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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Natural Sciences

DATA40160: Data Science Research Project

Type Tied Level 4 Credits 60 Availability Available in 2021/22
Tied to G5K823 Data Science
Tied to G5K923 Data Science (Digital Humanities)
Tied to G5P123 Data Science (Earth and Environment)
Tied to G5P223 Data Science (Bioinformatics and Biological Modelling)
Tied to G5P323 Data Science (Health)
Tied to G5P423 Data Science (Social Analytics)

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To allow students to conduct, via individual initiative, a substantial piece of research into an unfamiliar area of Data Science, or in the subject specialisation area with a focus on Data Science.
  • 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 written and presentations skills in a scholarly fashion.

Content

  • Students are expected to choose a project from a list offered by potential supervisors, or propose a topic of their choice if an appropriate supervisor can be identified.
  • 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 science component.
  • Projects may be practically or theoretically based or both.
  • 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 and management skills.
  • 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 data science leading to new research results either in the methodological area or the chosen specialisation area.
  • have a deep understanding of the state of the art in their chosen area of specialisation demonstrated through critical analysis of relevant literature identified.
  • have an in-depth knowledge and understanding of their chosen area of specialisation.
  • have appreciable levels of the research skills and methods required in conducting a successful research-based project.
Subject-specific Skills:
  • Students will be able to:
  • propose and carry out comprehensive research appropriate to a project.
  • demonstrate effective project planning, including the ability to evaluate their own project planning skills.
  • assimilate, critically evaluate, and analyse information.
  • identify appropriate related research material along with the skills to critique this work in the context of their own project.
  • formulate effective solutions to a problem, making effective use of time and resources available.
  • create solutions to their problem.
  • manage personal learning.
  • reflect and critique their own work against their own aims and objectives.
  • critically evaluate their own learning, progress and quality of solution objectively.
  • prepare and deliver technical writing at a high level of quality.
  • present properly referenced documents, with citations, references and bibliographies.
  • exercise critical self-evaluation.
  • present and interpret results effectively and relate these to the aims and objectives of their work.
Key Skills:
  • The capacity for independent self-learning.
  • Effective communication of general and specialised Data Science 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 arrange regular meetings with their supervisor at intervals relevant to the individual project, giving formative feedback on the suitability of the implementation and report.
  • The research conducted and the implementation developed will be written up in the form of a report, the format of which will be determined in discussion with their supervisor to suit the project. The report will be a maximum of 60 pages and may include additional supporting materials such as data sets, programming code and dashboards.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Supervision session Term 3 5
Preparation, Exercises and Reading 595
Total 600

Summative Assessment

Component: Dissertation Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Dissertation max 60 A4 pages 100%

Formative Assessment:

Feedback on progress is given during regular 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