Data Science - BSc (Hons)
with a Foundation Year
- Standard option
- with a Year in Industry
with a Foundation Year
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Data science combines powerful computing technology, sophisticated statistical methods, and expert subject knowledge to analyse and gain practical insights from huge amounts of data produced by modern societies.
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.
Our degree programme
The BSc Data Science with a Foundation year is studied over four years full time. The Foundation Year provides an opportunity for you to develop your mathematics and computing skills and start learning some university-level material, fully preparing you for university study before you progress onto Stage 1 of the BSc Data Science. Upon successful completion of the Foundation Year, you will have the choice of taking a placement year between stages 2 and 3 by transferring onto the BSc Data Science with a Year in Industry.
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.
Year in industry
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 a School of Mathematics, Statistics and Actuarial Science student 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.
The University will consider applications from students offering a wide range of qualifications. All applications are assessed on an individual basis but some of our typical requirements are listed below. Students offering qualifications not listed are welcome to contact our Admissions Team for further advice. Please also see our general entry requirements.
CCD including Mathematics grade C (not Use of Mathematics). Either General Studies or Critical Thinking (but not both) can be accepted against the requirements.
Grade 7 GCSE maths
Access to HE Diploma
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 Qualifications (QCF; NQF; OCR).
34 points overall or 13 points at HL with Mathematics 4 at HL or Mathematics 6 at SL.
International Foundation Programme
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
If you are an international student, visit our International Student website for further information about entry requirements for your country, including details of the International Foundation Programmes. Please note that international fee-paying students who require a Student visa cannot undertake a part-time programme due to visa restrictions.
Please note that meeting the typical offer/minimum requirement does not guarantee that you will receive an offer.
English Language Requirements
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: 4 years full-time
The following modules are indicative of those offered on this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
In stages 1 and 2 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.
Students with A-level mathematics or equivalent take MAST0018, whilst all other students will take MAST0017.
Functions: Functions, inverse functions and composite functions. Domain and range.
Elementary functions including the exponential function, the logarithm and natural logarithm functions and ax for positive real numbers a. Basic introduction to limits and continuity of a function, without epsilon-delta proofs.The derivative: The derivative as the gradient of the tangent to the graph; interpretation of the derivative as a rate of change. The formal definition of the derivative and the calculation of simple examples from first principles. Elementary properties of the derivative, including the product rule, quotient rule and the chain rule; differentiation of inverse functions; calculating derivatives of familiar functions, including trigonometric, exponential and logarithmic functions. Applications of the derivative, including optimisation, gradients, tangents and normal. Parametric and implicit differentiation of simple functions. Taylor series.Graphs: Curve sketching including maxima, minima, stationary points, points of inflection, vertical and horizontal asymptotes and simple transformations on graphs of functions. Additional material may include parametric curves and use of Maple to plot functions.
This module introduces the ideas of integration and numerical methods.
a) Integration: Integration as a limit of a sum and graphical principles of integration, derivatives, anti-derivatives and the Fundamental Theorem of Calculus (without proof), definite and indefinite integrals, integration of simple functions.b) Methods of integration: integration by parts, integration by change of variables and by substitution, integration by partial fractions.c) Solving first order differential equations: separable and linear first order differential equations. Construction of differential equations in context, applications of differential equations and interpretation of solutions of differential equations.d) Maple: differentiation and integration, curve sketching, polygon plots, summations.
Additional material may include root finding using iterative methods, parametric integration, surfaces and volumes of revolution.
Students will be introduced to key mathematical skills, necessary in studying for a mathematics degree: use of the University Library and other sources to support their learning, present an argument in oral or written form, learn about staff in the School and beyond, etc. In particular, students will study various techniques of proof (by deduction, by exhaustion, by contradiction, etc.). These techniques will be illustrated through examples chosen from various areas of mathematics (and in particular co-requisite modules).
Statistical techniques are a fundamental tool in being able to measure, analyse and communicate information about sets of data. Using illustrative data sets we show how statistics can be indispensable in applied sciences and other quantitative areas. This module covers the basic methods used in probability and statistics using Excel for larger data sets.
Sampling from populations. Data handling and analysis using Excel. Graphical representation for the interpretation of univariate and bivariate data; outliers. Sample summary statistics: mean, variance, standard deviation, median, quartiles, inter-quartile range, correlation. Probability: combinatorics, conditional probability, Bayes' Theorem. Random variables: discrete, continuous; expectation, variance, standard deviation. Discrete and continuous distributions: Binomial, discrete uniform, Normal, uniform. Sampling distributions for the mean and proportion. Hypothesis testing: one sample, mean of Normal with known variance and proportion, 1- and 2-tail. Confidence intervals: one sample, mean of Normal with known variance and population proportion.
This module introduces fundamental methods needed for the study of mathematical subjects at degree level.
Trigonometry: introduction to the trigonometric functions, inverse trigonometric functions, radians, properties of sine and cosine functions, trigonometric identities, solving trigonometric equations
Geometry: circles and ellipses, triangles, SOHCAHTOA, sine and cosine rule, opposite and alternate angle theorems
Hyperbolic functions: introduction to hyperbolic functions and inverse hyperbolic functions including definitions, domains and ranges and graphs.
Complex numbers: introduction to the system of complex numbers and its geometrical interpretation..
Through this module, students will develop the transferable linguistic and academic skills necessary to successfully complete other modules on their programme and acquire the specific language skills that they will require when entering SMSAS and SPS Stage 1 programmes. The programme of study focuses on writing and speaking skills, enhancing academic language through classroom, homework and assessed activities. Writing skills will be used to write a technical report, interpret data and describe processes. Spoken skills will be used in presentations and seminars.
This module introduces the students to the basics of Maple and three topics in the mathematical sciences. The precise topics will vary in any particular year. Potential topics include (for example): history and/or people active in the mathematical sciences, algorithms, engaging the public in the mathematical sciences, mathematical games. Each topic is supported by a series of workshops introducing key aspects of the topic. Maple: the Maple environment, basic commands, basic calculus, curve sketching. There is no specific mathematical syllabus for the topics part of the module.
Algebra: simplifying expressions and rearranging formulae, indices, surds, algebraic fractions, solution of linear and quadratic equations and simultaneous equations, quadratic equations and the discriminant, inequalities and interval notation, binomial expansions, manipulating and factorising polynomials, exponentials and logarithms, equations involving exponentials. Functions and graphs: plotting and recognising the graphs of elementary functions (modulus, exponential, …), roots, intercepts, turning points, area (graphical methods), co-ordinate geometry of straight lines, parallel and perpendicular lines, applications to plots of experimental data, simple graph transformations.
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).
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.
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.
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.
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.
To be confirmed.
Built on the foundation of object-oriented software development, this module provides an introduction software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including summarising data, using measures of central tendency and dispersion, and effective graphical representations. Various probability models, including normal and binomial distributions, sampling and inference and predictive techniques are introduced.
Throughout the module, students will learn to embed data analysis and statistics concepts into a programming language which offers good support for AI (e.g., Python). Students will learn to use important AI-purposed libraries and tools, and apply these techniques to data loading, processing, manipulation and visualisation.
Building scaleable web sites using client-side and and server-side frameworks (e.g. JQuery, CodeIgniter). Data transfer technologies, e.g. XML and JSON. Building highly interactive web sites using e.g. AJAX. Web services. Deploying applications and services to the web: servers, infrastructure services, and traffic and performance analysis. Web and application development for mobile devices.
This module aims to strengthen the foundational programming-in-the-small abilities of students via a strong, practical, problem solving focus. Specific topics will include introductory algorithms, algorithm correctness, algorithm runtime, as well as big-O notation. Essential data structures and algorithmic programming skills will be covered, such as arrays, lists and trees, searching and sorting, recursion, and divide and conquer.
This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. You learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.
This module provides an introduction to the theory and practice of database systems. It extends the study of information systems in Stage 1 by focusing on the design, implementation and use of database systems. Topics include database management systems architecture, data modelling and database design, query languages, recent developments and future prospects.
This module is designed to provide students with an introduction to the use of data analytics tools on large data sets including the analysis of text data. The module will begin by discussing the principles of text-mining and big data. The module will then discuss the techniques that can be used to explore large data sets (including pre-processing and cleaning) and the use of multivariate statistical techniques for supervised and unsupervised learning. The module will conclude by considering several data mining techniques.
Constructing suitable models for data is a key part of statistics. For example, we might want to model the yield of a chemical process in terms of the temperature and pressure of the process. Even if the temperature and pressure are fixed, there will be variation in the yield which motivates the use of a statistical model which includes a random component. In this module, students study linear regression models (including estimation from data and drawing of conclusions), the use of likelihood to estimate models and its application in simple stochastic models. Both theoretical and practical aspects are covered, including the use of R.
You take these indicative core modules, plus your choice from a selection of optional modules.
The 2023/24 annual tuition fees for this course are:
- Home full-time £9,250
- EU full-time £13,500
- International full-time £18,000
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.*
Your fee status
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.
Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.Search scholarships
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
The Kent Scholarship for Academic Excellence
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 and assessment
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 course aims to:
- Attract and meet the needs of those contemplating a career as a data scientist.
- Equip students with the technical appreciation, skills and knowledge appropriate to graduates in Data Science.
- Develop students’ facilities of rigorous reasoning and precise expression.
- Develop students’ capabilities to formulate and solve problems, relevant to Data Science.
- Develop in students an appreciation of recent developments in Data Science, and of the links between the theory and practical application.
- Develop in students a logical approach to solving problems.
- Develop in students an enhanced capacity for independent thought and work.
- Ensure students are skilled in the use of relevant Data Science software.
- Provide students with opportunities to study advanced topics in Data Science, engage in research at some level, and develop communication and personal skills.
Knowledge and understanding
You will gain a knowledge and understanding of:
- Core mathematical principles of calculus, algebra, mathematical methods and linear algebra.
- The subjects of probability and inference.
- Information technology skills as relevant to Data Science.
- Methods and techniques appropriate to Computing and Statistics.
- The role of logical mathematical argument and deductive reasoning.
- Practice, including problem identification, deploying established approaches accurately to analyse and solve problems and testing and evaluation.
- Software, including programming languages and practice, tools and packages, computer applications, structuring of data and information.
- The legal background, security and ethical issues involved in data science.
You will gain the ability to demonstrate:
- A reasonable understanding of the basic body of knowledge for Computing, Mathematics and Statistics used in data science.
- A reasonable level of skill in calculation and manipulation of mathematical and statistical material written within the course and some capability to solve problems formulated within it.
- A range of core concepts and principles in well-defined contexts relevant to Computing, Mathematics and Statistics used in data science.
- Logical argument.
- Skill in solving problems in Data Science by various appropriate methods.
- Relevant computer skills and usage.
- Work with relatively little guidance.
- Succinctly to a range of audiences rational and reasoned arguments.
You will gain the ability to demonstrate:
- Knowledge of key mathematical and statistical concepts and topics, both explicitly and by applying them to the solution of problems.
- Skills in codification and storage of data and in pre-processing raw data for later retrieval and analysis.
- Understanding of fundamental computational concepts and algorithmic thinking, including recursive, distributed and parallel possibilities; the role of these in devising artificial intelligence/machine learning algorithms and in statistical modelling as well as in delivering innovative solutions to applied problems.
- Ability to comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form in order to facilitate their analysis and solution.
- How to use key aspects of statistics, artificial intelligence/machine learning and optimisation in a principled fashion to address the challenges of small and large data sets in well-defined contexts, showing judgement in the selection and application of tools and techniques.
- Computational and more general IT facilities as an aid to mathematical and statistical processes.
- Mathematical and statistical arguments and the conclusions from them with clarity and accuracy.
- How to critically evaluate and analyse complex problems, argument and evidence, including those with incomplete information, and devise appropriate computing solutions, within the constraints of a budget.
You will gain the following transferable skills:
- Problem-solving skills, relating to qualitative and quantitative information.
- Communication skills.
- Numeracy and computational skills.
- Information technology skills such as word-processing, internet communication, etc.
- Personal and interpersonal skills and management skills.
- Time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working.
- Study skills needed for continuing professional development.
Mathematics at Kent was ranked 19th for student satisfaction in The Complete University Guide 2023.
Our graduates have gone on to work in:
- software engineering
- mobile applications development
- systems analysis
- web design and e-commerce
- finance and insurance
Recent graduates have gone on to develop successful careers at leading companies such as:
- BAE Systems
- The Walt Disney Company
Help finding a job
The University has a friendly Careers and Employability Service, which can give you advice on how to:
- apply for jobs
- write a good CV
- perform well in interviews.
The School has a dedicated Employability Coordinator who is a useful contact for all student employability queries.
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:
- think critically
- communicate your ideas and opinions
- analyse situations and troubleshoot problems
- work independently or as part of a team.
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
Apply for Data Science with a Foundation Year - BSc (Hons)
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 apply through UCAS or directly on our website if you have never used UCAS and you do not intend to use UCAS in the future.
Find out more about how to apply
United Kingdom/EU enquiries
Enquire online for full-time study
T: +44 (0)1227 768896
International student enquiries
T: +44 (0)1227 823254
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