Computing

Data Science with a Year in Industry - BSc (Hons)

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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.

Overview

Our specialist BSc Data Science with a Year in Industry 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

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

Your year in industry takes place between your second and final year, giving you invaluable work experience. You earn a salary and there may be the possibility of a job with the same company after graduation.

Our students go to a wide range of companies including:

  • Accenture
  • BT
  • GSK
  • IBM
  • Kent Police
  • Microsoft
  • Morgan Stanley
  • The Walt Disney Company.

You have the option to take this programme as a three-year degree, without the year in industry. For details, see Data Science.

Study resources

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 and statistical software such as:

  • R
  • Python
  • Maple
  • MATLAB
  • Minitab.

Extra activities

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 students in the Division of Computing, Engineering and Mathematical Sciences.

90%
Computer Science at Kent scored 90% overall in The Complete University Guide 2021.

Entry requirements

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.

Entry requirements

Home/EU students

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.

New GCSE grades

If you’ve taken exams under the new GCSE grading system, please see our conversion table to convert your GCSE grades.

  • Certificate

    A level

    BBB including Mathematics grade B (not Use of Mathematics). General Studies or Critical Thinking (but not both) can be accepted against the requirements

  • Certificate

    GCSE

    Mathematics grade 4/C

  • Certificate

    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.

  • Certificate

    BTEC Level 3 Extended Diploma (formerly BTEC National Diploma)

    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.

  • Certificate

    International Baccalaureate

    30 points overall or 15 points at HL with Mathematics 5 at HL

International students

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.

Meet our staff in your country

For more advice about applying to Kent, you can meet our staff at a range of international events.

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.

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Course structure

Duration: 4 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. You spend your year between stages 2 and 3 on and industrial placement and return in stage 3 you will have a choice from a range of modules in addition to core modules

Stage 1

Indicative core modules:

  • Computers and the Cloud 
  • Mathematical Methods 1 
  • Introduction to Object-oriented Programming
  • Probability
  • Databases and the Web
  • Statistics
  • Linear Mathematics
  • Programming for Artificial Intelligence
Compulsory modules currently include

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.

Find out more about CO320

An introduction to databases and SQL, focussing on their use as a source for content for websites. Creating static content for websites using HTML(5) and controlling their appearance using CSS. Using PHP to integrate static and dynamic content for web sites. Securing dynamic websites. Using Javascript to improve interactivity and maintainability in web content.

Find out more about CO323

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)

Find out more about CO337

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.

Find out more about MA306

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.

Find out more about MA347

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

Find out more about MA348

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.

Find out more about MA351

Year in industry

You spend a year working in an industrial or commercial environment between Stages 2 and 3.

Our students go to a wide range of companies including:

  • IBM 
  • Intel
  • Disney
  • Morgan Stanley.

They have also been to overseas employers in locations including Amsterdam, Hong Kong and the US.

Fees

The 2021/22 annual tuition fees for this programme are:

  • Home full-time TBC
  • International full-time TBC

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.

Fees for Year in Industry

Fees for Home undergraduates are £1,385.

Fees for Year Abroad

Fees for Home undergraduates are £1,385.

Students studying abroad for less than one academic year will pay full fees according to their fee status. 

Additional costs

General additional costs

Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.

Funding

University funding

Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details. 

Government funding

You may be eligible for government finance to help pay for the costs of studying. See the Government's student finance website.

Scholarships

General scholarships

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.

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

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. 

Academic support

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.

Teaching staff

Our staff have written internationally acclaimed textbooks for learning programming, which have been translated into eight languages and are used worldwide. 

Contact hours

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.

Programme aims

The programme 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.

Learning outcomes

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.

Intellectual skills

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
  • to demonstrate a reasonable level of skill in calculation and manipulation of mathematical and statistical material written within the programme and some capability to solve problems formulated within it
  • to apply a range of core concepts and principles in well-defined contexts relevant to Computing, Mathematics and Statistics used in data science
  • to use logical argument
  • to demonstrate skill in solving problems in Data Science by various appropriate methods
  • in relevant computer skills and usage
  • to work with relatively little guidance
  • to present succinctly to a range of audiences rational and reasoned arguments.

Subject-specific skills

You will gain these subject-specific skills:

  • ability to demonstrate knowledge of key mathematical and statistical concepts and topics, both explicitly and by applying them to the solution of problems.
  • ability to demonstrate skills in codification and storage of data and in pre-processing raw data for later retrieval and analysis.
  • ability to demonstrate 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.
  • ability 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.
  • ability to use computational and more general IT facilities as an aid to mathematical and statistical processes
  • ability to present mathematical and statistical arguments and the conclusions from them with clarity and accuracy
  • ability 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.

Transferable skills

You 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.

Teaching Excellence Framework

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.

TEF Gold logo

Independent rankings

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.

Careers

Graduate destinations

Our graduates have gone on to work in:

  • software engineering
  • mobile applications development
  • systems analysis
  • consultancy
  • networking
  • web design and e-commerce
  • finance and insurance
  • commerce
  • engineering
  • education
  • government
  • healthcare.

Recent graduates have gone on to develop successful careers at leading companies such as:

  • BAE Systems
  • Cisco
  • IBM
  • The Walt Disney Company
  • Citigroup
  • BT.

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.

You will have access to a dedicated Employability Coordinator who is a useful contact for all student employability queries.

Career-enhancing skills

You graduate with a solid grounding in the fundamentals of data science and a range of professional skills, including:

  • programming
  • modelling
  • design.

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 this course

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

All applicants

Apply through UCAS

International applicants

Apply now to Kent

Contact us

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United Kingdom/EU enquiries

Enquire online for full-time study

T: +44 (0)1227 768896

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International student enquiries

Enquire online

T: +44 (0)1227 823254
E: internationalstudent@kent.ac.uk