Capstone Project/ Dissertation

This information is for the 2017/18 session.

Teacher responsible

A project/dissertation supervisor will be identified during MT.


This course is compulsory on the MSc in Data Science. This course is not available as an outside option.

Course content

The capstone project/dissertation will provide students with the opportunity to study in depth a topic of specific interest. The topic may be identified from a list supplied by the Department or may be proposed by the student. The topic will normally relate to a specific data source or sources and will require the use of data science skills learnt on the programme. The topic for a capstone project will be similar to that for the kinds of data-based issues faced in practice by private or public sector organisations. The focus is likely to be practical and there may be the opportunity to liaise with such an organisation during the project to ensure the project has practical relevance. A dissertation will be more academic; it will refer to a research literature and address a research question, building on that literature and using one or more data sources.


A topic and project/dissertation supervisor will be identified during MT. Supervisors will provide advice from the end of MT until two weeks after the end of ST. The student will prepare and submit a dissertation or project report by the end of August.

The Library has dedicated Data Librarians that can provide training to students on access to and use of datasets and on managing their own data.

Formative coursework

Formative assessment is via informal feedback from supervisors on draft chapters, sections or plans of dissertation/report.

Other courses on the MSc programme will also provide a range of formative assessments of relevance to the outcomes of this project/dissertation.


Dissertation (100%) in August.

Maximum page limit of 50 single-sided sheets of A4 (minimum font size of 11pt and line spacing 1.5).

Key facts

Department: Statistics

Total students 2016/17: Unavailable

Average class size 2016/17: Unavailable

Controlled access 2016/17: No

Value: One Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Commercial awareness
  • Specialist skills