Anyone who has experience in basic statistics or data science, but wishes to learn how to handle large, multidimensional datasets. You are motivated by a need to understand complex structures within data, beyond what is possible using standard techniques.
Multivariate Analysis (online)
Overview
| Module Code | STAT40740 |
| Module Title | Multivariate Analysis (online) |
| Subject Area | Data Science |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | 19th January 2026 |
| Duration | 12 Weeks |
| Time | N/A |
| Mode of Delivery | Online |
| Course Leader | Dr. Garrett Greene |
| Fee | €900 |
| Application Deadline | 5th January 2026 |
This module will cover many common statistical techniques used to analyse high dimensional data. Topics include: clustering techniques; classification techniques; ordination techniques such as principal components analysis; and graphical techniques such as multidimensional scaling. All analyses will be conducted using the R software.
The student will be familiarised with the basic multivariate techniques and where their use is appropriate. The student will develop skills to conduct an analysis of multivariate data using statistical software, interpret the results and draw conclusions. The student will be made aware of the advantages and limitations of each method.
Anticipated content:
- Introduction to multivariate data.
- Mathematical necessities.
- Clustering
- Classification
- Multidimensional scaling
- Principal components analysis
- Factor analysis
You will become able to quickly analyse highly complex data to pull out the key structures within the data. You will know how to visualise, simplify, and summarise such data. You will be able to select from a suite of methods and interpret and present results using plots and tables.
Video lectures posted each week that walk through module content, blending theory with example exercises. Practice problem sheets to enable self-assessment of learning outcomes. All content delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set.
Prior learning requirements: Basic statistics modules covering e.g. hypothesis testing, inference, regression, maximum likelihood. Elementary matrix algebra including eigenvalues and eigenvectors.
Assignment – 40%
Exam – 60%
Group/class feedback, post-assessment.
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