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Curricular information is subject to change
The student will be familiarised with the basic multivariate techniques, why they work, how to use them 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.
Additionally students will be equipped with the necessary transferable skills for statistical analysis in the real world: developing application domain knowledge and presenting reports of analyses.
Anticipated content:
Introduction to multivariate data.
Mathematical necessities.
Clustering
Classification
Multidimensional scaling
Principal components analysis
Factor analysis
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 11 |
Specified Learning Activities | 32 |
Autonomous Student Learning | 48 |
Total | 115 |
Statistics modules covering inference, regression, maximum likelihood estimation. Some knowledge of vectors and linear algebra including eigenvalues & eigenvectors.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Exam (In-person): 2hr in-person written exam. | n/a | Standard conversion grade scale 40% | No | 60 |
|
Assignment(Including Essay): Data analysis project | n/a | Standard conversion grade scale 40% | No | 40 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Ms Sinead Mcparland | Lecturer / Co-Lecturer |
Mr Ganesh Babu | Tutor |
Ms Laura Craig | Tutor |
Mr Brian Hassett | Tutor |
Koyel Majumdar | Tutor |