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Data Science Project

Lecturer: Dr Elizabeth Sklar (office hours)

Semester: 3

Credit level: 7

Credit value: 60


MSc Data Science

Learning aims & outcomes

To provide students with an opportunity to demonstrate practical research skills, critical judgement and computer science and statistical competence in a Data Science application or research project, encompassing analysis of a real-world data set or the development or evaluation of advanced Data Science methods or techniques.

On successful completion of this module, students will be able to:

  • Development and Understanding
    • Integrate comprehensive disciplinary knowledge in support of their particular project topic.
    • Demonstrate critical awareness of personal responsibility and professional codes of conduct in relation to the context of their project.
    • Work with theoretical and research-based knowledge within Data Science as an academic discipline.
  • Cognitive / Intellectual skills
    • Analyse complex areas of knowledge.
    • Aynthesise potentially innovative solutions.
    • Critically evaluate their project solution and related approaches.
    • Apply originality and autonomy in problem solving.
  • Key / transferable skills
    • Manage their own learning and information in support of their project work and undertake self-evaluation of their work.
    • Demonstrate autonomous working practice with the ability to communicate progress to supervisory and assessment teams.
    • Show independent learning ability.
  • Practical skills
    • Operate in specialised and unpredictable contexts.
    • Exercise initiative and personal responsibility in professional practice.
    • Can adapt / design / develop skills and demonstrate technical expertise.


The project provides students with an opportunity to engage in a self-managed and detailed investigation of a focussed aspect of Data Science. A substantial piece of written work will be produced, including reviews of literature and related work, design and implementation of software to support experimentation with real-world data, and report on students' own experimental results based on real-world data.

Weekly teaching arrangements

No weekly teaching. A member of academic staff supervises work



Summative assessment

Details of the module's summative assessment/s
 Type Weighting Marking model
Dissertation 100% Model 2 - Double Marking

Formative assessment


Module Pass Mark: 50% 

Suggested reading/resources

08 September 2017
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