Capstone Project

This information is for the 2021/22 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 is a collaborative project, providing students with the opportunity to work in groups studying 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 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 students will prepare and submit a group project report, as well as a short individual 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).

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Student performance results

(2017/18 - 2019/20 combined)

Classification % of students
Distinction 38.3
Merit 55
Pass 6.7
Fail 0

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Statistics

Total students 2020/21: 30

Average class size 2020/21: Unavailable

Controlled access 2020/21: 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