ETF3500 High dimensional data analysis
Faculty of Business and Economics
ETF3500 High dimensional data analysis is a level 3, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2020 in Semester 2 at Caulfield. It needs ETF5910, ETC2420, ETW2410, ETC3440, ETC2410 or ETF2100.
- Credit points
- 6
- Offered in 2020
- Semester 2
- Caulfield
- Assessment
- Exam 50%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2020 handbook entry. See the 2025 entry.
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Requisites
Before ETF3500
Prerequisites
Pass these before you enrol.
After ETF3500
No unit lists ETF3500 as a prerequisite in the 2020 handbook.
Equivalent units
The same content under another code. Only one of them counts.
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, Discriminant Analysis and MANOVA can be used to identify, predict and test for differences 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 and Structural Equation Modelling are used to predict and test theories and explain and predict business outcomes.
Offerings in 2020
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Caulfield | On campus |
Assessment
- Within semester assessment50%
- Examination50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
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
- 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 and teaching
- Lectures24 hours
- Laboratories12 hours
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.
Where it fits
ETF3500 is part of 4 areas of study in the 2020 handbook.
Contacts
- Chief Examiners
- Dr Ruben Loaiza Maya
Common questions
What are the prerequisites for ETF3500?
You need ETF5910, ETC2420, ETW2410, ETC3440, ETC2410 or ETF2100 before you enrol.
When is ETF3500 offered?
In 2020, ETF3500 runs in Semester 2 at Caulfield.
How much work is ETF3500?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETF3500 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.
Which majors and minors include ETF3500?
ETF3500 is part of Business analytics and Business statistics.