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Modern societies produce huge amounts of data, which is only useful if we can analyse it and gain practical insights. Data science combines powerful computing technology, sophisticated statistical methods, and expert subject knowledge to carry out this analysis. An emerging field in recent decades, data science is now an exciting, fulfilling and high-profile career choice.
Our specialist BSc Data Science programme combines the expertise of internationally-renowned statisticians and mathematicians from the School of Mathematics, Statistics and Actuarial Science and computer scientists and machine learners from the School of Computing to ensure that you develop the expertise and quantitative skills required for a successful future career in the field.
On this new programme you gain a systematic understanding of key aspects of knowledge associated with data science and the capability to deploy established approaches accurately. You learn to analyse and solve problems using a high level of skill in calculation and manipulation of the material in the following areas: data mining and modelling, artificial intelligence techniques/statistical machine learning and big data analytics.
You also learn how to apply key aspects of big data science and artificial intelligence/statistical machine learning in well-defined contexts. In addition, you plan and develop a project themed in a data science area such as business, environment, finance, medicine, pharmacy and public health.
If you want to gain paid industry experience as part of your degree programme, our Data Science with a Year in Industry programme may be for you. If you decide to take the Year in Industry, our Placements Team will support you in developing the skills and knowledge needed to successfully secure a placement through a specialist programme of workshops and events.
The School of Computing and the SMSAS have had rich experience in running industrial placement related BSc programmes with a wide range of links to industry, currently holding the top two largest placement student groups in the University.
Facilities to support the study of Data Science include The Shed, the School of Computing's Makerspace. You have access to a range of professional mathematical, statistical and computing software such as:
You join a thriving student culture, with students from all degree programmes and all degree stages participating in student activities and taking on active roles within the University. As astudent you benefit from free membership of the Kent Maths Society and Invicta Actuarial Society.
You can also become a Student Rep and share the views of your fellow students to bring about changes. You could be employed as a Student Ambassador, earning money while you study by inspiring the next generation of mathematicians. Or join one of the society committees and organise socials and events for CEMS students.
You are more than your grades
At Kent we look at your circumstances as a whole before deciding whether to make you an offer to study here. Find out more about how we offer flexibility and support before and during your degree.
The University will consider applications from students offering a wide range of qualifications. Typical requirements are listed below. Students offering alternative qualifications should contact us for further advice.
Please note that meeting this typical offer/minimum requirement does not guarantee an offer being made. Please also see our general entry requirements.
If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.
BBB including Mathematics grade B (not Use of Mathematics). General Studies or Critical Thinking (but not both) can be accepted against the requirements
Mathematics grade 4/C
The University will not necessarily make conditional offers to all Access candidates but will continue to assess them on an individual basis.
If we make you an offer, you will need to obtain/pass the overall Access to Higher Education Diploma and may also be required to obtain a proportion of the total level 3 credits and/or credits in particular subjects at merit grade or above.
The University will consider applicants holding BTEC National Diploma and Extended National Diploma Qualifications (QCF; NQF; OCR) on a case-by-case basis. Please contact us for further advice on your individual circumstances.
30 points overall or 15 points at HL with Mathematics 5 at HL
The University welcomes applications from international students. Our international recruitment team can guide you on entry requirements. See our International Student website for further information about entry requirements for your country.
However, please note that international fee-paying students who require a Student visa cannot undertake a part-time programme due to visa restrictions.
If your highest qualification or your English level is insufficient for direct entry onto our degree programme, our International Foundation Programmes are perfect for you.
For more advice about applying to Kent, you can meet our staff at a range of international events.
Please see our English language entry requirements web page.
Please note that if you do not meet our English language requirements, we offer a number of 'pre-sessional' courses in English for Academic Purposes. You attend these courses before starting your degree programme.
Duration: 3 years full-time
In stages 1 and 2 will you will study a number of core modules in statistics, mathematics, computer science and artificial intelligence, while in stage 3 you will have a choice from a range of modules in addition to core modules
Indicative core modules:
This module provides an introduction to object-oriented software development. Software pervades many aspects of most professional fields and sciences, and an understanding of the development of software applications is useful as a basis for many disciplines. This module covers the development of simple software systems. Students will gain an understanding of the software development process, and learn to design and implement applications in a popular object-oriented programming language. Fundamentals of classes and objects are introduced and key features of class descriptions: constructors, methods and fields. Method implementation through assignment, selection control structures, iterative control structures and other statements is introduced. Collection objects are also covered and the availability of library classes as building blocks. Throughout the course, the quality of class design and the need for a professional approach to software development is emphasised and forms part of the assessment criteria.
This module equips students with an understanding of how modern cloud-based applications work. Topics covered may include:
• A high-level view of cloud computing: the economies of scale, security issues, ethical concerns, the typical high-level architecture of a cloud-based application, types of available services (e.g., parallelization, data storage).
• Cloud infrastructure: command line interface; containers and virtual machines; parallelization (e.g., MapReduce, distributed graph processing); data storage (e.g., distributed file systems, distributed databases, distributed shared in-memory data structures).
• Cloud concepts: high-level races, transactions and sequential equivalence; classical distributed algorithms (e.g., election, global snapshot, consensus, distributed mutual exclusion); scheduling, fault-tolerance and reliability in the context of a particular parallelization technology (e.g., MapReduce).
• Operating system support: network services (e.g., TCP/IP, routing, reliable communication), virtualization services (e.g., virtual memory, containers)
Introduction to R and investigating data sets. Basic use of R (Input and manipulation of data). Graphical representations of data. Numerical summaries of data.
Sampling and sampling distributions. ?² distribution. t-distribution. F-distribution. Definition of sampling distribution. Standard error. Sampling distribution of sample mean (for arbitrary distributions) and sample variance (for normal distribution) .
Point estimation. Principles. Unbiased estimators. Bias, Likelihood estimation for samples of discrete r.v.s
Interval estimation. Concept. One-sided/two-sided confidence intervals. Examples for population mean, population variance (with normal data) and proportion.
Hypothesis testing. Concept. Type I and II errors, size, p-values and power function. One-sample test, two sample test and paired sample test. Examples for population mean and population variance for normal data. Testing hypotheses for a proportion with large n. Link between hypothesis test and confidence interval. Goodness-of-fit testing.
Association between variables. Product moment and rank correlation coefficients. Two-way contingency tables. ?² test of independence.
This module serves as an introduction to algebraic methods and linear algebra methods. These are central in modern mathematics, having found applications in many other sciences and also in our everyday life.
Indicative module content:
Basic set theory, Functions and Relations, Systems of linear equations and Gaussian elimination, Matrices and Determinants, Vector spaces and Linear Transformations, Diagonalisation, Orthogonality.
This module introduces widely-used mathematical methods for functions of a single variable. The emphasis is on the practical use of these methods; key theorems are stated but not proved at this stage. Tutorials and Maple worksheets will be used to support taught material.
Complex numbers: Complex arithmetic, the complex conjugate, the Argand diagram, de Moivre's Theorem, modulus-argument form; elementary functions
Polynomials: Fundamental Theorem of Algebra (statement only), roots, factorization, rational functions, partial fractions
Single variable calculus: Differentiation, including product and chain rules; Fundamental Theorem of Calculus (statement only), elementary integrals, change of variables, integration by parts, differentiation of integrals with variable limits
Scalar ordinary differential equations (ODEs): definition; methods for first-order ODEs; principle of superposition for linear ODEs; particular integrals; second-order linear ODEs with constant coefficients; initial-value problems
Curve sketching: graphs of elementary functions, maxima, minima and points of inflection, asymptotes
Introduction to Probability. Concepts of events and sample space. Set theoretic description of probability, axioms of probability, interpretations of probability (objective and subjective probability).
Theory for unstructured sample spaces. Addition law for mutually exclusive events. Conditional probability. Independence. Law of total probability. Bayes' theorem. Permutations and combinations. Inclusion-Exclusion formula.
Discrete random variables. Concept of random variable (r.v.) and their distribution. Discrete r.v.: Probability function (p.f.). (Cumulative) distribution function (c.d.f.). Mean and variance of a discrete r.v. Examples: Binomial, Poisson, Geometric.
Continuous random variables. Probability density function; mean and variance; exponential, uniform and normal distributions; normal approximations: standardisation of the normal and use of tables. Transformation of a single r.v.
Joint distributions. Discrete r.v.'s; independent random variables; expectation and its application.
Generating functions. Idea of generating functions. Probability generating functions (pgfs) and moment generating functions (mgfs). Finding moments from pgfs and mgfs. Sums of independent random variables.
Laws of Large Numbers. Weak law of large numbers. Central Limit Theorem.
The 2021/22 annual tuition fees for this programme are:
For details of when and how to pay fees and charges, please see our Student Finance Guide.
For students continuing on this programme, fees will increase year on year by no more than RPI + 3% in each academic year of study except where regulated.*
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.
Scholarships are available for excellence in academic performance, sport and music and are awarded on merit. For further information on the range of awards available and to make an application see our scholarships website.
At Kent we recognise, encourage and reward excellence. We have created the Kent Scholarship for Academic Excellence.
The scholarship will be awarded to any applicant who achieves a minimum of A*AA over three A levels, or the equivalent qualifications (including BTEC and IB) as specified on our scholarships pages.
Teaching is based on lectures, with practical classes and seminars, but we are also introducing more innovative ways of teaching, such as virtual learning environments and work-based tuition.
We provide excellent support for you throughout your time at Kent. This includes access to web-based information systems, podcasts and web forums for students who can benefit from extra help. We use innovative teaching methodologies, including BlueJ and LEGO© Mindstorms for teaching Java programming.
Our staff have written internationally acclaimed textbooks for learning programming, which have been translated into eight languages and are used worldwide.
For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours. The precise breakdown of hours will be subject dependent and will vary according to modules. Please refer to the individual module details under Course Structure.
Methods of assessment will vary according to subject specialism and individual modules. Please refer to the individual module details under Course Structure.
The programme aims to:
You will gain a knowledge and understanding of:
You will gain the ability:
You will gain these subject-specific skills:
You gain the following transferable skills:
All University of Kent courses are regulated by the Office for Students.
Based on the evidence available, the TEF Panel judged that the University of Kent delivers consistently outstanding teaching, learning and outcomes for its students. It is of the highest quality found in the UK.
Please see the University of Kent's Statement of Findings for more information.
Mathematics at Kent scored 91% overall in The Complete University Guide 2021.
Computer Science at Kent (which includes all programmes offered by the School of Computing) scored 90% overall in The Complete University Guide 2021.
Computer Science at Kent was ranked 8th for research intensity in The Complete University Guide 2021.
Our graduates have gone on to work in:
Recent graduates have gone on to develop successful careers at leading companies such as:
The University has a friendly Careers and Employability Service, which can give you advice on how to:
The School has a dedicated Employability Coordinator who is a useful contact for all student employability queries.
You can gain commercial experience working as a student consultant within the Kent IT Consultancy. You can also gain teaching experience by taking the Computing in the Classroom module.
You graduate with a solid grounding in the fundamentals of data science and a range of professional skills, including:
To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
If you are from the UK or Ireland, you must apply for this course through UCAS. If you are not from the UK or Ireland, you can choose to apply through UCAS or directly on our website.Find out more about how to apply
T: +44 (0)1227 768896