Data Science MSci
A*A*A at A level, A* A* in Mathematics and Further Mathematics Or A*AA to include A* A; graduates earn a median £41,500 three years on.
Typical A level offer
A*A*A
A* A* in Mathematics and Further Mathematics Or A*AA to include A* A · 94% of entrants came with A levels · 89% stay on after year 1 · Warwick course page, Discover Uni
Year 13 · applying
What do you need to get in?
A*A*Atypical A level offer; the typical offer can be met by achieving any one of the following: a*a*a to include a* a* in mathematics and further mathematics or a*aa to include a* a (in any order) in mathematics and further math
Typical offer — the grades most applicants are asked for. Contextual offer: A*AB, for applicants from under-represented backgrounds.
Warwick course page, read 19 September 2026 · Discover Uni entry profile
- 1A* A* in Mathematics and Further Mathematics Or A*AA to include A* A
- 2A* A
- 3Useful subjects: A* in Mathematics and a in AS Further Mathematics Or A*AA to include A* in Mathematics and one of the following: STEP
Who actually got in: 94% with A levels, 5% with the IB. 39% arrived with 208 or more UCAS tariff points — roughly A*A*A or better. Based on 105 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 |
|---|---|---|
| IB | 39 pointsThe typical offer can be met by achieving any ONE of the following: 39 overall to include 7 in Higher Level Mathematics 'Analysis and Approaches' Or 38 overall to include 6 in Higher Level Mathematics | 6 in Higher Level Mathematics 'Analysis and Approaches' and one of the following: STEP; 6 in Higher Level Mathematics 'Applications and Interpretations' and one of the following: STEP |
| BTEC | —Level 3 BTECs will be considered alongside two A Levels including A Level Mathematics. | — |
| Advanced Highers | A1 in Advanced Higher Mathematics, A in one additional subject at Advanced HigheA1 in Advanced Higher Mathematics, A in one additional subject at Advanced Higher, and AAA in three additional Highers subjects. | — |
| Access | 45 Credits at Level 3, including Distinction in 33 Level 3 credits and Merit in We will consider applicants returning to study who are presenting a QAA-recognised Access to Higher Education Diploma on a case-by-case basis. Typically, we require 45 Credits at Level 3, including Di | — |
Years 1–4
What do you study?
11compulsory modules in year 1
Compulsory modules this year: mathematics & statistics 69% · digital & ict skills 23% · transversal skills 4% · business & administration 2%.
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.
Warwick course page · optional modules vary by year
Year 1 · 11 core · 120 credits ≈ 1,200 hours
- Programming for Computer Scientists
What you learn (8)
- describe the stages of the programming process from problem definition to solution design
- apply integrated testing practices while coding a solution
- write programs in a high-level language using imperative programming constructs
- structure software designs using encapsulation and abstraction concepts
- justify the correctness of a method or program using informal reasoning
- justify that a program terminates as intended
- apply different strategies to test software programs
- manage run-time errors using exception handling techniques
- Design of Information Structures
What you learn (9)
- identify standard abstract data types used in programming
- describe how standard ADTs support common programming tasks
- assess the time and space complexity of simple algorithms
- evaluate the correctness of simple algorithms
- select appropriate algorithms to solve simple problems
- design user-defined abstract data types for a specific application
- develop algorithms associated with custom data structures
- apply software engineering principles within a larger software project
- use data structures as the foundation for software project design
- Mathematical Programming 1
What you learn (8)
- identify business problems suited to optimisation modelling
- formulate a mathematical model representing a business optimisation problem
- solve mathematical optimisation problems using appropriate techniques
- list the underlying assumptions of key optimisation models
- critique the validity of assumptions made in optimisation models
- evaluate the limitations of different optimisation models
- explain the business implications of an optimal solution
- communicate quantitative results clearly to a business audience
- Sets and Numbers
What you learn (4)
- apply properties of standard number systems
- use set notation and quantifiers accurately
- describe functions and relations using formal notation
- apply different proof techniques to mathematical statements
- Calculus 1
What you learn (6)
- describe the structure and properties of the real number system
- explain the concept of infinity as a mathematical symbol
- calculate limits of sequences and series
- distinguish between conditional and absolute convergence of series
- identify properties of continuous functions
- explain the concept of absolute continuity of functions
- Calculus 2
What you learn (6)
- define differentiability and higher-order derivatives of functions
- describe key properties of differentiable functions
- apply Taylor's theorem to approximate functions using series
- explain when a function can be represented by a power series
- apply fundamental properties of integrals to solve problems
- apply the fundamental theorem of calculus to evaluate integrals
- Vectors and Matrices
What you learn (10)
- explain the concept of a vector space
- determine whether a set of vectors is linearly independent
- identify a basis and calculate the dimension of a vector space
- explain the concept of a linear transformation
- perform row and column operations to reduce a matrix
- apply matrix reduction techniques to solve systems of linear equations
- calculate the determinant of an n by n matrix
- calculate eigenvalues and eigenvectors of a matrix
- interpret the geometric meaning of eigenvalues and eigenvectors
- describe linear transformations between euclidean spaces
- Refresher Mathematics
What you learn (4)
- apply core A-level mathematical techniques to solve problems
- apply selected further mathematics concepts in problem solving
- explain the structure of mathematical definitions, theorems and proofs
- construct logical mathematical proofs using standard methods
- Introduction to Statistical Modelling
What you learn (14)
- formulate a probabilistic model for a given dataset
- derive a likelihood function corresponding to a simple model
- compute parameter estimates from observed data using a model
- quantify the uncertainty associated with estimated parameters
- compare predicted and observed data using graphical methods
- explain the assumptions underlying a statistical model
- interpret statistical model output to guide decisions
- write scripts in R to perform data analysis tasks
- produce data visualisations using the r environment
- discuss ethical issues in data collection and analysis
- evaluate ethical implications of communicating statistical results
- collaborate with peers to integrate diverse ideas into a shared task
- divide a complex task into manageable components for a team
- critique contributions from team members constructively
- Probability 1
What you learn (8)
- interpret standard mathematical notation
- apply problem solving techniques to mathematical tasks
- use visualisation methods to support mathematical reasoning
- construct concise and rigorous mathematical arguments
- calculate probabilities using appropriate methods
- interpret results of probability calculations
- explain key concepts of random variables
- interpret properties of probability distributions
- Probability 2
What you learn (5)
- calculate probabilities for random variables
- compute expectations of random variables
- explain fundamental concepts of probability theory
- explain theoretical concepts underlying probability distributions
- describe how randomness is incorporated into mathematical models
Year 2 · 8 core, 10 optional · 95 credits ≈ 950 hours
- Database Systems
What you learn (7)
- design efficient database schemas for various application scenarios
- implement a database design using SQL statements
- convert natural language queries into formal query notation
- formulate queries in standard database notations for common tasks
- specify integrity constraints appropriate to a database design
- identify common security threats affecting databases
- select suitable control measures to mitigate database security risks
- Algorithms
What you learn (8)
- apply algorithm design techniques to solve computational problems
- apply data structure design techniques within algorithmic solutions
- describe common data structures and their properties
- use appropriate data structures when designing and implementing algorithms
- describe techniques for designing efficient algorithms
- prove the correctness of an algorithm
- analyse the computational efficiency of an algorithm
- identify fundamental algorithmic problems and their solution methods
- Software Engineering
What you learn (8)
- explain the stages involved in the software life cycle
- describe different software development methodologies
- design object oriented software using a formal modelling notation
- explain key principles of graphical user interface design
- explain key principles of user-centred design
- identify practical challenges in developing a significant software system
- identify practical challenges in implementing a graphical user interface
- describe practical challenges of working within a software development team
- Stochastic Processes
What you learn (7)
- describe the concept of a markov chain
- apply conditional probability and matrix methods to discrete-time markov chains
- analyse long-run behaviour of markov chains including transience and recurrence
- determine equilibrium distributions of markov chains
- apply markov chain concepts to solve basic applied problems
- evaluate the suitability of markov chains as models for given situations
- present mathematical solutions using clear and logical argumentation
- Mathematical Methods for Statistics and Probability
What you learn (7)
- evaluate multiple integrals using suitable mathematical techniques
- solve optimisation problems by selecting appropriate methods
- examine properties of matrices and vector spaces
- apply spectral theory to identify structural features of matrices
- compare strategies for solving mathematical and probabilistic problems
- assess the effectiveness of methods used in multidimensional problems
- present mathematical solutions using clear and logical reasoning
- Probability for Mathematical Statistics
What you learn (7)
- derive properties of averages of random variables
- explain the significance of the central limit theorem in statistics
- describe joint and conditional probability distributions
- calculate conditional expectation and conditional variance
- derive properties of the multivariate normal distribution
- apply properties of distributions related to the normal distribution
- present mathematical solutions using clear and logical arguments
- Mathematical Statistics
What you learn (9)
- describe core concepts of statistical inference such as models, estimators and tests
- explain the uses and limitations of statistical inference techniques
- calculate maximum likelihood estimators for given statistical models
- apply likelihood ratios to compare competing statistical models
- construct hypothesis tests using likelihood ratio methods
- derive properties of sampling distributions for various estimators
- evaluate models using frequentist model selection criteria
- evaluate models using Bayesian model selection criteria
- present mathematical solutions using clear and logically structured arguments
- Linear Statistical Modelling with R
What you learn (11)
- define a normal linear model
- describe the assumptions underlying linear models
- derive statistical properties of estimators in linear models
- calculate confidence intervals for linear model parameters
- conduct hypothesis tests on linear model parameters
- fit regression models using R software
- check diagnostic adequacy of a fitted regression model
- improve and compare alternative regression models
- interpret the output and parameters of linear models
- evaluate the appropriateness of different linear models
- present statistical solutions using clear structured arguments
10 optional modules
- Digital Communications and Signal Processing
What you learn (7)
- describe the stages involved in transmitting information between parties
- explain key control challenges encountered in communication networks
- explain core concepts underlying digital signal processing
- identify typical real-world applications of digital signal processing
- design a simple digital signal processing algorithm
- implement a digital signal processing algorithm in software
- analyse the performance of a digital signal processing algorithm
- Artificial Intelligence
What you learn (11)
- explain the concept of rational agency in artificial intelligence
- describe uninformed search strategies for problem solving
- apply heuristic search techniques to solve problems
- apply adversarial search methods to game-playing problems
- describe methods for representing knowledge in intelligent systems
- apply basic logical reasoning techniques to derive conclusions
- apply planning approaches to achieve defined goals
- explain the fundamentals of machine learning approaches
- differentiate supervised and unsupervised learning methods
- describe methods for representing uncertainty in intelligent systems
- apply probabilistic reasoning techniques to handle uncertain information
- Data Analytics
What you learn (7)
- explain core methodologies and tools used to derive insights from data
- apply fundamental data analysis principles to extract meaningful insights
- describe data-driven modelling approaches across different disciplines
- apply machine learning methods to analyse datasets
- use big data mining techniques to prepare for advanced project work
- develop programming skills to implement data analytic solutions
- apply theoretical data analytics knowledge to real-world case studies
- Introduction to Secondary Mathematics Teaching
What you learn (5)
- evaluate key issues in school mathematics education
- reflect critically on debates relevant to teaching mathematics
- reflect critically on personal practice in teaching mathematics
- apply learning theories to mathematics teaching practice in schools
- demonstrate professional skills to support pupils' mathematics learning
- Mathematical Programming 2
What you learn (8)
- identify business problems suitable for optimisation modelling
- formulate business problems as mathematical optimisation models
- solve optimisation problems using spreadsheet software
- explain optimal solutions in business terms
- list the assumptions underlying key optimisation models
- critique the assumptions underlying key optimisation models
- evaluate the limitations of studied optimisation models
- evaluate the limitations of studied optimisation models
- Metric Spaces
What you learn (11)
- define key properties of normed, metric and topological spaces
- apply theorems about metric and topological spaces to solve problems
- perform calculations involving distances and norms in metric spaces
- compare the open-set definition of continuity with other definitions
- apply continuity concepts to analyse open and closed sets
- describe fundamental concepts of compactness, connectedness and completeness
- apply theorems relating to compactness, connectedness and completeness
- perform calculations involving compactness, connectedness and completeness
- analyse relationships between compactness, connectedness and completeness
- evaluate how continuous maps preserve compactness, connectedness and completeness
- apply compactness, connectedness and completeness concepts to simple problems
- Mathematical Analysis 3
What you learn (7)
- compute contour integrals using cauchy's integral formulas
- apply cauchy's integral formulas to solve related problems
- distinguish uniform convergence from pointwise convergence of functions
- describe properties of the limit function of a convergent sequence
- apply the cauchy-riemann equations to test complex differentiability
- manipulate complex power series to represent analytic functions
- represent derivatives of multivariable functions as linear maps
- Games and Decisions
What you learn (7)
- compare mathematical foundations of alternative probability theories
- analyse philosophical basis of different probability approaches
- describe key characteristics of normative decision theory
- analyse decision-making models applied to real-world examples
- apply game theory to solve simplified mathematical games
- evaluate limitations of game theory in real-world scenarios
- present problem solutions using clear structured arguments
- Python for data-analytic tasks
What you learn (11)
- design a python program to solve a given problem
- write readable and valid python code
- build modular code structures for reuse
- verify code produces reliable results
- use python tools to manage and store data
- create data visualisations using python libraries
- apply python techniques to analyse datasets
- apply python techniques to solve optimisation problems
- document python code clearly for others to use
- produce reproducible outputs as python modules or packages
- collaborate with others to share coding work
- Visualisation and Communication of Data
What you learn (4)
- evaluate the quality and effectiveness of existing data visualisations
- produce improved versions of poorly designed visualisations using R
- select suitable visualisation types for specific data and tasks
- design original visualisations in R based on a dataset and specification
Year 3 · 2 core, 12 optional · 45 credits ≈ 450 hours
- Data Science Project
What you learn (9)
- plan a significant individual project including resources and timetable
- manage project work while considering professional issues
- design and build a substantial software system with documentation
- analyse data from hypotheses to supported conclusions
- conduct a research project from methodology design to conclusions
- deliver an oral presentation using audio-visual and interactive aids
- respond effectively to audience questions during a presentation
- write a substantial technical report describing project outcomes
- reflect critically on one's own project experience in writing
- Professional Practice of Data Analysis
What you learn (9)
- describe the stages of a statistical investigative cycle
- carry out a statistical investigation on a straightforward problem
- generate numerical summaries in R to support data analysis
- create graphical visualisations in R to support data analysis
- write a report presenting the findings of a statistical analysis
- present analysis results through an online format
- explain key approaches to teamwork and leadership
- identify common issues arising in team communication
- collaborate productively within a team on a shared task
12 optional modules
- Complexity of Algorithms
What you learn (7)
- describe different complexity classes of computational problems
- distinguish between problems belonging to different complexity classes
- apply formal methods to prove a problem is solvable
- apply formal methods to prove a problem is unsolvable
- prove formal bounds on the time required to solve a problem
- prove formal bounds on the memory required to solve a problem
- design simplified versions of computationally hard problems that are easier to solve
- Computer Graphics
What you learn (4)
- explain the mathematical foundations of geometric transformations
- describe techniques for modelling three-dimensional objects
- describe techniques used to approximate realistic image generation
- explain how graphics programming standards implement rendering techniques
- Neural Computing
What you learn (5)
- explain the fundamental principles underlying neural networks
- identify typical application areas of neural networks
- design a simple neural network to solve a given problem
- implement a simple neural network using appropriate software tools
- analyse the behaviour and performance of a neural network
- Advanced Databases
What you learn (11)
- explain challenges of concurrency control in database systems
- describe parallelism issues in data management systems
- formulate database queries using relational algebra
- write equivalent queries using sql
- design data storage structures for a given dataset
- select appropriate indexing strategies for efficient retrieval
- describe principles of nosql database systems
- explain distributed processing models such as mapreduce and pig latin
- identify suitable storage methods for temporal data
- identify suitable storage methods for multimedia data
- identify suitable storage methods for spatial data
- Digital Forensics
What you learn (5)
- describe methods for acquiring image and video data for forensic analysis
- explain computational approaches used to detect image or video forgery
- select an appropriate computational technique to verify image or video authenticity
- design a computational method to confirm or refute media authenticity
- apply chosen computational techniques to assess authenticity of image or video files
- Approximation and Randomised Algorithms
What you learn (6)
- design approximation algorithms using appropriate mathematical techniques
- analyse the performance guarantees of approximation algorithms
- design randomised algorithms using appropriate mathematical techniques
- analyse the expected performance of randomised algorithms
- construct faster algorithms offering provable but weaker guarantees than exact methods
- compare trade-offs between exact and approximate algorithmic solutions
- Bayesian Statistics and Decision Theory
What you learn (11)
- describe the construction and evaluation process for Bayesian models
- explain naive Bayes model assumptions and calibration issues
- perform prior to posterior updating for basic statistical models
- apply discrete prior to posterior inference to simple problems
- carry out beta and Dirichlet conjugate analysis
- explain the theoretical basis of utility theory
- apply utility theory to multi-attribute decision problems
- elicit a utility function from decision-maker preferences
- model complex systems using conditional independence structures
- explain how directed acyclic graphs structure probabilistic models
- estimate probabilities in DAGs using product Dirichlet conjugate distributions
- Multivariate Statistics
What you learn (12)
- construct graphical displays of multivariate datasets
- interpret graphical displays to describe multivariate data patterns
- apply principal component analysis to reduce data dimensionality
- summarise high-dimensional datasets using derived components
- perform cluster analysis on multivariate data
- characterise identified subgroups within a population
- apply classification methods to assign individuals to groups
- evaluate discrimination techniques for group assignment accuracy
- conduct statistical inference for multivariate mean vectors
- construct confidence regions for multivariate parameters
- explain the use of confidence regions in comparing groups
- describe advanced multivariate statistical techniques covered in lectures
- Bayesian Forecasting and Intervention
What you learn (7)
- explain recurrence relations used in forecasting models
- calculate forecasting recurrences for specific special cases
- select a suitable forecasting model for a given simple scenario
- describe key dynamic linear models used for business forecasting
- apply intervention techniques to adjust forecasts using external information
- describe diagnostic methods for assessing dynamic linear models
- explain estimation techniques used within this model class
- Topics in Data Science
What you learn (4)
- explain key concepts of the selected data science topics
- identify computational challenges arising in data analysis
- apply techniques designed to address data analysis challenges
- evaluate the application of selected data science topics
- Generalised Linear Models for Regression and Classification
What you learn (9)
- identify a suitable regression model for a given response variable type
- select and fit a statistical model following a systematic procedure
- check whether a fitted model's assumptions hold
- quantify uncertainty in estimated model parameters
- quantify uncertainty in model predictions
- explain the core assumptions underlying generalised linear models
- extend a standard GLM to handle overdispersion
- explain the theoretical foundations of generalised linear models
- apply computational methods to fit generalised linear models
- Machine Learning frameworks
What you learn (7)
- select appropriate statistical learning approach for a given problem
- design data-analytic pipelines for statistical learning tasks
- build machine learning and deep learning models using python frameworks
- write well-documented and reproducible python code
- share code and results collaboratively with others
- interpret results from a statistical analysis
- communicate analytical findings to diverse audiences
Year 4 · 1 core, 16 optional · 30 credits ≈ 300 hours
- Data Science Masters Dissertation
What you learn (8)
- conduct a literature review on a chosen data science topic
- plan a project schedule for an advanced data science investigation
- develop an advanced data science project from design to completion
- explain an advanced data science topic or technique in depth
- apply relevant theoretical and methodological approaches to a research problem
- evaluate the selection and interpretation of methods and literature used
- present research findings clearly to a specialist audience
- write an extended academic document with correct source referencing
16 optional modules
- High Performance Computing
What you learn (6)
- explain the contribution of high performance computing to scientific and engineering work
- identify common paradigms used for parallel programming
- describe commonly used HPC platforms including cluster systems
- apply methods to measure the performance of HPC applications
- analyse the performance results of HPC applications
- describe how HPC management software handles administration, workload and resources
- Agent Based Systems
What you learn (3)
- describe methodologies for designing multi-agent systems
- analyse interactions between agents in competitive and cooperative settings
- apply theoretical and practical approaches to multi-agent system analysis
- Algorithmic Game Theory
What you learn (8)
- describe standard models used to represent strategic games
- explain key solution concepts used in cooperative and non-cooperative games
- describe algorithms used to compute game-theoretic equilibria
- explain complexity results associated with computing equilibria
- apply known solution concepts to analyse unfamiliar game variants
- apply equilibrium algorithms and complexity results to novel games
- summarise current research developments in algorithmic game theory
- identify open problems within algorithmic game theory research
- Image and Video Analysis
What you learn (9)
- apply convolutional neural networks to image analysis problems
- apply convolutional neural networks to video analysis problems
- describe core concepts underlying image and video analysis
- identify practical applications of image and video analysis techniques
- produce a written lab report documenting experimental work
- use Python toolboxes to implement image processing solutions
- interpret mathematical foundations of image processing algorithms
- program solutions to real-world image and video analysis problems
- evaluate the effectiveness of multimedia applications
- Optimisation Methods
What you learn (8)
- identify core techniques from convex optimisation
- describe discrete and combinatorial optimisation techniques
- apply linear algebra tools relevant to optimisation
- describe widely used optimisation methods in applied settings
- explain how optimisation methods are used in machine learning
- explain the mathematical theory underpinning optimisation methods
- implement optimisation algorithms in software
- apply optimisation methods to solve real-world problems
- Computational Biology
What you learn (12)
- describe high-throughput imaging methods for tissue slides
- identify key molecular biology concepts relevant to computational biology
- explain common algorithms used in bioinformatics
- describe algorithms for computing sequence alignments
- relate sequence alignment algorithms to current research applications
- identify common challenges in processing tissue images
- describe standard solutions to tissue image analysis problems
- apply image analysis techniques to computational pathology problems
- apply machine learning methods to computational pathology problems
- explain principles of fluorescence microscopy
- describe image-based modelling approaches for single cell dynamics
- apply mathematical techniques to model cellular dynamics
- Applied Stochastic Processes with Advanced Topics
What you learn (5)
- formulate continuous-time Markov chain models for real-world problems
- apply basic theory to quickly determine equilibrium distributions of reversible chains
- calculate transition probabilities for Markov chains on finite state spaces
- apply Markov chain methods to model and analyse queueing systems
- apply Markov chain methods to model and analyse epidemic spread
- Monte Carlo Methods
What you learn (7)
- describe a range of simulation methods including Markov chain Monte Carlo
- explain the procedures underlying Monte Carlo simulation
- design an MCMC algorithm tailored to a specified probability distribution
- implement an MCMC algorithm using computational tools
- assess the computational efficiency of a stochastic simulation algorithm
- judge the validity of inference results produced by a simulation algorithm
- apply Monte Carlo methods to solve scientific problems
- Medical Statistics with Advanced Topics
What you learn (5)
- apply generalised linear models to medical datasets
- apply survival analysis methods to medical datasets
- interpret results from generalised linear models and survival analysis
- explain the contribution of statistics to medical research
- describe statistical principles underpinning good practice in medical investigations
- Designed Experiments with Advanced Topics
What you learn (8)
- describe core concepts underlying designed experiments
- explain how experimental design links to the linear model
- relate the linear model to analysis of experimental data
- estimate model parameters from experimental data
- perform analysis of variance on experimental designs
- differentiate between types of experimental design
- evaluate the efficiency and usefulness of different designs
- carry out diagnostic checks on experimental results
- Dynamic Stochastic Control
What you learn (7)
- formulate solution approaches for stochastic control and optimal stopping problems
- derive explicit solutions to simple Hamilton-Jacobi-Bellman equations
- compute numerical solutions to Hamilton-Jacobi-Bellman equations
- apply stochastic control methods to mathematical finance problems
- apply stochastic control methods to reinforcement learning problems
- select appropriate reinforcement learning algorithms for a given problem
- apply basic reinforcement learning algorithms to solve problems
- Advanced Topics in Data Science
What you learn (6)
- explain core concepts of the selected advanced data science topics
- identify computational challenges arising in data analysis
- apply techniques designed to address data analysis challenges
- evaluate the application of selected data science topics
- describe current research developments in selected data science topics
- critique recent research developments in selected data science topics
- Statistical Learning and Big Data
What you learn (9)
- explain core concepts of statistical learning
- critically discuss statistical learning methods and their limitations
- apply statistical learning tools to analyse data sets
- analyse key challenges in handling large-scale data sets
- discuss fundamental tools used for modelling big data
- implement prediction methods using data partitioning techniques
- evaluate the performance of partition-based predictive models
- apply sparsity and regularisation techniques to large data sets
- apply error rate control methods in high-dimensional data analysis
- Statistical Consulting
What you learn (11)
- select suitable statistical methods for a given dataset
- apply chosen statistical techniques to analyse data
- interpret the results of statistical analysis
- explain statistical concepts to non-specialist audiences
- present statistical findings clearly in written or spoken form
- use statistical software to perform data analysis
- evaluate the suitability of different software packages for analysis
- identify stages of team development
- define roles and responsibilities within a team
- apply critical thinking to solve practical statistical problems
- formulate evidence-based recommendations from analysis
- Introduction to Advanced Probability
What you learn (6)
- evaluate measure-theoretic techniques for probability problems
- apply measure-theoretic methods to calculate event probabilities
- formulate formal definitions of probability concepts
- compute probabilities, independence and expectation for given scenarios
- apply probability concepts across varied situations
- evaluate data to construct probability models for random experiments
- Time Series
What you learn (9)
- apply techniques for analysing time series data
- evaluate different time series analysis techniques
- conduct exploratory analysis of time series data
- produce a descriptive analysis of time series data for applications such as finance
- derive statistical properties of linear time series models
- derive statistical properties of nonlinear time series models
- apply nonlinear modelling approaches to diverse time series situations
- design a time series modelling study including model selection and fitting
- apply forecasting and simulation methods to validate time series models
“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?
£41,500median pay three years after graduating
Lower quarter£34k
Median£42k
Upper quarter£57k
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 50 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 £34,000 and £57,000. Five years on, the median is £59,500. 85% 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 19% left | 89% | 105 |
| In work or study, 15 months on0% studying | 85% | 20 |
| Professional or managerial job | 90% | 15 |
| Pay 15 months on, median | £40,000 | 10 |
| Pay 3 years on, median | £41,500 | 50 |
| Pay 5 years on, median | £59,500 | 45 |
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.
Warwick fees page · source
About £40,200 in fees over 4 years, covered by a tuition fee loan you repay only when earning above the threshold.
National Student Survey 2025-26
What do students say?
95%agreed the course was good overall (question 26)
NSS — a yearly survey of final-year students at every UK university; 75% of this course’s final year answered. Small groups can swing a lot year to year.
National Student Survey, via Discover Uni · 175 students answered
Highest: 99% agreed on question 1; lowest: 71% on question 25.
All questions26
| Question | Agree |
|---|---|
| Question 1 | 99% |
| Question 2 | 85% |
| Question 3 | 95% |
| Question 4 | 95% |
| Question 5 | 87% |
| Question 6 | 94% |
| Question 7 | 88% |
| Question 8 | 84% |
| Question 9 | 85% |
| Question 10 | 78% |
| Question 11 | 87% |
| Question 12 | 87% |
| Question 13 | 79% |
| Question 14 | 75% |
| Question 15 | 93% |
| Question 16 | 94% |
| Question 17 | 90% |
| Question 18 | 93% |
| Question 19 | 98% |
| Question 20 | 92% |
| Question 21 | 96% |
| Question 22 | 94% |
| Question 23 | 89% |
| Question 24 | 88% |
| Question 25 | 71% |
| Question 26 | 95% |
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 Warwick’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 Warwick is a trade mark of the university; sch.today is independent of it.