MSc Data Analytics

Gain an in-depth understanding of the theory and practice of data science and its application in different organisational contexts. You'll be provided with a set of fundamental principles that support ethical extraction of information and knowledge from data. Case studies will help to show the practical application of these principles to real life problems.

The programme is centred on three key aspects of data science fundamental data-related principles, supporting infrastructures and organisational context.

You'll gain practical skills in handling structured and unstructured data, analysing and visualising data, data mining, as well as gaining hands-on experience of software tools used and their use in real-world settings. You'll gain the skills of a data manager who understands what the algorithms (e.g., for data mining or handling ‘big data’) can do and when to use them for the benefit of the organisation.

Throughout the programme, there will be opportunities to gain hands-on experience using a variety of tools, such as R, Python and SPSS, Weka or Tableau/Spotfire and, although you're not required to have any knowledge of these tools before starting the course, it may be advantageous to read up on them in advance. You'll conduct in-depth research into your particular areas of interest for your dissertation.

CILIP accredited

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Modules

  • Scalable Machine Learning
  • Text Processing
  • Machine Learning and Adaptive Intelligence
  • Natural Language Processing
  • Professional Issues
  • Team Project
  • Statistical Data Science in R
  • Data Analytics Dissertation Project
  • Modelling and Simulation of Natural Systems
  • Computer Security and Forensics
  • Network Performance Analysis
  • Web Technologies
  • Parallel Computing with Graphical Processing Units (GPUs)
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    £28,700 Per Year

    International student tuition fee

    1 Year

    Duration

    Sep 2025

    Start Month

    Aug 2025

    Application Deadline

    Upcoming Intakes

    • September 2025
    • September 2026

    Mode of Study

    • Full Time