Data Analytics Professional Portfolio (ODL) STATS5092P
- 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
This course constitutes the dissertation phase of the MSc programmes in Data Analytics (ODL). It gives students with an opportunity to critically reflect upon their own professional practice, taking into account both the knowledge acquired on the MSc programme and the relevant literature.
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.
Students can only take this course if their proposal for the portfolio has been accepted by the project management team before the start of the semester in which they want to enrol for the course. If their proposal is rejected students must choose STATS5093P instead.
Data Analytics Project (ODL)
20% presentation and mini viva, 80% final submission of a portfolio, which includes a dissertation
This course aims to provide students with an opportunity to critically reflect upon their own professional practice. It is intended that students describe substantial project work they have already carried out themselves in the area of data analytics and construct and discuss what would constitute best practice in such situations, taking into account both the knowledge acquired on the MSc programme and the relevant literature.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. critically reflect upon their own professional practice;
2. integrate and consolidate the knowledge and skills they have gained from other components of their degree programme;
3. critique a plan for a statistical analysis or other data-analytic work, as well as its execution
4. investigate and discuss the merits and risks involved in using different approaches to tackle a data analytics problem;
5. critically evaluate the relevant literature and software and make connections between them and the project(s) to be investigated;
6. assemble and discuss best practice guidelines for specific data analysis problems, taking into account technical and ethical issues as well as organisational and other constraints;
7. present key results and conclusions to both technical and non-technical audiences; and
8. synthesise and write up the lessons from the enquiry highlighting key lessons learned and their implications.
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.