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Data Science BSc(Hons)

Attendance UCAS code/apply Year of entry
3 years full time G300 2018
4 years full time including sandwich year G301 2018
4 years full time including foundation year G308 2018
6 years part time Apply direct to the University 2018

Why choose this course?

The growth in data and knowledge is radically and continually changing the way we experience our world, whether in business, science, education or government. This course assumes no prior knowledge beyond GCSE of mathematics, statistics or computer science and is designed to equip you to pursue a career in a wide range of roles, such as Data Scientist, Data Analyst and Data Engineer, across a broad spectrum of employers, including the creative arts, telecommunication and management consultancy, in what is currently a growth area for employment, globally.

The overarching ethos of the delivery is that you should be engaged in active learning wherever possible. A largely problem-centred approach to learning is adopted, whereby you begin with the problems of interest and learn the necessary theory and techniques required to solve them. To assist with this, extensive use is made of computational support. You will gain computing skills and experience of a variety of up to date professional, industry-standard software packages deployed on the University's modern computing facilities.

The format of assessments is varied - for example, in addition to examinations, you will investigate case studies, individually and in groups, writing reports and giving oral presentations. Typically you will produce simulations, posters, videos, schedules/quotations for customers, write articles, etc. In this way, as you progress through the course, you will ‘learn by doing and making' and assemble a portfolio of tangible outputs which provide evidence, explicitly, of the knowledge and skills you have gained and which may be used to demonstrate your capabilities to future employers.

Of key importance is a theme integrating mathematical, statistical and data-oriented professional skills utilising industry-relevant software packages, culminating in you undertaking a substantial piece of independent study allowing you to design and create solution implementations or other appropriate artefacts. A distinctive feature of this theme is that, along the way to the final project, you will work in groups together with students from other (IT-based) disciplines on real world case-studies, developing your own professional skills and awareness of your place in the wider professional world.

Our computing courses ranking rose by 34 places in the Guardian University League Tables 2018.

Foundation year

If you would like to study data science, computing or mathematics at Kingston University but are not yet ready to join the first year of a BSc(Hons) course, you can include an extra foundation year within your chosen degree. Please see the foundation year course page for details of modules.

What you will study

The Data Science BSc(Hons) is tailored to data-oriented professional roles and as such there are no 'option' (elective) modules, instead the core programme aims to combine experience in the use of relevant software with mathematical and statistical knowledge to maximise graduate employability:

Year 1 gives a thorough grounding in software development and data analysis skills, typically using Matlab and SAS, whilst developing just the necessary foundation in mathematics and statistics for later data science modules. The mathematics and statistics teaching assumes no prior knowledge beyond GCSE and students are supported by a Personal Tutor and the 'MathsAid' study skills centre, helping to bridge any gaps in knowledge.

Year 2 enlarges the type of data sets that can be dealt with by developing experience with database and statistical software systems alongside some more advanced statistics to model data. The statistics teaching is strongly supported by software so that the underlying theory can be developed gradually and low-level programming of algorithms and statistical tests is unnecessary - however 'scripting' is an essential skill for data scientists, gluing software packages together and transforming data so programming skills from Year 1 are further developed using Python, Oracle and R, alongside project management, and the use of the web as a data source, repository and publishing platform.

The final year (Year 3 or Year 4 following a placement) brings together the threads of professional experience, mathematics, statistics and programming knowledge to perform more advanced Artificial Intelligence(AI), Machine Learning, Big Data and Data Mining in the taught modules, for example making use of packages like Weka and Matlab, alongside a year-long independent project. All students are guided in the choice of their final project and this is an ideal opportunity to showcase a range of professional skills in an exciting piece of work.

Module listing

Please note that this is an indicative list of modules and is not intended as a definitive list. Those listed here may also be a mixture of core and optional modules.

Year 1

  • We make the first steps into the analysis of data. We begin by considering what are data, how they are obtained and introduce consideration of aspects of data collection, including designing surveys to obtain the information desired. Then, we look at how to approach data analysis, defining questions and identifying the best techniques to achieve  solutions to the problems posed. Some probability concepts are introduced to support  the statistical inference methods used as the module progresses. The main objective of the module is to teach practical data analysis skills using a problem centred approach simulating the practice most commonly encountered in industry and other real life scenarios, thus improving students' employablity. We teach students to work together and ask questions of the data and to find the correct statistical analysis tools to obtain good information and make useful decisions.

    The module is the basis for much of the work in Statistics and in part the Data Science stream of the Mathematics course. It is foundational for the Data Science degree.

    On successful completion of the module, students will be able to:

    • characterise the data set in terms of purpose, source timescale and measurement.
    • describe and summarise the main features of a dataset by using appropriate tables,  diagrams and summary statistics
    • construct confidence intervals and conduct hypotheses tests for means and proportions in well-defined, appropriately sized samples and interpret the results;
    • enhance employability by using appropriate industry standard software for data manipulation, basic statistical analysis and presentation of data;
    • demonstrate awareness of the key principles and practices of survey design and implementation.

    Read full module description

  • This module, which is core for the BSc in Data Science, will equip students with the essential mathematics required for their other modules, and ultimately careers in Data Science and associated fields. A variety of teaching and learning methods will be employed with an emphasis on a problem-centred approach to learning. New topics will be introduced through real problems, showing the necessity for previously unseen concepts, methods and techniques to solve those problems.

    The module will reinforce, develop and formalise students' existing knowledge of mathematical concepts and techniques and familiarise them with more advanced topics in algebra and calculus, providing the foundation for further study of statistics and permitting consideration of real life problems in Data Science.

    In addition, the module will help students to interpret a real world problem and to develop the ability to work within a team and present their work in a clear and concise format, designed specifically to enhance their employability.

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  • This module is taken by all first year undergraduate students undertaking a degree in the computing subject area. Previous experience of programming is not assumed. The module seeks to introduce a foundation for programming that can be built on in subsequent years and that accommodates specialist practice within computing eg games, software engineering, media, UX etc.

    Teaching and learning is split between a variety of different units to ensure the module is flexible enough to accommodate each cohort and student's needs. As befits a practical discipline like programming, a hands-on approach is used that facilitates self-paced and self-directed learning. Students are encouraged to engage with, develop and experiment with programs in a constructivist fashion inspired by bricolage (Stiller, 2009; Stiller, 2017).

    The intent is to build students' confidence as they learn to program, and provide a foundation that can be built on so that in later years they can go beyond simple solutions to problems and be ready to engage in full-fledged application development.

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  • The goal of the Professional Environments module is to prepare students for professional practice firstly by ensuring they acquire suitable employability assets and secondly by equipping them with an understanding of the role of a professional in society and the role of professional bodies.

    While the bulk of the taught programme focuses primarily on domain knowledge, the Professional Environments module focuses on developing key skills (as enumerated in the Programme Specification), personal qualities (eg commercial awareness, reliability and punctuality, understanding the centrality of customers and clients), and professional knowledge including the need to engage with continuing professional development. With such assets, students will generate a CV, an employment portfolio, and a professional online presence.

    Being a professional also means understanding the key legal, ethical and societal issues pertinent to the domain, and understanding the need for continuing professional development (CPD) especially when technology develops at such a rapid pace. The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.

    Reflecting the fact that team working is ubiquitous in the modern workplace, a significant proportion of the assessment work on the course is group-work based. There is considerable evidence that group work promotes a much deeper engagement with taught content. It also encourages the development of diverse learning communities. This module will therefore introduce students to best practice in group working covering how to approach group work, how to deal with different types of people, and methods of selecting and managing groups.

    Read full module description


Year 2

  • This module develops and builds on the concepts of probability and statistics introduced in the module Practical Data Analyst Skills . It is a core module for students taking Mathematics, and Data Science degrees.

    Probability underpins aspects of statistics and we need a sound grounding in those topics that are directly applicable to many real world applications of the subject. We also need to be able to apply these probability distributions to real world data in order to obtain more information. In addition, we will be looking at how data from experiments and related studies are analysed  and how we can make useful sense of data. We also study some of the general linear models which give us understanding how various factors influence output data.

    On successful completion of the module, students will be able to:

    • solve problems in applied probability, using discrete and continuous random variables and probability distributions
    • estimate the parameters of certain probability distributions using appropriate techniques of statistical inference
    • choose and apply the appropriate techniques of General Linear Models to obtain useful information from experimental and other related data sets and test the efficacy of these models
    • develop and apply statistical and other software to analyse data, and communicate the results of the analysis

    Read full module description

  • This module is core for students studying data Science and combines the development of further mathematical skills with an introduction to software important in the data science industry.The first part of this module builds on the knowledge and skills students will have acquired in MA4600, and further develops their competence and confidence in the principles and use of the more advanced mathematical techniques necessary and relevant for the study and practice of data science. It will employ a hybrid, blended approach to learning and teaching, taking a problem-centred approach, with real problems being used to motivate the need to gain knowledge of previously unseen methods and theory, for use in their solution.This will reinforce, develop and formalise students' previous knowledge of concepts related to algebra and calculus,and familiarise them with more advanced topics required for the further study of data science, such as functions of more than one variable and calculus thereof, and further matrix algebra, including more advanced methods and their application in the contexts of data science. In the second part of the module, students will develop practical experience of using open-access software platforms in order to read, check, explore and analyse data.This will reinforce and develop the knowledge acquired in the previous module MA4550,with an emphasis on measures of uncertainty in statistical modelling and the methods for evaluating and enhancing data quality.The main tools for data description and inference will be put in the context of their practical applications, with emphasis on the conceptual interpretation rather than the analytical derivation. Students will gain valuable experience in undertaking and completing a data analysis exercise within their assessment and through presenting their findings in a professional manner.

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  • This module seeks to establish the skills required to build full-stack database-driven web applications. Students will learn how to design, build and query databases according to user information needs using logical data models and structured query language (SQL).

    They will also learn how to design and build scalable interactive applications that are delivered over the web and integrated with a backend database.

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  • Following a project-based pedagogic approach, students will undertake a major inter-disciplinary team-work project drawn from a list of authentic industrial problems. Achieving the goals of the project will require students, firstly, to apply the various development methodologies they have acquired on their course and, secondly, to develop professional skills in project management and team working.

    While the bulk of the taught programme focuses primarily on the learning of domain knowledge, the goal of the Professional Environments 2 module is to prepare students for professional practice in their respective domains. They will develop the necessary project management and team-working skills, and, by working as a team on an authentic industrial project, they will gain a high degree of familiarity with the typical requirements capture, design, and development methodologies relevant to their discipline. With the focus on making real-world artefacts, the students will integrate their work into an employment focused portfolio.

    Being a professional practitioner also mean critically assessing both goals and solutions from legal, ethical and societal perspectives as well as addressing security and safety concerns. Students are also encouraged to consider their continuing professional development needs and to engage with their professional bodies. To encourage career management skills and promote employability after graduation, students are expected to integrate the artefacts they produce and reflective practice narratives into their employability portfolios and personal development plans.

    The module is designed to support different domain areas and to integrate experience from other professions. The subject areas being studied demand a global perspective which encourages the inclusion of our diverse of communities and national practices.

    Read full module description


Year 3

  • This module is designed to introduce students to further developments of statistical modelling methodologies introduced at Level 5. The module will be taught in a very practical way using an example driven approach to present applications of the theory, and subsequently interpretation and communication of the outcomes. Students will also be introduced to the applications of advanced models in real life scenarios  including within the Business and Health fields where demand for such skills is consistently high. During the module students will gain practical experience of how to determine and apply appropriate statistical methodologies and how to interpret, present and contextualize the findings of such analyses to the standard expected in a professional setting. They will also learn about the processes involved in such applications such as the full cycle of clinical trial analysis and the practical implementation of forecasting methods in business. Throughout students will be instructed in appropriate statistical software for carrying out such analyses and in the effective communication of their results, hence enhancing employability potential.

    On successful completion of the module, students will be able to:

    • demonstrate understanding of selected Generalised Linear Models and when it is appropriate to use them;
    • choose and apply a statistical methodology appropriate to a given data analysis problem;
    • identify and analyse data obtained from clinical studies, and interpret and report the results of such analyses;
    • select and apply appropriate forecasting techniques for data analysis and critically assess the validity of the modelling results for time series data from the computer output; and
    • design, implement and produce solutions using appropriate modern computational software for the statistical techniques learned.

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  • This module is a Level 6 core module for the BSc Data Science, and an elective (option) module for the BSc Mathematics programme. It builds upon the foundations of Data Analysis & Modelling and computing skills developed in earlier modules. This module aims to introduce the study of artificial intelligence with applications in research-informed topics such as language modelling, speech recognition or pattern recognition in "big data" applications.

    It introduces both "traditional" (logic-based) and "modern" (eg neural networks, including "Deep Learning", decision tree-based and probabilistic) "machine learning" approaches to artificial intelligence, and includes some case studies of modern practical applications. These are important mathematical and statistical concepts that are essential attributes for employable data scientists, mathematicians and statisticians in the modern, data-driven world.

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  • This module is a Level 6 option for BSc Data Science. It aims to make students familiar with, and proficient in applying, the fundamental principles of 'data mining' (discovering patterns within data) and data visualisation, which are essential skills for data scientists and data analysts working in the modern world, who need to distil and identity the key features and factors influencing measured 'outcomes' in large datasets and present their findings visually in a format suitable to be understood by people who may have had rather less training in scientific fields (managers, financial sponsors/clients, accountants, politicians or policy makers, the general public). The module will build on skills acquired in earlier modules, making use of elementary mathematical, statistical and computational principles, and complementing the content of MA6600 (however that module is not a co-requisite). Students will be trained in data mining, pattern recognition and data visualisation techniques, and apply these to address problems involving large datasets from various domains, such as text data (eg newspapers), web mining (eg integrating data and text mining within a website), demography (eg historical census data) or sports (eg results and statistics on websites). These have broad application in a range of careers and enhance the employability of graduates in numerate and computational disciplines.

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  • The goal of the module is to further develop skills in organisation, timekeeping, research literature, developing and critically analysing results as well as reporting work verbally and in a written format. The end result will be an artefact or artefacts which demonstrate creativity and technical competence as well as a technical report.

    Read full module description


You will have the opportunity to study a foreign language, free of charge, during your time at the University on a not-for-credit basis as part of the Kingston Language Scheme. Options currently include: Arabic, French, German, Italian, Japanese, Mandarin, Portuguese, Russian and Spanish.

Most of our undergraduate courses support studying or working abroad through the University's Study Abroad or Erasmus programme.

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