Data Science Project
Lecturer: Dr Elizabeth Sklar (office hours)
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
Module Pass Mark: 50%
08 September 2017