2016/17 Taught Postgraduate Module Catalogue
MATH5745M Multivariate Methods
15 creditsClass Size: 20
Module manager: Dr Arief Gusnanto
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2016/17
Pre-requisite qualificationsMATH5741M, or equivalent.
This module is mutually exclusive with
This module is not approved as an Elective
Module summaryIn big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
ObjectivesTo introduce basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
On completion of the module, the student should:
- be able to discover and exploit dependency between variables;
- be able to reduce the dimension of a dataset with dependent components, and interpret the results;
- be able to identify clusters in a given data set;
- be able to visualise similarities between observations in lower dimension.
- Introduction to multivariate analysis
- Statistical dependence, covariance matrix
- High dimensional problems, the "curse of dimensionality"
- Principal Component Analysis (PCA), dimension reduction
- Clustering, K-means method, distances between/within clusters
- Multidimensional Scaling (MDS)
|Delivery type||Number||Length hours||Student hours|
|Independent online learning hours||22.00|
|Private study hours||95.00|
|Total Contact hours||33.00|
|Total hours (100hr per 10 credits)||150.00|
Private studyThe student will be expected to complete regular written worksheet assignments testing their understanding of theoretical course elements.
The student will learn to perform analysis using the software package R, which includes performing dimension reduction such as principal component analysis and factor analysis, clustering such as hierarchical and k-means clustering, multi-dimensional scaling, and learning different types of plots for presenting high-dimensional data into informative two-dimensional. Part of the assessment for the module consists of a practical, where the student will apply these techniques to a real-world data set.
Opportunities for Formative FeedbackMonitoring by regular worksheets and achievement in supervised practical sessions.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||30.00|
There is no resit available for the coursework components of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.
|Exam type||Exam duration||% of formal assessment|
|Unseen exam||2 hr 00 mins||70.00|
|Total percentage (Assessment Exams)||70.00|
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading listThe reading list is available from the Library website
Last updated: 08/04/2016
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