Undergraduate BSc (Hons)
Data Science
Learn valuable analytical skills while exploring cutting-edge mathematical science.
Learn valuable analytical skills while exploring cutting-edge mathematical science.
Data science combines powerful computing technology, sophisticated statistical methods, and expert subject knowledge to analyse and gain practical insights from the huge amounts of data produced by modern societies.
This new course combines the expertise of internationally-renowned statisticians and mathematicians, computer scientists and machine learners to ensure that you develop the expertise and quantitative skills required for a successful future career in the field.
You'll 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.
An emerging field in recent decades, data science is now an exciting, fulfilling and high-profile career choice.
Learn industry standard software like PROPHET, R and Python.
Amazing facilities, software packages and makerspaces on a vibrant campus with stunning city views.
You’ll benefit from free membership of the Kent Maths Society and Invicta Actuarial Society.
You gain invaluable workplace experience, get paid and have the chance to assess a particular career path.
BBB
DDM
120 tariff points from your IB Diploma, including Maths at 4 at HL or SL, typically H5, H6, H6 or equivalent.
Mathematics grade 6/B
N/A
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
A typical offer would be to obtain the Access to HE Diploma in a suitable subject with a minimum of 45 credits at Level 3, with 24 credits at Distinction and 21 credits at Merit.
The following modules are offered to our current students. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.
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.
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.
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.
This module covers the design and implementation of high-quality software, and provides an introduction to software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including effective graphical representations. 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.
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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.
In this module you learn what is meant by machine learning and how to explain the key algorithms (especially the backpropagation algorithm for learning deep neural networks) and mathematical equations that underlie them. You also familiarise yourself with a range of machine learning algorithms and their inner mechanisms. Using state-of-the-art software technology apply these algorithms to the solution of problems. You will study the existing machine learning implementations of selected algorithms, and you will also engage in implementation of algorithms and procedures relevant to machine learning.
There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their late careers.
This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.
This is a practical module to develop the skills required by a professional statistician (report writing, consultancy, presentation, wider appreciation of assumptions underlying methods, selection and application of analysis method, researching methods).
Software: R, SPSS and Excel (where appropriate/possible). Report writing in Word. PowerPoint for presentations.
• Presentation of data
• Report writing and presentation skills
• Hypothesis testing: formulating questions, converting to hypotheses, parametric and non-parametric methods and their assumptions, selection of appropriate method,
application and reporting. Use of resources to explore and apply additional tests. Parametric and non-parametric tests include, but are not limited to, t-tests, likelihood
ratio tests, score tests, Wald test, chi-squared tests, Mann Whitney U-test, Wilcoxon signed rank test, McNemar's test.
• Linear and Generalised Linear Models: simple linear and multiple regression, ANOVA and ANCOVA, understanding the limitations of linear regression, generalised linear
models, selecting the appropriate distribution for the data set, understanding the difference between fixed and random effects, fitting models with random effects, model
selection.
• Consultancy skills: group work exercise(s)
There is no specific mathematical syllabus for this module; students will chose a topic in mathematics, statistics or financial mathematics from a published list on which to base their coursework assessments (different topics for levels 6 and 7). The coursework is supported by a series of workshops covering various forms of written and oral communication. These may include critically evaluating the following: a research article in mathematics, statistics or finance; a survey or magazine article aimed at a scientifically-literate but non-specialist audience; a mathematical biography; a poster presentation of a mathematical topic; a curriculum vitae; an oral presentation with slides or board; a video or podcast on a mathematical topic. Guidance will be given on typesetting mathematics using LaTeX.
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.
Methods of assessment will vary according to subject specialism and individual modules.
Please refer to the individual module details under Course Structure.
For programme aims and learning outcomes please see the programme specification.
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.
An industrial placement can greatly enhance your studies and have a dramatic impact on your graduate choices.
The 2024/25 annual tuition fees for this course 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.
Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
Next steps
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.
We welcome applications from students all around the world with a wide range of international qualifications.
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