Data Science BSc
AAA at A level, Mathematics; graduates earn a median £40,000 three years on.
Typical A level offer
AAA
Mathematics · 97% of entrants came with A levels · 94% 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. if you are studying towards a fourth a level, we will make an alternative offer of aabb including grade 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
- 1Mathematics
- 2A in Mathematics
Who actually got in: 97% with A levels, 1% with other higher education. 7% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 280 entrants.
UCAS tariff points — a points scale for grades: A* = 56, A = 48, so A*A*A = 160. Entrants’ totals include extra subjects.
Other qualifications5
| Qualification | Typical offer | Required subjects |
|---|---|---|
| Access | We accept the Access to Higher Education Diploma. The syllabus must contain a siWe accept the Access to Higher Education Diploma. The syllabus must contain a significant portion of Mathematics that is considered equivalent to A level standard. Applications will be considered on a | — |
| 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 overall, including grade 6 in Higher Level Mathematics (either Analysis and Approaches or Applications and Interpretations). | 6 in Higher Level Mathematics |
| 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 47% · digital & ict skills 36% · engineering & manufacturing 8% · law & public administration 3%.
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
- Software 1: Foundations of Programming for Computer Science
What you learn (12)
- describe core concepts of procedural programming
- write small procedural programs to meet specified requirements
- relate program syntax to its semantics and analyse execution outcomes
- implement custom data structures to represent process states
- translate pseudocode algorithms into working code
- develop programs incrementally using tests to verify each stage
- store data using built-in collection types
- read and write data from CSV and JSON files
- use a software development environment to write and manage code
- identify and fix bugs in code using a debugging tool
- organise code according to software engineering principles
- document the design and implementation of code
- Software 2: Object Oriented Data Structures and Algorithms
What you learn (18)
- assemble data and methods into classes applying encapsulation
- integrate standard library code into own programs
- write code that satisfies given interface specifications
- test code to verify it meets interface requirements
- debug programs by identifying and fixing faults
- structure program code according to software engineering standards
- produce documentation explaining code design and implementation
- generate documentation automatically using programming tools
- analyse problems to design algorithms for simple tasks
- explain how algorithms and programs operate
- implement small programs based on algorithmic designs
- calculate worst-case running time using asymptotic analysis
- categorise sorting and searching algorithms by efficiency
- compare abstract data structures such as lists and graphs
- select appropriate data structures for design problems
- apply greedy and dynamic programming design paradigms
- justify choice of algorithmic paradigm for a problem
- explain the concept and benefit of approximation algorithms
- DATA: Introduction to Data Science
What you learn (12)
- distinguish between data types produced in science, engineering and design contexts
- apply strategies to ensure the quality of collected data
- retrieve data from diverse sources and file formats
- apply inferential statistical methods to test hypotheses about data
- analyse relationships and features within data sets using statistical procedures
- select suitable visualisations to present data findings
- use visual techniques to explore patterns within data sets
- use relational and non-relational databases to store data
- construct queries to retrieve data from different database systems
- identify ethical issues relating to data provenance and individual privacy
- evaluate the societal impact of data analytics practices
- apply principles from a professional data protection code of ethics
- 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
- Engineering 1: Software and Systems Engineering
What you learn (16)
- explain software engineering terminology and process models
- apply lifecycle and process models to support a project
- elicit user and system requirements from stakeholders
- document user and system requirements clearly
- design software architecture using object-oriented principles
- implement object-oriented software from a design
- estimate project risk to inform decision-making
- produce realistic project effort and time estimates
- use continuous integration tools in software development
- collaborate effectively within a team to complete a project
- apply software licensing models to project artefacts
- incorporate third-party software reuse into a project
- address cyber-security issues throughout the development lifecycle
- evaluate ethical considerations in software engineering practice
- write documentation for stages of the engineering lifecycle
- deliver presentations on project progress and outcomes
- Intelligent Systems: Machine Learning and Optimisation
What you learn (11)
- distinguish between strong, weak and general artificial intelligence
- describe the relationship between computation and artificial intelligence
- differentiate machine learning from the broader field of artificial intelligence
- calculate partial derivatives of multivariate functions
- apply linear regression to model relationships between variables
- apply logistic regression to classification problems
- explain the cause and effect of overfitting in machine learning models
- apply regularisation techniques to prevent overfitting
- train neural network weights using the backpropagation algorithm
- analyse ethical arguments concerning artificial intelligence applications
- evaluate privacy implications arising from machine learning systems
- Systems and Devices 2: Operating Systems, Security, and Networking
What you learn (13)
- write application code using OS-supported concurrency, communication and I/O features
- explain how hardware features extend basic OS structures introduced previously
- apply basic OS resource management mechanisms for time and memory allocation
- apply hardware and OS memory protection mechanisms including memory-mapped I/O
- apply OS file system mechanisms to enforce information security
- write concurrent programs at the process level
- explain how system hardware supports and implements process-level concurrency
- identify situations of deadlock, livelock and starvation in concurrent systems
- apply strategies to avoid deadlock, livelock and starvation
- explain the rationale behind the layered network model
- develop software that communicates with other systems using OS-level networking sockets
- describe how networked architectures integrate into an operating system
- develop simple networked applications using operating system networking knowledge
- 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 3 · 3 core, 20 optional · 80 credits ≈ 800 hours
- Computer Science project or Mathematics project
What you learn (15)
- apply advanced technical skills in a chosen specialist area building on theory
- design a computer systems engineering solution to a given problem
- implement a designed computer systems engineering solution
- evaluate the outcomes of an engineering project against its objectives
- apply established research methods to generate new knowledge
- interpret findings produced through recognised research techniques
- compare alternative approaches to a project problem
- justify the chosen approach at each project stage
- identify project tasks feasible within time and resource constraints
- structure a written project report clearly according to set criteria
- reference sources to a publishable academic standard
- critically evaluate the work undertaken and methods applied in the report
- explain legal, ethical, social and professional issues relevant to the project
- propose mitigation strategies for identified ethical or professional risks
- summarise the context, methods and results of a project for a general audience
- Governance of Data Science
What you learn (9)
- explain the objectives behind governing data science projects
- analyse the governance challenges arising in data science projects
- describe key governance frameworks relevant to data science practice
- critically analyse legislation and professional codes governing data science
- identify governance frameworks applicable to a given data science project
- interpret how specific governance frameworks apply to a project
- design a data science project that complies with relevant governance frameworks
- conduct data science work consistent with applicable governance requirements
- evaluate data science project design and delivery against governance standards
- Cloud Based Data Analysis
What you learn (8)
- explain core principles of cloud computing
- analyse benefits and challenges of adopting cloud computing
- critique the components and structure of a data ecosystem
- analyse the high-performance computing needs of big data processing
- build machine learning applications using cloud resources
- deploy big data analytics pipelines on cloud platforms
- evaluate and monitor the performance of deployed analytics pipelines
- design cloud-based advanced analytics services to meet organisational needs
20 optional modules
- Quantum Computation
What you learn (7)
- explain the potential advantages of quantum computation
- identify the limitations and constraints of quantum computation
- apply quantum gate operations to evolve quantum states
- calculate measurement outcomes for given quantum states
- design and analyse quantum computational circuits
- explain key quantum algorithms and their significance
- simulate quantum algorithms acting on quantum states
- Cryptography Theory and Practice or Cryptography
What you learn (8)
- explain core concepts of symmetric-key cryptography
- explain core concepts of public-key cryptography
- apply informal definitions of security properties to cryptographic constructions
- apply formal definitions of security properties to cryptographic constructions
- analyse the theoretical security of cryptographic constructions
- analyse the practical efficiency of cryptographic constructions
- assess whether a cryptographic algorithm meets specified security requirements
- assess whether a cryptographic protocol meets specified security requirements
- Deep Learning
What you learn (9)
- explain probability theory as a foundation for machine learning models
- describe manifold embedding techniques for dimensionality reduction
- apply kernel methods to transform data representations
- apply Bayesian regression methods to predict continuous outcomes
- apply Bayesian classification methods to categorise data
- apply Bayesian clustering methods to group unlabelled data
- identify key architectures used in deep learning
- apply gradient-based optimisation algorithms to train models
- compare variants of optimisation algorithms used in model training
- AI Search and Logic
What you learn (11)
- formulate a search problem using states, actions and a goal
- identify a suitable heuristic for a search problem
- translate a scenario into propositional logic statements
- apply logical inference to derive conclusions from encoded statements
- formulate a discrete optimisation problem as a constraint model
- solve a constraint model using a constraint programming tool
- justify the choice of search algorithm over alternatives
- select an adversarial search method suited to a given game
- design a heuristic to support adversarial search when required
- describe algorithms used in SAT, local search and constraint solvers
- apply solver algorithms to small worked examples
- Autonomous Robots
What you learn (7)
- describe levels of autonomy achievable in robotic systems
- discuss safety considerations in designing and deploying autonomous robots
- evaluate ethical implications of autonomous robotic system deployment
- explain methodologies used to engineer autonomous robotic systems
- describe how modern robotics middleware is applied in practice
- build an autonomous robotic system to solve a given task
- evaluate the strengths and weaknesses of a built robotic solution
- Engineering 2: Automated Software Engineering
What you learn (15)
- explain core concepts of modelling and metamodelling
- apply modelling practices to represent software systems
- build domain-specific models and metamodels for a given problem
- execute model management operations to coordinate models
- describe design principles underlying data-intensive software architectures
- evaluate architecture characteristics such as availability, scalability and security
- relate organisational characteristics to system architecture decisions
- structure data-intensive systems using independent microservices
- assess benefits and challenges of microservice-based architectures
- package system components as containers for reproducible deployment
- coordinate containers to operate together in a scalable system
- describe assurance practices for developing and deploying large systems
- identify testing and hardening techniques for data-intensive systems
- explain how model-driven engineering fits within broader system engineering processes
- relate model-driven engineering practices to organisational policies
- Qualitative Approaches to Investigating UX
What you learn (11)
- select an appropriate qualitative methodology for a research question
- justify the choice of a qualitative methodology
- identify the epistemological assumptions underlying qualitative methods
- explain how epistemological positions shape qualitative research choices
- explain the influence of the researcher's position on the research process
- evaluate research methods against standards of rigour and validity
- compare methods used across different qualitative studies
- present findings from a qualitative analysis appropriate to the chosen method
- discuss the implications of analysis results for the research question
- describe key principles of ethical conduct in research
- describe key principles of data governance in research
- Interaction Design and Evaluation
What you learn (12)
- justify adopting a user-centred approach in interaction design
- explain how well-designed technology can enhance everyday well-being
- explain how stakeholder requirements shape interactive system design
- identify diverse user needs including accessibility and cultural factors
- conduct conceptual design analysis of target users and tasks
- apply user-centred design methods to a realistic design problem
- build basic prototypes using varied interaction styles and technologies
- evaluate interactive systems for usability through expert inspection
- evaluate user experience through studies involving participants
- apply ethical principles when conducting research with human participants
- critique own interactive system design
- defend design and implementation decisions
- Natural Language Processing
What you learn (13)
- describe the process of splitting text into tokens
- explain how part-of-speech tagging assigns grammatical categories to words
- explain how parsing determines the syntactic structure of sentences
- build a text classification pipeline using NLP techniques
- build a named entity recognition system
- build a sentiment analysis system using NLP methods
- apply word embedding techniques to represent language meaning numerically
- apply n-gram models to estimate word sequence probabilities
- apply recurrent neural networks to model sequential language data
- evaluate NLP model outputs using standard performance metrics
- assess the limitations of NLP models using benchmark datasets
- describe how classical statistical methods contributed to large language model development
- describe how neural approaches underpin modern large language models
- Large Language Models
What you learn (9)
- critically assess chain-of-thought reasoning methods in modern models
- describe training, fine-tuning and alignment techniques used to develop language models
- evaluate explainability approaches and evaluation metrics for language models
- use modern software frameworks to implement language model applications
- discuss scalability challenges of deploying large-scale language models
- measure the energy consumption of training and deploying language models
- interpret environmental impact data to inform sustainable model development decisions
- identify ethical concerns relating to bias and fairness in language models
- address ethical issues of language model use across real-world scenarios
- Engineering LLM-Based Agents and Applications
What you learn (13)
- describe common architectural patterns for combining LLMs with planning and memory modules
- compare design patterns integrating retrieval and external tool use in LLM agents
- build an intelligent software agent using LLM components
- deploy a functioning agent system to a working environment
- design an autonomous workflow combining LLM reasoning with retrieval mechanisms
- construct multi-step action plans that link reasoning to execution
- implement a practical agent application such as a task assistant or planner
- integrate external APIs and services into an agent application
- evaluate agent performance using qualitative and quantitative measures
- analyse limitations of agent systems relating to reliability and latency
- identify safety and ethical risks arising in agent design
- propose mitigations for hallucination and over-reliance on automated agents
- explain how systems thinking supports evaluation of LLM performance in practice
- 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?
£40,000median pay three years after graduating
Lower quarter£32k
Median£40k
Upper quarter£54k
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 115 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 £32,000 and £53,500. Five years on, the median is £53,500. 90% were in work or further study 15 months after finishing; 88% of those in work were in professional or managerial jobs.
All outcome figures7
| Measure | Value | Based on |
|---|---|---|
| Continue after year 12% left | 94% | 190 |
| In work or study, 15 months on5% studying | 90% | 75 |
| Professional or managerial job | 88% | 60 |
| Pay 15 months on, median | £38,000 | 45 |
| Pay 3 years on, median | £40,000 | 115 |
| Pay 5 years on, median | £53,500 | 110 |
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: £33,500 a year (2027/28).
National Student Survey 2025-26
What do students say?
93%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 64% 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: 95% agreed on question 21; lowest: 48% on question 14.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 86% |
| Question 2 | 79% |
| Question 3 | 88% |
| Question 4 | 80% |
| Question 5 | 82% |
| Question 6 | 83% |
| Question 7 | 66% |
| Question 8 | 61% |
| Question 9 | 80% |
| Question 10 | 60% |
| Question 11 | 79% |
| Question 12 | 74% |
| Question 13 | 87% |
| Question 14 | 48% |
| Question 15 | 94% |
| Question 16 | 87% |
| Question 17 | 76% |
| Question 18 | 85% |
| Question 19 | 94% |
| Question 20 | 88% |
| Question 21 | 95% |
| Question 22 | 83% |
| Question 23 | 74% |
| Question 24 | 63% |
| Question 25 | 79% |
| Question 26 | 93% |
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.