Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Statistics Project and Dissertation (with Placement) STATS5090P

  • Academic Session: 2021-22
  • School: School of Mathematics and Statistics
  • Credits: 60
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Summer
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This project provides Master's-level students in Statistics with an opportunity to carry out a placement in another organisation (industry, government, etc.), and to present their investigation in the form of a dissertation.


Supervisory meetings to be arranged at certain fixed points, typically, weeks 2, 5/6 and 8/9, with additional weekly support sessions.

Requirements of Entry

Selection for this project will be on the basis of successfully securing an appropriate industrial placement and getting it approved by a date (around April) specified by the MSc Programme Director.

Excluded Courses

Statistics Project and Dissertation (STATS5029P)

Advanced Statistics Project and Dissertation (STATS5XXXP)


Interim assessment (20%, including a presentation and mini-viva) + dissertation (80%).

Course Aims

This course aims to provide students with an opportunity to practice their data-analytic skills acquired on the programme. It is intended that most project focus on the analysis of a complex real-world data set using advanced data analytic methods and/or on the development of software to carry out complex data-analytic tasks. The course also aims to train students in discussing their work with others, presenting it to an audience and synthesising conclusions in a report.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. design and execute a project plan for an appropriate data analysis or software development project;

2. investigate and discuss the merits and risks involved in the approach taken as well as other strategies that could have been employed;

3. integrate and consolidate the knowledge and skills they have gained from other components of their degree programme;

4. implement and/or use both standard and advanced data analytic methods in a real-world context;

5. critically reflect upon their work discussing assumptions and limitations;

6. present key results and conclusions to both technical and non technical audiences; and

7. document their work and synthesise and write up results and conclusions in a concise report.

8. defend their analysis and conclusions in a mini-viva.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.