Statistics Project and Dissertation (with Placement) STATS5090P

  • Academic Session: 2019-20
  • 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.

Timetable

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)

Assessment

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.