Durham University
Programme and Module Handbook

Postgraduate Programme and Module Handbook 2018-2019 (archived)

Module COMP52060: Project

Department: Computer Science

COMP52060: Project

Type Tied Level 5 Credits 60 Availability Not available in 2018/19 Module Cap



Excluded Combination of Modules


  • To allow students to conduct via individual initiative a substantial piece of research into an unfamiliar area of Scientific Computing or Data Analysis, or in the subject specialisation area, or in a related area brought forward by industry partners.
  • To allow students to propose, develop and critically evaluate their work.
  • To allow students to evaluate and select the most appropriate research methods and skills relevant for conducting their project.
  • To provide an opportunity for students to demonstrate originality in their application of knowledge they have gained through their degree, along with the ability to identify appropriate gaps in their knowledge and conduct independent learning to address these gaps.
  • To critically analyse background literature within their chosen domain in order to set their work in context.
  • To enhance students skills in writing up and presenting work in a scholarly fashion.


  • Students are expected to choose a project from a list offered by potential supervisors (from Mathematics, Computer Science, the chosen specialisation area, or from an industry partner in collaboration with an academic from either of these disciplines).
  • Projects are inevitably and deliberately very varied in the topics they address and in the type of approach required; the common factor is that they are research-led and have a strong data analysis or compute component.
  • For example they may be practically or theoretically based. Many projects will consist of a combination of these.
  • All projects are open-ended and contain considerably more work than can be achieved in the available time. Students therefore need to evaluate the problem domain and propose the elements of the greater problem they will address.
  • One of the main outcomes of the project is a significant academic-quality report.
  • Successful completion requires good organisation, communication skillss and management.
  • Management is the responsibility of the student, in regular consultation with the supervisor.

Learning Outcomes

Subject-specific Knowledge:
  • On completion of this module, students will be able to demonstrate a detailed knowledge and understanding of one or more aspects of scientific computing or data analysis leading to new research results either in the methodological area or the chosen specialisation area.
  • On completion of this module, students will be able to demonstrate mastery of the professional skills (version control, testing, code documentation) of Core I.
  • A deep understanding of the state of the art in the student’s chosen area of specialisation demonstrated through critical analysis of relevant literature identified by the student.
  • An in-depth knowledge and understanding of the student's chosen area of specialisation.
  • Appreciable levels of the research skills and methods required in conducting a successful research-based project.
Subject-specific Skills:
  • To be able to propose and carry out comprehensive research appropriate to a project.
  • To be able to demonstrate effective project planning, including the ability to evaluate their own project planning skills.
  • To be able to assimilate, critically evaluate, and analyse information.
  • To identify appropriate related research material along with the skills to critique this work in the context of their own project.
  • To be able to formulate effective solutions to a problem, making effective use of time and resources available.
  • To be able to create solutions to their problem.
  • To be able to manage personal learning.
  • The ability to reflect and critique their own work against their own aims and objectives.
  • To be able to critically evaluate their own learning, progress and quality of solution objectively.
  • To be able to prepare and deliver technical writing at a high level of quality.
  • To be able to present properly referenced documents, with citations, references and bibliographies.
  • To be able to exercise critical self-evaluation.
  • To be able to present and interpret results effectively and relate these to the aims and objectives of their work.
Key Skills:
  • Capacity for independent self-learning.
  • The effective communication of general and specialised Scientific Computing and Data Analysis concepts (written, verbal, presentational, ...)
  • Effective use of IT resources.
  • Time and resource management.
  • Advanced problem solving skills.
  • The ability to propose, conduct and critically evaluate a piece of research within the wider context of their subject area.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Students will receive regular process meetings with their supervisor giving formative feedback on the suitability of the implementation and scientific report.
  • The research conducted and the implementation developed will be written up in the form of the scientific report along with presentations of their work in the form of oral presentations and posters.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Supervisor meetings 10 1/2 hour 5
Self-study 595

Summative Assessment

Component: Dissertation Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Dissertation 100%

Formative Assessment:

Feedback on progress is given during weekly meetings with supervisors. This includes review of drafts of written work.

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University