58³Ô¹Ï

Course information may change. Module information and course content, teaching and assessment may change each year and after you have accepted your offer to study at the University of 58³Ô¹Ï. We display the most up-to-date information possible, but this could be from a previous academic year. For the latest module information, see the module catalogue.

• Compulsory

  • 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.
  • Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.

• Optional

  • 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.
  • Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
  • Data-Intensive Systems: CS5052 is an advanced research-focused module, which presents the programming paradigms, algorithmic techniques, and design principles for large-scale distributed systems
  • Dissertation for MSc Programme/s
  • Dissertation in Computer Science: This module is an individually supervised MSc project on a topic suitable to the student's programme in the School of Computer Science.
  • Group Project and Dissertation in Computer Science: This module is a group-based MSc project on a topic suitable to the students' programmes in the School of Computer Science.
  • Information Visualisation: This module provides an introduction to information visualisation. It focuses on the question of how to utilise visual representations to make information accessible for exploration and analysis.
  • Machine Learning for Data Analysis: This module covers many of the methods found under the banner of Datamining, building from a theoretical perspective but ultimately teaching practical application.
  • Masters Programming Projects: This module reinforces key programming skills gained in CS5002, by means of a series of coursework assignments posed as small programming projects.
  • Object-Oriented Modelling, Design and Programming: introduces and revises object-oriented modelling, design and implementation up to the level required to complete programming assignments within other MSc modules.
  • Programming Principles and Practice: This module 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 an 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.