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BSc (Honours) Data Science - Learning outcomes

Educational aims

As a graduate of our honours degree in data science, you will learn theories and techniques that will equip you with a range of skills to analyse complex data and guide evidence-based decision and policy making across a range of public and private organisations. Data scientists bring a variety of different areas of expertise to their work and this qualification will provide you with a broad base across discipline areas and the opportunity to specialise in one or more of these. Together with developing knowledge and understanding of the fundamental concepts, techniques and technologies, and issues involved in their application, it will also:

  • Enable you to keep ahead in a rapidly changing subject area by helping you to develop as an independent learner
  • Develop relevant skills in communication and problem solving
  • Imbue the qualities that come with being a graduate in any subject: specialist knowledge, intellectual self-confidence, independent, analytical ability and the life-long learning skills needed to keep up with fast-changing technologies and techniques
  • Enable you to apply your learning in your private, social and professional life
  • Develop your capability to work with abstract concepts
  • Familiarise you with data analysis, problems arising in the collection of data, sampling techniques, modelling and prediction
  • Familiarise you with Bayesian statistics, multivariate statistics, linear and generalised linear modelling
  • Familiarise you with mathematical techniques involving matrices, linear algebra and calculus which are fundamental to applied mathematics and needed to analyse data using advanced numerical analysis, optimisation, network and graph theory
  • Provide practical experience in the use of information and communication technologies
  • Give you understanding of machine learning, artificial intelligence and computer programming
  • Give you the ability to model real world situations and use your knowledge of statistics, mathematics and computing to develop solutions to practical problems.

Learning outcomes

Knowledge and understanding

On completion of this degree, you will have knowledge and understanding of:

  • a range of simple and more advanced methods for analysing statistical data (including medical applications data, time series data and multivariate data), working with statistical models and carrying out statistical inference (including in particular methods for linear and generalised linear models, and Bayesian methods)
  • calculus, differential equations, linear algebra, multivariable calculus and vector calculus
  • the fundamental principles, concepts and techniques underlying computing and IT, and the range of models used to support the analysis and design of computing and IT systems
  • the range of situations in which computing and IT systems are used in data science and the possibilities and limitations of such systems
  • machine learning and artificial intelligence
  • the ethical and legal issues associated with data science
  • a selection (depending on your options) of advanced topics including: advanced data management and analysis, graph theory, network analysis, mathematical methods, applied probability, mathematical statistics and interactive design.

Cognitive skills

On completion of this degree, you will be able to:

  • use your judgement in applying and selecting a wide range of mathematics and statistics tools and techniques to solve real world problems
  • construct appropriate mathematical and statistical models and draw justifiable inferences in qualitative and quantitative problem-solving skills
  • reason with abstract concepts
  • apply and critically evaluate key computing and IT concepts in a range of contexts
  • select and apply appropriate techniques and tools for abstracting, modelling, problem-solving, designing and testing computing and IT systems, and be aware of the limitations involved.

Practical and/or professional skills

On completion of this degree, you will be able to:

  • be an independent learner, able to acquire further knowledge with minimal guidance or support
  • use appropriate professional tools, including programming languages, to support your work
  • apply mathematical, statistical and computational concepts, principles and methods
  • analyse and evaluate problems and plan strategies for their solution
  • analyse, design, evaluate and/or test models and systems, using appropriate simulation and modelling tools as appropriate
  • identify and address the ethical, social and legal issues that may arise during the development and use of computing and IT systems.

Key skills

On completion of this degree, you will be able to demonstrate the following skills:

  • communicate information, arguments, ideas and issues clearly and in appropriate ways, bearing in mind the audience for and the purpose of the communication
  • find, assess and apply information from a variety of sources, using information technology where appropriate
  • select, and use accurately, appropriate numerical and analytical techniques to solve problems
  • prepare mathematical, statistical and computational content for a range of purposes, which may include writing for both specialist and non-specialist audiences
  • recognise and understand a range of technological and practical problems and select suitable techniques for solving them.

Teaching, learning and assessment methods

Knowledge and understanding, as well as cognitive skills, are acquired through distance-learning materials that include specially written module texts, guides to study, assignments and specimen examination papers; through a range of multimedia material including computer software and through feedback from tutors on your assignments.

You will work independently with the distance-learning materials, and will be supported by optional tutorials online, which you are strongly advised to attend whenever possible.

Written tutor feedback on assignments provides you with individual tuition and guidance. Modules at higher levels build on the foundations developed in modules at lower levels.

Each module has a final examination or end of module assessment. In any mathematics and statistics module that has an unseen examination, you’re permitted either a module handbook or are provided with any non-trivial formulae required in the examination paper. This reduces the need for memorisation and enables you to concentrate on applying concepts and techniques. For each module, the final result will be based on a combination of the examination (or end-of-module assessment) score and the score on (or engagement with) the continuous assessment. In some cases there is a threshold on individual components.