| 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 algorithms and assess and compare the results.
- Implement 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 over 4 weekly sessions (weeks 9-12 of the TCD Hilary Teaching Term). The module content is taught through online videos. All content is available through Blackboard. Each week will also feature a one hour synchronous online tutorial and a one hour synchronous R software lab. These will be taught by the module lecturer and/or teaching assistant.
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Continuous Assessment | Individual project | L01-L04 | 80% | N/A | N/A |
| Continuous Assessment | Session quizzes | L01-L03 | 20% | N/A | N/A |
Reassessment Details
Reassessment is a take-home exam (5hrs duration).
Contact Hours and Indicative Student Workload
| Contact Hours (Video Material): | 10 hours |
| Contact Hours (Online tutorials and software labs): | 8 hours |
| Independent study (outside scheduled contact hours), broken down by: | 100 hours |
| Total Hours | 116 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