Capstone Project

This information is for the 2019/20 session.

Teacher responsible

A project 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 will provide students with the opportunity to study in depth a topic of specific interest. 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 capstone project is typically conducted in partnership with a company partner and is jointly supervised by the LSE faculty and company partner collaborators. The capstone project partner proposes a data science research project, potentially provides access to data, and engages through participation in joint meetings that are either online or onsite. The capstone project may require students to spend some time on company partner’s premises, for example, to have access to data. The capstone project requires creative work in formulating research questions and hypotheses, identifying most suited methodology, referring to research literature, and analysing data sources using data science computing technologies.


A topic and project 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 project report by a date in August.

Formative coursework

Formative assessment is via informal feedback from supervisors on the project report and contributions to the project as an individual contributor and team member.

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


Project (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 2018/19: 16

Average class size 2018/19: Unavailable

Controlled access 2018/19: 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