Capstone Project

This information is for the 2023/24 session.

Teacher responsible

Dr Marcos Barreto (course co-ordinator). A project supervisor will be identified during the Autumn Term (AT).


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


There are no formal pre-requisites to this course. The students should master a programming language (such as Python) and a set of data science methodologies and tools learnt and/or improved during their MSc programme. Other specific requisites depend on the problem being addressed and data/models to be used.

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 an industry partner and is jointly supervised by the LSE faculty and company partner collaborators. The 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 the 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 approaches and technologies.


A topic and project supervisor will be identified during AT. Supervisors will provide formal advice from the end of AT until two weeks after the end of ST. Project partners will engage with students in weekly or bi-weekly meetings, agreed at convenience of both sides. The students are expected to be proactive in communicating with and asking for technical support from their partners and LSE supervisor.

The students should attend all planned meetings (proposals presentation, kick-off meeting with partners, and all-hands meetings) and deliver a draft report (some date in June) and a final report (some date in August). They should also attend all meetings with the partner and engage with the agreed activities.

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.

Indicative reading

* J. Burke, M. Dempsey. Undertaking capstone projects in education: a practical guide to students. Routledge, 2022.

* J. Poulin, S. Kauffman, T. Ingersoll. Social work capstone projects: demonstrating professional competencies through applied research. Springer, 2021.

* J. Chong, Y. Chang. How to lead in data science. Manning, 2021.

* M. Braschler, T. Stadelmann, K. Stockinger. Applied data science. Springer, 2019.

* M. Carey. The social work dissertation: using small-scale qualitative methodology. 2nd edtion, Open University Press, 2013.

* D. Patil. Building data science teams. O'Reilly, 2011.



Project (100%) in August.

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

Student performance results

(2019/20 - 2021/22 combined)

Classification % of students
Distinction 37.5
Merit 58.8
Pass 3.8
Fail 0

Key facts

Department: Statistics

Total students 2022/23: 35

Average class size 2022/23: Unavailable

Controlled access 2022/23: No

Value: One Unit

Guidelines for interpreting course guide information

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.

Personal development skills

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