ETX3250 Predictive analytics and machine learning
Faculty of Business and Economics
ETX3250 Predictive analytics and machine learning is a level 3, 6-credit-point, undergraduate unit from the Faculty of Business and Economics, offered in 2022 in Semester 1 at Caulfield. It needs ETW2001, ETX2250, ETC1010 or ETF2020.
- Credit points
- 6
- Offered in 2022
- Semester 1
- Caulfield
- Assessment
- Exam 50%
- and 1 other task
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before ETX3250
After ETX3250
No unit lists ETX3250 as a prerequisite in the 2022 handbook.
Enrolment rules
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
Many problems in business including sales and inventory forecasting, credit scoring, recommender systems in online commerce and fraud detection use advanced tools for data analytics. This unit covers some of the most popular tools that may include tree-based methods, boosting, bagging, support vector machines, neural networks and deep learning. The algorithmic details of each method, their implementation using popular software tools (such as R) and their application to real business problems will all be covered.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Caulfield | On campus |
Assessment
- Within semester assessment50%
- Examination50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
understand different techniques used in business analytics and to be able to compare these from a statistical and computational point of view
- 2
frame problems in finance, marketing, economics and related areas so that they can be solved by modern tools in business analytics
- 3
implement machine learning methods in a modern software environment (for example, R) with potentially large datasets
- 4
explain and interpret the analyses undertaken in a clear and effective manner and be aware of the limitations of these analyses.
Workload and teaching
- Lectures24 hours
- Tutorials18 hours
- Teaching approachActive 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. 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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Where it fits
ETX3250 is part of 4 areas of study in the 2022 handbook.
- BUSANLMJ01Additional business analytics unitsBusiness analyticsNo reviews yet
- BUSANLYT05Additional business analytics unitsBusiness analyticsNo reviews yet
- BUSSTATS05Level 2 and 3 elective unitsBusiness analytics and statisticsNo reviews yet
- BUSSTATS06Core unitsBusiness analytics and statisticsNo reviews yet
Contacts
- Chief Examiners
- Dr Ruben Loaiza Maya
Common questions
What are the prerequisites for ETX3250?
You need ETW2001, ETX2250, ETC1010 or ETF2020 before you enrol. Enrolment rules also apply.
When is ETX3250 offered?
In 2022, ETX3250 runs in Semester 1 at Caulfield.
How much work is ETX3250?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETX3250 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.
Which majors and minors include ETX3250?
ETX3250 is part of Business analytics; and Business analytics and statistics.