This course is closed
for Clearing 2023
This course is closed for Clearing 2022
If you would like to study at Warwick, there are other courses available for 2024 entry.
Master of Science (MSci)
4 years full-time
27 September 2021
Department of Study
Department of Statistics
Location of Study
University of Warwick
Our Data Science (MSci) degree provides an essential mix of highly developed mathematical, statistical and computing skills for those interested in working at the forefront of the modern data revolution, that is, in a career which leverages advanced technology to extract value from data - or in developing such technology.
Data Science questions how to make sense of the vast volumes of data generated daily in modern life, from social networks to scientific research and finance. It then suggests sophisticated computing techniques for processing this deluge of information. These courses provide an essential mix of highly developed mathematical, statistical and computing skills for those interested in working at the forefront of the modern data revolution, that is, in a career which leverages advanced technology to extract value from data - or in developing such technology.
Taught by specialists from the departments of Statistics, Computer Science and Mathematics, you will develop expertise in specialist areas of machine learning, data mining and algorithmic complexity. Skills development in mathematical and statistical modelling, algorithm design and software engineering prepares you for other careers including manufacturing, pharmaceuticals, finance, telecoms and scientific research.
BSc and MSci are the same during the first two years, making it easy to reconsider your preference. Differences become apparent in the final years. In particular, the fourth year of the MSci degree offers a supervised Master's level project and the possibility to specialise in areas such as advanced statistical learning and big data, high-performance computing, algorithmic game theory, and computational biology and statistical genetics.
You will learn through a combination of lectures, small-group tutorials and practical sessions based in the Department's well-equipped undergraduate computing laboratory. A central part of learning in Data Science is problem solving.
The curriculum is built on the principle that module choices get more and more flexible as you progress through the degree. On top of that, you may choose to study additional options from an even wider range of modules.
Year One: 100% core, Year Two: about 15% optional modules, Year Three: about 60% optional modules, Year Four: about 75% optional modules
How will I learn?
The curriculum is divided up into modules consisting of lectures and assessments, which are often supplemented by smaller group teaching such as tutorials, supervisions and computer labs. Homework assignments for each module are often biweekly and the expectation is that students work hard trying to tackle problems covering a range of levels of difficulty.
Contact time is around 15 hours a week.
Class sizes vary from 15 students for selected optional modules up to 350 students for some core modules. Support classes usually consist of 15 students.
How will I be assessed?
You will be assessed by a combination of closed and open-book examinations, continuous assessment and project work, depending on your options. Your third year will contain a Data Science project and your fourth year will contain a research project. The first year counts 10%, the second year 20%, the third year 30% and the fourth year 40% towards the final MSci degree mark.
We support student mobility through study abroad programmes and all students have the opportunity to apply for an intercalated year abroad at one of our partner universities. The Study Abroad Team based in the Office for Global Engagement offers support for these activities, and the Department's dedicated Study Abroad Co-ordinator can provide more specific information and assistance.
You may additionally choose to spend an ‘intercalated’ year in an approved industry, business or university between your second and third or third and fourth years at Warwick.
General entry requirements
- A*AA to include A* in Mathematics and A in Further Mathematics
- OR AAA to include Mathematics + STEP (grade 2)
- 37 overall to include 7 in Higher Level Mathematics ‘Analysis and Approaches’
- OR 36 overall to include 6 in Higher Level Mathematics ‘Analysis and Approaches’ + STEP (grade 2)
- OR 36 overall to include 7 in Higher Level Mathematics ‘Applications and Interpretations’ + STEP (grade 2)
Alternative offers and additional requirements:
We welcome applications from students with other internationally recognised qualifications.
Contextual data and differential offers
Warwick may make differential offers to students in a number of circumstances. These include students participating in the Realising Opportunities programme, or who meet two of the contextual data criteria. Differential offers will be one or two grades below Warwick’s standard offer (to a minimum of BBB).
Warwick International Foundation Programme (IFP)
All students who successfully complete the Warwick IFP and apply to Warwick through UCAS will receive a guaranteed conditional offer for a related undergraduate programme (selected courses only).
Taking a gap year
Applications for deferred entry welcomed.
We do not typically interview applicants. Offers are made based on your UCAS form which includes predicted and actual grades, your personal statement and school reference.
Programming for Computer Scientists
On this module, whatever your starting point, you will begin your professional understanding of computer programming through problem-solving, and fundamental structured and object-oriented programming. You will learn the Java programming language, through practical work centred on the Warwick Robot Maze environment, which will take you from specification to implementation and testing. Through practical work in object-oriented concepts such as classes, encapsulation, arrays and inheritance, you will end the course knowing how to write programs in Java, and, through your ability to analyse errors and testing procedures, be able to produce well-designed and well-encapsulated and abstracted code.
Design of Information Structures
Following on from Programming for Computer Scientists, on the fundamentals of programming, this module will teach you all about data structures and how to program them. We will look at how we can represent data structures efficiently and how we can apply formal reasoning to them. You will also study algorithms that use data structures. Successful completion will see you able to understand the structures and concepts underpinning object-oriented programming, and able to write programs that operate on large data sets.
Mathematical Programming I
Operational Research is concerned with advanced analytical methods to support decision making, for example for resource allocation, routing or scheduling. A common problem in decision making is finding an optimal solution subject to certain constraints. Mathematical Programming I introduces you to theoretical and practical aspects of linear programming, a mathematical approach to such optimisation problems.
Linear algebra addresses simultaneous linear equations. You will learn about the properties of vector space, linear mapping and its representation by a matrix. Applications include solving simultaneous linear equations, properties of vectors and matrices, properties of determinants and ways of calculating them. You will learn to define and calculate eigenvalues and eigenvectors of a linear map or matrix. You will have an understanding of matrices and vector spaces for later modules to build on.
Calculus is the mathematical study of continuous change. In this module there will be considerable emphasis throughout on the need to argue with much greater precision and care than you had to at school. With the support of your fellow students, lecturers and other helpers, you will be encouraged to move on from the situation where the teacher shows you how to solve each kind of problem, to the point where you can develop your own methods for solving problems. By the end of the year you will be able to answer interesting questions like, what do we mean by `infinity'?
Sets and Numbers
It is in its proofs that the strength and richness of mathematics is to be found. University mathematics introduces progressively more abstract ideas and structures, and demands more in the way of proof, until most of your time is occupied with understanding proofs and creating your own. Learning to deal with abstraction and with proofs takes time. This module will bridge the gap between school and university mathematics, taking you from concrete techniques where the emphasis is on calculation, and gradually moving towards abstraction and proof.
Statistical Laboratory 1
If you’re studying ST115 (Introduction to Probability) or ST111/2 (Probability), this course supports your understanding of statistical analysis. You’ll lay foundations for applying mathematical probability, and learn to calculate using probabilities and expectations. You’ll become familiar with the R software package for exploratory data analysis, and gain experience of elementary simulation techniques on real data, and, using visualisations, be able to propose probabilistic models for simple data sets. You’ll also cover sampling technique (standard discrete and continuous distributions – Bernoulli, geometric, Poisson, Gaussian and gamma) and learn generic sampling methods for univariate distributions, preparing you to move on to ST221 (Linear Statistical Modelling).
Introduction to Probability
Following modules MA137 (Mathematical Analysis) and MA138 (Sets and Numbers), this builds your knowledge by introducing key notions of probability and developing your ability to calculate using probabilities and expectations. You will experiment with random outcomes through the notion of events and their probability, and look at examples of discrete and continuous probability spaces. You will learn counting methods (inclusion–exclusion formula and binomial coefficients), and study theoretical topics including conditional probability and Bayes’ Theorem. Later, you will scrutinise important families of distributions and the distribution of random variables, and the light this shines on the properties of expectations. Finally, you will examine mean, variance and co-variance of distribution, through Chebyshev's and Cauchy-Schwartz inequalities.
Want to think like a mathematician? This practical, problem-solving module is for you. Building on your A-level knowledge, you’ll develop a deeper understanding of mathematical concepts and relations, using problem-solving techniques such as visualisation and pattern exploration. Using concrete examples from counting, combinatorics, calculus, geometry and inequalities, you will learn to express mathematical concepts clearly and precisely and enhance your mathematical and logical reasoning and communication skills. By the end of the module, you’ll be able to comprehend, construct, visualise and present a coherent mathematical argument.
How does the theory of relational algebra serve as a framework for the efficient organisation and retrieval of large amounts of data? During this module, you will learn to understand standard notations (such as SQL) which implements relational algebra, and gain practical experience of database notations that are widely used in the industry. Successful completion will see you equipped to create appropriate, efficient database designs for a range of simple applications and to translate informal queries into formal notation. You will have learned to identify and express relative integrity constraints for particular database designs, and have gained the ability to identify control measures for some common security threats.
Data structures and algorithms are fundamental to programming and to understanding computation. On this module, you will be using sophisticated tools to apply algorithmic techniques to computational problems. By the close of the course, you’ll have studied a variety of data structures and will be using them for the design and implementation of algorithms, including testing and proofing, and analysing their efficiency. This is a practical course, so expect to be working on real-life problems using elementary graph, greedy, and divide-and-conquer algorithms, as well as gaining knowledge on dynamic programming and network flows.
Centred on teamwork, you will concentrate on applying software engineering principles to develop a significant software system with your peers from feasibility studies through modelling, design, implementation, evaluation, maintenance and evolution. You’ll focus on design quality, human–computer interaction, technical evaluation, teamwork and project management. With a deeper appreciation of the stages of the software life-cycle, you’ll gain skills to design object-oriented software using formal modelling and notation. You will be taught the principles of graphical user interface and user-centred design, and be able to evaluate projects in the light of factors ranging from technical accomplishment and project management, to communication and successful teamwork.
The concept of a stochastic (developing randomly over time) process is a useful and surprisingly beautiful mathematical tool in economics, biology, psychology and operations research. In studying the ideas governing sequential stochastic processes, you’ll learn about Markov chains, which use conditional probability for a widely applicable family of random processes; random walks – the building blocks for constructing other processes as well as being important in their own right – and renewal theory, for processes that ‘begin all over again’. Your understanding will extend to notions of behaviour, including transience, recurrence and equilibrium, and you will apply these ideas to problems in probability theory.
Following the algebraic modules MA106 (Linear Algebra) and MA137 (Mathematical Analysis), you will gain expertise in the everyday techniques of probability and statistics essential to your continued study. You will gain a grounding in optimisation, convergence, regression and best approximation. By the end of your course, expect to apply multivariate calculus (integration, calculation of under-surface volumes, variable formulae and Fubini’s Theorem) and to use partial derivatives, critical points and extrema, and to understand constrained optimisation. You will work on eigenvalues and eigenvectors, diagonalisation, characteristic polynomials, constant co-efficient differential equations, and orthogonal bases and orthonormalisation. You will also study convergence and continuity in metric spaces to advance your mathematical thinking.
Mathematical Statistics Part A
If you have already completed ST115 (Introduction to Probability), on this module, you will have the opportunity to acquire the knowledge you need to study more advanced topics in probability. You will study discrete, continuous and multivariate distributions in greater depth, and also learn about Jacobian transformation formula, conditional and multivariate Gaussian distributions, and the related distributions Chi-squared, Student’s and Fisher. In the second part, you will move on to more advanced topics, including moment-generating functions for random variables, convergence, and the Law of Large Number and the Central Limit Theorem.
Mathematical Statistics Part B
If you’ve completed Part A, this second-term module is your next step, where you’ll study the major ideas behind statistical inference, with an emphasis on likelihood methods of estimation, repeated sampling, and testing. You’ll learn to apply important models (such as the parametrised statistical model), hypothesis tests, linear models, estimators, and the Chi-squared goodness of fit. You’ll spend time calculating sampling distributions (Fisher’s theorem), and confidence intervals, and understand the principles of data reduction, point estimation and the notion of sufficient statistics. You’ll also become familiar with asymptotic normality and contingency tables, giving you a very firm foundation for your future engagement in advanced mathematical statistics.
BSc and MSci are the same during the first two years, making it easy to reconsider your preference. Differences become apparent in the final years. The third year of MSci involves a module whose aim is to prepare you for the statistical investigative cycle from problem formulation to the communication of conclusions. The fourth (final) year of MSci offers a range of advanced modules from across Data Science, and you also choose a Master's level dissertation project from a wide selection of topics.
Examples of optional modules/options for current students:
- Artificial Intelligence
- Games, Decisions and Behaviour
- Neural Computing
- Machine Learning
- Approximation and Randomised Algorithms
- Mobile Robotics
- Computer Graphics
- Professional Practice of Data Analysis
Additional course costs
There may be costs associated with other items or services such as academic texts, course notes, and trips associated with your course. Students who choose to complete a work placement or study abroad will pay reduced tuition fees for their third year.
We believe there should be no barrier to talent. That's why we are committed to offering a scholarship that makes it easier for gifted, ambitious international learners to pursue their academic interests at one of the UK's most prestigious universities. This new scheme will offer international fee-paying students 250 tuition fee discounts ranging from full fees to awards of £13,000 to £2,000 for the full duration of your Undergraduate degree course.
Recent graduates have pursued job roles such as:
- Actuaries, economists and statisticians
- Software developers
- Chartered and certified accountants
- Finance and investment analysts
- Telecommunication designers
- Data scientists and engineers
UK firms that have employed recent Warwick graduates from the Mathematics and Statistics Departments include:
- Adder Technology
- BlackRock International
- Merrill Lynch
- Civil Service
- Department of Health
- Ford Motor Company
- Fore Consulting
- Goldman Sachs
- Government Actuaries
- Jane Street Capital
- Met Office
- Ministry of Justice
- RenaissanceRe (Syndicate 1458)
- Oxford Clinical Trials Unit
- Solid Solutions
- Sword Apak
- Towers Watson
Helping you find the right career
Our department has a dedicated professionally qualified Senior Careers Consultant to support you. They offer impartial advice and guidance, together with workshops and events throughout the year. Previous examples of workshops and events include:
- Finding experience to boost your CV in Year One and Two
- Careers in Data Science and Artificial Intelligence
- Warwick careers fairs throughout the year
- Interview skills for Statistics students
- Maths and Stats Careers Fair
"This degree is just so versatile, you really can do whatever you want in computer science, mathematics, or statistics because you will be given a foundational background in all of those. It is also incredible employable, even at a bachelor’s level you will find what you study is immediately applicable in industry.
Companies are desperately looking for more people with analytical skills and also the capacity to program not just a small little prototype of a particular model but actually with the foundational knowledge you’ll have of computer programming concepts."
BSc Data Science
This information is applicable for 2021 entry. Given the interval between the publication of courses and enrolment, some of the information may change. It is important to check our website before you apply. Please read our terms and conditions to find out more.