ETF3500 - High dimensional data analysis

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Overview

In many fields of business, analysts must deal with data on many variables, for example, surveys with a large number of questions. In such cases, statistical tools known as multivariate methods must be used to analyse the data and drive business decisions. This unit covers such methods in three sections: … For more content click the Read More button below.

This unit covers such methods in three sections: Cluster Analysis can be used to identify and predict differences between groups such as between distinct classes of customers or products; Principal Components Analysis, Correspondence Analysis and Multidimensional Scaling are dimension reduction methods that help analysts to visualise complicated datasets; and finally, Factor Analysis is used to explain and predict business outcomes.

Offerings

S2-01-CAULFIELD-ON-CAMPUS

Requisites

Prerequisite

Rules

Enrolment Rule

If you do not meet the prerequisite you must be granted permission by the Chief Examiner to undertake this unit.

To be successful in this unit, background knowledge and application of maths is required at the equivalent of VCE Year 12 level. You may have satisfied this by completing relevant prerequisite unit/s, or you have covered relevant topics in your final years of secondary study. You should self-assess your maths competency prior to enrolling in this unit.

Contacts

Chief Examiner(s)

Dr Ruben Loaiza Maya

Learning outcomes

On successful completion of this unit, you should be able to:

demonstrate an understanding of the role that multivariate statistical techniques such as factor analysis, structural equation modelling, categorical data analysis, cluster analysis, multidimensional scaling and correspondence analysis play in uncovering relationships and patterns in survey data

appraise the strengths and limitations of these techniques

apply tools in R to generate solutions for the appropriate statistical techniques

demonstrate skills in using the appropriate statistical techniques from a user and provider perspective

demonstrate skills in communicating the results of the analysis so that decision making can be implemented.

Teaching approach

Active learning

Assessment

1 - Within semester assessment

2 - Examination

Scheduled and non-scheduled teaching activities

Lectures

Tutorials

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.