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 2024 in Semester 2 at Caulfield. It needs ETC1010, ETF3231, ETC2410, ETC2420, ETW2001, ETC3550, ETX2250, ETF2020 or ETF2100.
- Credit points
- 6
- Offered in 2024
- Semester 2
- Caulfield
- Assessment
- Exam 50%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2025 entry.
Reviews
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Requisites
Before ETF3500
Prerequisites
Pass these before you enrol.
- ETC1010Introduction to data analysisNo reviews yet
- ETF3231Business forecastingNo reviews yet
- ETC2410Introductory econometricsNo reviews yet
- ETC2420Statistical thinkingNo reviews yet
- ETW2001Foundations of data analysis No reviews yet
- ETC3550Applied forecastingNo reviews yet
- ETX2250Data visualisation and analyticsNo reviews yet
- ETF2020Statistical foundations of business analyticsNo reviews yet
- ETF2100Introductory econometricsNo reviews yet
After ETF3500
No unit lists ETF3500 as a prerequisite in the 2024 handbook.
Enrolment rules
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.
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 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 in 2024
| 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
- Seminars24 hours
- Tutorials18 hours
- Teaching approachActive learning
- Teaching approachProblem-based learning
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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Where it fits
ETF3500 is part of 5 areas of study in the 2024 handbook.
- BUSANLMJ01Additional business analytics unitsBusiness analyticsNo reviews yet
- BUSANLYT08Additional business analytics unitsBusiness analyticsNo reviews yet
- BUSSTATS05Level 2 and 3 elective unitsBusiness analytics and statisticsNo reviews yet
- BUSSTATS06Core unitsBusiness analytics and statisticsNo reviews yet
- FINECMTR01Additional financial econometrics unitsFinancial econometricsNo reviews yet
Contacts
- Chief Examiners
- Associate Professor Ole Maneesoonthorn
Common questions
What are the prerequisites for ETF3500?
You need ETC1010, ETF3231, ETC2410, ETC2420, ETW2001, ETC3550, ETX2250, ETF2020 or ETF2100 before you enrol. Enrolment rules also apply.
When is ETF3500 offered?
In 2024, 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; Business analytics and statistics; and Financial econometrics.