| Module Code | STP80130 |
| Module Name | Multivariate Analysis |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 2 |
| Module Coordinator/s | Dr Emma Howard |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Define and describe various classical dimension reduction techniques for multivariate data.
- Implement clustering and/or classification algorithms and assess and compare the results.
- Implement and interpret output of data analysis performed by R.
Module Content
Topics covered in this module are:
- Principal Components Analysis
- Multidimensional Scaling
- K-Nearest Neighbours
- Discriminant Analysis
- Hierarchical Clustering
- K-means Clustering
- R as applied to multivariate analysis
Teaching and Learning Methods
This module is taught through a blended approach over 4 weekly sessions. The content is taught online. All content is available through Blackboard. Tutorials will be taught in person by the module lecturer and/or teaching demonstrator.
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Continuous Assessment | Written project with short video presentation | L01-L03 | 40% | N/A | N/A |
| Examination | Online exam | L01-L03 | 60% | N/A | N/A |
Reassessment Details
Supplemental assessment is by examination only (100%).
Contact Hours and Indicative Student Workload
| Contact Hours (Lecture Video and Tutorial): | 12 hours |
| Independent study (outside scheduled contact hours), broken down by: | 113 hours |
| Total Hours | 125 hours |
Recommended Reading List
Material will be provided as needed. Further reading material will be recommended for each session.
Module Pre-requisites
Prerequisite modules:
Other/alternative non-module prerequisites: N/A
Module Co-requisites
N/A