Data Science (BSc) - Course full
Our Data Science (BSc) 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.
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.
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 Mathematics and Statistics 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 Two: about 15% optional modules, Year 3: about 60% optional modules.
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 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 for core modules are around 220 students, though can be higher in some core modules joint with Mathematics degree students. Size of classes for optional modules varies; it can be as large as in core modules but it can be as low as 15 in specific topics in higher years. Support classes usually consist of 15 students.
You will be assessed by a combination of closed and open-book examinations, continuous assessment and project work, depending on your options. Your final year will contain a Data Science project. The first year counts 10%, the second year 30% and the third year 60% towards the final BSc 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 last two years at Warwick.
A level: A*AA to include A* in Mathematics and A in Further Mathematics or A*A*A + AS level A to include A* in Mathematics and A in AS level Further Mathematics or A*A*A* to include Mathematics or AAA to include Mathematics + 2 in STEP or Merit in AEA
IB: 38 overall to include 7 in Higher Level Mathematics or 38 overall to include 6 in Higher Level Mathematics and 2 in any STEP paper
Additional requirements: You will also need to meet our English Language requirements.
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). For full details of standard offers and conditions visit the IFP website.
- We welcome applications from students with other internationally recognised qualifications. For more information please visit the international entry requirements page.
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 use 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.
Analysis is the rigorous study of calculus. 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 and MA138, this builds your knowledge by introducing key notions of probability and developing your ability to calculate using probabilities and expectations. You’ll experiment with random outcomes through the notion of events and their probability, and look at examples of discrete and continuous probability spaces. You’ll learn counting methods (inclusion–exclusion formula and binomial co-efficients), and study theoretical topics including conditional probability and Bayes’ Theorem. Later, you’ll scrutinise important families of distributions and the distribution of random variables, and the light this shines on the properties of expectations. Finally, you’ll 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 and MA137, you’ll gain expertise in the everyday techniques of probability and statistics essential to your continued study. You’ll 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’ll work on eigenvalues and eigenvectors, diagonalisation, characteristic polynomials, constant co-efficient differential equations, and orthogonal bases and orthonormalisation. You’ll also study convergence and continuity in metric spaces to advance your mathematical thinking.
Mathematical Statistics Part A
If you have already completed ST115, on this module, you’ll have the opportunity to acquire the knowledge you need to study more advanced topics in probability. You’ll 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’ll 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.
Data Science Project
Selection of optional modules that current students are studying:Artificial Intelligence; Games, Decisions and Behaviour; Neural Computing; Machine Learning; Approximation and Randomised Algorithms; Mobile Robotics; Computer Graphics; Professional Practice of Data Analysis.
Graduates from courses related to Statistics have gone on to work for employers including: AXA Insurance, Deloitte, JCRA, JPMorgan, Lloyds Banking Group, MUFG Securities, Shell and The Financial Conduct Authority.
They have pursued roles such as: actuaries; business, research and administrative professionals; chartered and certified accountants; economists; IT business analysts, architects and systems designers; programmers and software development professionals; statisticians and secondary education teaching professionals.
Statistics graduates develop a strong range of transferable skills including excellent numerical, problem-solving and analytical abilities. These along with your ability to communicate complex ideas effectively are highly sought after by employers.
A number of students decide to continue in academia, studying for either a Statistics related Masters or PhD. Alternative study routes have included the study of Management Science & Operational Research or the PGCE teaching qualification.
Helping you find the right career
Our department has a dedicated professionally qualified Senior Careers Consultant who works within Student Careers and Skills to help you as an individual. Additionally your Senior Careers Consultant offers impartial advice and guidance together with workshops and events, tailored to our department, 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
- Becoming an Actuary Alumni Talk
Find out more about our Careers & Skills Services here.
Bachelor of Science (BSc)
3 years full-time
28 September 2020
Location of study
University of Warwick, Coventry
Find out more about fees and funding
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 will pay reduced tuition fees for their third year.
This information is applicable for 2020 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.
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