Data Analytics Project (ODL) STATS5093P

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 60
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Full Year
  • Available to Visiting Students: No
  • Available to Erasmus Students: No
  • Taught Wholly by Distance Learning: Yes

Short Description

This course constitutes the dissertation phase of the MSc programmes in Data Analytics (ODL). It gives students with an opportunity to work independently on a data analysis or software development project, showcasing the skills acquired on the MSc programme.

Timetable

None.

Requirements of Entry

The course is only available to students on the MSc in Data Analytics by online distance learning and the MSc for Data Analytics in Government by online distance learning.

Excluded Courses

Data Analytics Professional Portfolio (ODL)

Assessment

20% presentation and mini viva, 80% final submission of a portfolio, which includes a dissertation

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 and executive summary.

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.