- interpret statistical software results
- communicate statistical findings clearly
- Statistics 2X: Probability II
What you learn (13)
- explain the concept of a joint distribution
- explain the concept of independent random variables
- derive marginal and conditional distributions for random variables
- calculate moments of marginal and conditional distributions
- calculate covariance and correlation between discrete and continuous random variables
- determine the distribution of a function of several random variables
- apply standard techniques to derive the distribution of a sum of random variables
- derive relationships between different discrete and continuous probability distributions
- define the multivariate normal distribution
- derive marginal and conditional distributions from a multivariate normal distribution
- derive probability inequalities such as chebyshev's inequality
- apply probability inequalities to bound probabilities
- apply the central limit theorem and laws of large numbers to solve problems
- Statistics 2Y: Statistical Methods, Models and Computing 2
What you learn (10)
- define null and alternative hypotheses for a statistical test
- conduct standard hypothesis tests on sample data
- express linear models using algebraic and matrix notation
- derive estimators for linear model parameters
- interpret estimated parameters of a linear model
- construct linear models from given data
- evaluate assumptions underlying a fitted linear model
- compare alternative linear models to select the most suitable
- produce statistical software output for taught methods
- explain and present statistical software results clearly
Year 3 · 9 core · 120 credits ≈ 1,200 hours
- Principles of Designed Experiments and Surveys
What you learn (6)
- distinguish experiments from observational studies
- explain key design principles such as randomisation and blinding
- apply design principles to practical experimental problems
- explain the theory underlying factorial and block designs
- select suitable factorial or block designs for given applications
- evaluate competing designs using optimality criteria
- Statistical Inference
What you learn (16)
- formulate the likelihood function for a given statistical model
- carry out likelihood-based inference across different models
- justify the selection of a prior distribution for a given problem
- apply rules for updating prior distributions when new data arrive
- calculate posterior predictive distributions for a model
- apply numerical optimisation methods to maximise a likelihood function
- produce point and interval estimates within a likelihood framework
- produce point and interval estimates within a bayesian framework
- construct hypothesis tests for parameters of a likelihood model
- calculate the statistical power of a hypothesis test
- describe theoretical properties used to judge estimation and testing procedures
- evaluate whether likelihood-based methods satisfy these theoretical properties
- explain the statistical principles underlying the bootstrap method
- apply the bootstrap technique to solve practical statistical problems
- build likelihood and bayesian models using statistical software
- analyse model outputs produced by statistical programming tools
- Regression Models
What you learn (6)
- explain the theoretical basis for fitting regression models to data
- build regression models from real datasets using statistical software
- interpret the output of fitted regression models
- conduct statistical inference from fitted models using relevant distribution theory
- compare alternative regression models using model selection techniques
- evaluate whether model assumptions hold for a given real dataset
- Stochastic Processes
What you learn (15)
- describe the structure of the gambler's ruin problem
- derive the probability of ruin in the gambler's ruin problem
- derive the expected duration of a gambler's ruin game
- define a random walk process
- calculate the probability a random walk occupies a given position at a given time
- calculate the probability and expected time of first return for a random walk
- describe homogeneous markov chains with discrete time and finite states
- identify homogeneous markov chains from given processes
- use a transition matrix to determine basic markov chain properties
- explain the concepts of limiting and stationary distributions
- calculate limiting and stationary distributions in simple markov chains
- classify markov chain states as absorbing, periodic, persistent, transient or ergodic
- define a homogeneous poisson process
- apply the poisson process to model queueing behaviour
- derive reliability and hazard functions
- Statistical Programming in R and Python
What you learn (7)
- design functions using appropriate control structures in R and Python
- implement programmed functions in both R and Python
- apply built-in data structures efficiently in R and Python
- create interpretable figures and graphs using R and Python
- explain features of object-oriented classes in programming
- exploit object-oriented design features in practical coding
- perform data-analytic tasks using external software libraries
- Multivariate Methods
What you learn (13)
- produce graphical displays of multivariate data
- interpret graphical displays of multivariate datasets
- apply dimension reduction techniques to multivariate datasets
- interpret results obtained from dimension reduction techniques
- apply classical clustering techniques to group multivariate observations
- apply classical discriminant techniques to classify observations
- interpret outcomes of cluster and discriminant analyses
- apply formal criteria to select appropriate statistical models
- interpret output generated by statistical software for multivariate analysis
- distinguish algorithmic approaches from probabilistic approaches in data analysis
- compare advantages and limitations of linear and non-linear methods
- explain the concept of latent variables
- infer latent variables from observed multivariate data
- Communication Skills and Ethics
What you learn (6)
- synthesise results and conclusions using critical thinking
- carry out a data analysis project independently
- collaborate with others on a shared data analysis project
- present analytical results and conclusions to varied audiences
- explain statistical findings clearly to non-specialists
- apply basic functions of statistical software to analyse data
- Data Skills & Consultancy
What you learn (9)
- identify anomalous or outlying observations in a dataset
- determine appropriate ways to handle unusual data points
- apply client-facing consultancy techniques to a statistical problem
- translate a scientific problem into a suitable statistical approach
- work collaboratively within a team on a consultancy project
- coordinate roles and responsibilities within a project team
- present statistical analysis results clearly to an audience
- adapt presentation style to suit different audiences
- apply statistical knowledge to a real-world consultancy project
- Multivariate Statistics and Machine Learning 2
What you learn (8)
- compare the strengths and weaknesses of different classification algorithms
- explain advanced clustering and dimension reduction techniques
- explain techniques for inferring structure in graphical models
- identify limitations of machine learning methods
- evaluate approaches for assessing and improving model performance
- select appropriate machine learning methods for a given real-world problem
- implement machine learning methods to solve moderately complex problems
- evaluate the performance of implemented machine learning solutions
Year 4 · 5 core, 8 optional · 70 credits ≈ 700 hours
- Advanced Bayesian Methods
What you learn (11)
- demonstrate the application of monte carlo simulation methods
- explain how gibbs sampling generates posterior samples
- explain how the metropolis-hastings algorithm generates posterior samples
- code gibbs sampling algorithms in r
- code metropolis-hastings algorithms in r
- apply diagnostic techniques to assess mcmc convergence and mixing
- describe bayesian methods for comparing statistical models
- calculate bayes factors to compare simple models
- explain mcmc-based techniques for model selection
- explain mcmc-based techniques for model mixing
- describe posterior predictive checks used to assess model fit
- Advanced Data Analysis
What you learn (10)
- formulate research questions for a given practical context
- identify appropriate informal and formal statistical methods for a problem
- apply stages of advanced statistical analysis using R software
- interpret output produced by an R statistical procedure
- evaluate and organise findings and conclusions from an analysis
- summarise key findings and conclusions concisely
- compose a written report presenting analysis results
- manage own work on a practical data analysis task
- collaborate with others to solve a data analysis problem
- develop reusable R code for other users to run analyses
- Flexible Regression
What you learn (14)
- formulate non-linear regression models for given data
- formulate non-parametric regression models for given data
- formulate generalised additive models for given data
- describe different smoothing methods used in regression analysis
- compare the properties of alternative smoothing techniques
- describe approaches for selecting smoothing parameters
- state formulas for calculating the degrees of freedom of a smoother
- apply smoothing methods to diverse practical regression problems
- describe methods for fitting advanced regression models
- apply hypothesis tests to compare competing regression models
- apply selection criteria to choose an appropriate regression model
- evaluate the goodness of fit of an advanced regression model
- fit advanced regression models using R software
- interpret output produced by R procedures for advanced regression models
- Principles of Probability and Statistics
What you learn (11)
- describe different modes of stochastic convergence
- contrast the relationships between modes of stochastic convergence
- state standard probabilistic inequalities
- prove standard probabilistic inequalities
- apply probabilistic inequalities to bound probabilities
- apply the weak law of large numbers and central limit theorem to problems
- state optimality properties of point estimators
- discuss the significance of optimality properties for estimator selection
- apply the rao-blackwell theorem and cramer-rao bound to estimator problems
- apply asymptotic results to analyse maximum-likelihood estimators
- construct an expectation-maximisation algorithm for missing data problems
- Statistics Project
What you learn (13)
- apply knowledge and skills from prior study to conduct extended data analysis
- describe the context and objectives of a statistical project
- formulate research questions using both technical and plain language
- explain how statistical methods address research questions
- identify suitable statistical techniques for a given problem
- devise an analysis plan and revise it as circumstances change
- apply chosen statistical methods using software and coding tools
- verify that model assumptions underlying chosen methods hold
- interpret analysis results and draw conclusions
- explain the implications and limitations of a statistical analysis
- deliver a clear structured oral presentation of project work
- produce a clear structured written report of project work
- manage independent work while using supervisory support appropriately
8 optional modules
- 4H: Mathematical Finance
What you learn (15)
- explain the concept of time value of money and interest rates
- perform compounding and discounting calculations
- describe basic financial products such as bonds and stocks
- apply futures contract terminology including long, short positions, hedging and arbitrage
- apply one-period model concepts to compute outcomes in finite market models
- describe terminology for call, put, American and European options
- create graphs illustrating option payouts
- explain how options are modelled within one-period markets
- derive price inequalities and assess market completeness for options
- apply these option pricing concepts to example problems
- describe the binomial (CRR) model structure
- calculate option prices using the binomial model
- derive the Black-Scholes model as a limit of discrete-time models
- price contingent claims using the Black-Scholes model
- apply risk measurement approaches to manage financial risk
- 4H: Theoretical Foundations of Machine Learning and Deep Learning
What you learn (12)
- prove significant theoretical results in artificial intelligence and machine learning
- explain the historical development of key ideas in artificial intelligence
- identify challenges arising from convex and non-convex optimisation in deep learning
- discuss how optimisation difficulties affect convergence and generalisation of models
- state fundamental regularisation methods used in deep learning
- explain the mathematical basis underlying regularisation techniques
- derive probabilistic machine learning results using Jensen's inequality
- derive probabilistic machine learning results using Kullback-Leibler divergence
- describe common deep learning architectures
- explain relationships between different deep learning architectures
- compare generative and discriminative modelling approaches
- evaluate the advantages and disadvantages of generative versus discriminative models
- Biostatistics
What you learn (7)
- calculate required sample size for a clinical trial
- fit a statistical model to clinical trial data
- apply epidemiological study designs to estimate disease rates
- estimate exposure effects on disease rates using study data
- estimate the survival function from sample survival data
- quantify covariate effects on the hazard function
- interpret results from statistical models applied to medical data
- Environmental and Ecological Statistics
What you learn (6)
- identify quality issues in environmental and ecological datasets
- identify sources of bias and uncertainty affecting data analysis
- evaluate the impact of bias on interpretation of results
- design statistical analyses for complex spatial and temporal environmental phenomena
- apply modern statistical methods to handle variability and uncertainty
- integrate large-scale environmental and ecological datasets for analysis
- Time Series (Level H)
What you learn (8)
- identify trend, seasonality and short-term correlation in time series data
- assess whether a time series is stationary
- describe the structure of ARIMA probability models
- calculate parameter estimates for ARIMA models
- evaluate whether model assumptions hold for a fitted ARIMA model
- forecast future values of a time series
- select models suitable for time series exhibiting sudden structural changes
- apply statistical software to analyse real time series data sets
- Functional Data Analysis
What you learn (12)
- identify scenarios where data can be treated as smooth functions
- apply functional data analysis techniques to suitable datasets
- design visualization strategies for exploring functional data
- implement nonparametric smoothing methods on functional data
- formulate different types of functional linear models
- fit functional linear models to data
- describe the steps of the functional principal component analysis algorithm
- apply functional principal component analysis to simple datasets
- devise methods for analysing derivatives of functional data
- apply derivative-based techniques to solve practical problems
- explain the principles underlying curve registration
- apply registration techniques as a pre-processing step in practical analyses
- Statistical Genetics
What you learn (11)
- describe fundamental concepts and terminology of genetics
- derive the Hardy-Weinberg equilibrium relationship mathematically
- test genetic data for conformity with Hardy-Weinberg equilibrium
- explain possible causes of deviation from Hardy-Weinberg equilibrium
- apply statistical methods to estimate allele frequencies
- use the EM algorithm to estimate genetic parameters
- apply Bayes' theorem to solve genetic counselling problems
- explain the coalescent process derived from Wright-Fisher dynamics
- apply coalescent theory to analyse genetic ancestry problems
- explain how recombination influences ancestral models
- estimate evolutionary parameters using ancestry models
- Spatial Statistics
What you learn (9)
- distinguish geostatistical, areal unit and point process data types
- apply descriptive methods to detect spatial autocorrelation in data
- define stationarity in spatial processes
- define isotropy in spatial processes
- identify nugget, range and sill parameters in a variogram model
- derive the kriging equations used for spatial prediction
- derive the formulation of conditionally autoregressive models
- assess whether a point process shows spatial structure
- fit spatial statistical models to varied spatial data using r software
Year 5 · 1 core, 8 optional · 80 credits ≈ 800 hours
- Statistics Research Project 5
What you learn (11)
- integrate prior statistical knowledge and skills to conduct advanced research
- explain major issues relevant to the chosen statistics project area
- plan an extended statistical research project
- carry out an extended statistical research project
- evaluate statistical issues underlying the research questions
- construct a literature review relevant to the research topic
- develop appropriate statistical methodology for the research question
- implement statistical methods using software and programming tools
- present research results in a structured thesis
- critically discuss and interpret statistical findings
- present research findings orally to an audience
8 optional modules
- Deep Learning (M)
What you learn (9)
- construct a fully connected deep neural network
- train a fully connected deep neural network on data
- apply a trained neural network to a given task
- implement vectorised neural networks using python
- explain the computational backends underlying neural network libraries
- apply deep learning techniques to novel problem domains
- describe the stages of a machine learning pipeline
- explain engineering challenges in assembling training data
- explain the role of unlabelled data in model training
- 4H: Theoretical Foundations of Machine Learning and Deep Learning
What you learn (12)
- prove significant theoretical results in artificial intelligence and machine learning
- explain the historical development of key ideas in artificial intelligence
- identify challenges arising from convex and non-convex optimisation in deep learning
- discuss how optimisation difficulties affect convergence and generalisation of models
- state fundamental regularisation methods used in deep learning
- explain the mathematical basis underlying regularisation techniques
- derive probabilistic machine learning results using Jensen's inequality
- derive probabilistic machine learning results using Kullback-Leibler divergence
- describe common deep learning architectures
- explain relationships between different deep learning architectures
- compare generative and discriminative modelling approaches
- evaluate the advantages and disadvantages of generative versus discriminative models
- Biostatistics
What you learn (7)
- calculate required sample size for a clinical trial
- fit a statistical model to clinical trial data
- apply epidemiological study designs to estimate disease rates
- estimate exposure effects on disease rates using study data
- estimate the survival function from sample survival data
- quantify covariate effects on the hazard function
- interpret results from statistical models applied to medical data
- Time Series (Level H)
What you learn (8)
- identify trend, seasonality and short-term correlation in time series data
- assess whether a time series is stationary
- describe the structure of ARIMA probability models
- calculate parameter estimates for ARIMA models
- evaluate whether model assumptions hold for a fitted ARIMA model
- forecast future values of a time series
- select models suitable for time series exhibiting sudden structural changes
- apply statistical software to analyse real time series data sets
- Functional Data Analysis
What you learn (12)
- identify scenarios where data can be treated as smooth functions
- apply functional data analysis techniques to suitable datasets
- design visualization strategies for exploring functional data
- implement nonparametric smoothing methods on functional data
- formulate different types of functional linear models
- fit functional linear models to data
- describe the steps of the functional principal component analysis algorithm
- apply functional principal component analysis to simple datasets
- devise methods for analysing derivatives of functional data
- apply derivative-based techniques to solve practical problems
- explain the principles underlying curve registration
- apply registration techniques as a pre-processing step in practical analyses
- Statistical Genetics
What you learn (11)
- describe fundamental concepts and terminology of genetics
- derive the Hardy-Weinberg equilibrium relationship mathematically
- test genetic data for conformity with Hardy-Weinberg equilibrium
- explain possible causes of deviation from Hardy-Weinberg equilibrium
- apply statistical methods to estimate allele frequencies
- use the EM algorithm to estimate genetic parameters
- apply Bayes' theorem to solve genetic counselling problems
- explain the coalescent process derived from Wright-Fisher dynamics
- apply coalescent theory to analyse genetic ancestry problems
- explain how recombination influences ancestral models
- estimate evolutionary parameters using ancestry models
- Spatial Statistics
What you learn (9)
- distinguish geostatistical, areal unit and point process data types
- apply descriptive methods to detect spatial autocorrelation in data
- define stationarity in spatial processes
- define isotropy in spatial processes
- identify nugget, range and sill parameters in a variogram model
- derive the kriging equations used for spatial prediction
- derive the formulation of conditionally autoregressive models
- assess whether a point process shows spatial structure
- fit spatial statistical models to varied spatial data using r software
- Environmental and Ecological Statistics (Level M)
What you learn (7)
- identify sources of bias in environmental and ecological datasets
- explain how data uncertainty affects analysis and interpretation
- design statistical analyses for spatial and temporal ecological data
- apply modern statistical methods to handle variability and large datasets
- communicate statistical findings to specialist and general audiences
- evaluate methodology and validity of claims in scientific publications
- critique data analysis techniques used in environmental reports
“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?
£31,500median pay three years after graduating
Lower quarter£27k
Median£32k
Upper quarter£33k
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 25 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 £27,000 and £32,500. Five years on, the median is £37,000. 80% 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 | 80% | 40 |
| In work or study, 15 months on0% studying | 80% | 20 |
| Professional or managerial job | 90% | 15 |
| Pay 15 months on, median | £33,500 | 10 |
| Pay 3 years on, median | £31,500 | 25 |
| Pay 5 years on, median | £37,000 | 25 |
National Student Survey 2025-26
What do students say?
73%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 67% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 40 students answered
Highest: 98% agreed on question 4; lowest: 73% on question 26.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 88% |
| Question 2 | 87% |
| Question 3 | 84% |
| Question 4 | 98% |
| Question 5 | 85% |
| Question 6 | 92% |
| Question 7 | 88% |
| Question 8 | 79% |
| Question 9 | 88% |
| Question 10 | 77% |
| Question 11 | 88% |
| Question 12 | 92% |
| Question 13 | 84% |
| Question 14 | 83% |
| Question 15 | 96% |
| Question 16 | 93% |
| Question 17 | 82% |
| Question 18 | 84% |
| Question 19 | 93% |
| Question 20 | 89% |
| Question 21 | 95% |
| Question 22 | 90% |
| Question 23 | 86% |
| Question 24 | 77% |
| Question 25 | 80% |
| Question 26 | 73% |
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 Glasgow’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 Glasgow is a trade mark of the university; sch.today is independent of it.