ETX5500 · High dimensional data analysis

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: 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

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. demonstrate an understanding of the role that multivariate statistical techniques such as factor analysis, structural equation modelling, logistic regression, categorical data analysis, cluster analysis, multidimensional scaling and correspondence analysis play in uncovering relationships and patterns in survey data
2. appraise the strengths and limitations of these techniques
3. apply tools in R to generate solutions for the appropriate statistical techniques
4. demonstrate skills in using the appropriate statistical techniques from a user and provider perspective
5. demonstrate skills in communicating the results of the analysis so that decision making can be implemented.

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. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.