Data-Intensive Analysis (MSc) 2025 entry

For more details, including weekly contact hours, teaching methods and assessment, please see the module catalogue. The modules are examples from previous academic years and may be subject to change before you start your course.

• Compulsory

  • Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.
  • Advanced Data Analysis: covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice.
  • Knowledge Discovery and Datamining: covers many of the methods found under the banner of datamining, building from a theoretical perspective but ultimately teaching practical application.
  • Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.

• Optional

  • Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
  • Data-Intensive Systems: presents the programming paradigms, algorithmic techniques and design principles for large-scale distributed systems, such as those utilised by companies such as Google, Amazon and Facebook.
  • Information Visualisation: explores how to utilise visual representations to make information accessible for exploration and analysis.
  • Masters Programming Projects: reinforces key programming skills gained during the first programming module of the programme and offers increasing depth and scope for creativity.
  • Object-Orientated Modelling, Design and Programming: introduces and reinforces object-orientated modelling, design and implementation to provide a common basis of skills, allowing students to complete programming assignments within other MSc modules. The module assumes a substantial amount of prior programming experience equivalent to having completed an undergraduate degree in Computer Science.
  • Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.
  • Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.

• Project

  During the second semester, students work with staff to define and agree upon a topic for the extended project, which they will work on during the final three months of the course, and which culminates in a 15,000-word dissertation. Dissertation projects may be group-based or completed individually, however, students are assessed individually in either case.

  The dissertation typically comprises:

  • a review of related work
  • the extension of existing or the development of new ideas
  • software implementation and testing
  • analysis and evaluation

  Students may be required to give a presentation of their work in addition to the written dissertation.

  Each project is supervised by one or two members of staff, typically through regular meetings and reviews of software and dissertation drafts. Supervisors and topics may be from either of the schools of Computer Science or Mathematics and Statistics and many are in collaboration with companies or other external bodies.