ITO5212 Data analysis for semi-structured data
Faculty of Information Technology
ITO5212 Data analysis for semi-structured data is a level 5, 6-credit-point, postgraduate unit from the Faculty of Information Technology. It isn't offered in 2026. It needs ITO5197.
- Credit points
- 6
- Offered in 2026
- Other periods
- Monash Online
- Assessment
- No exam
- 4 tasks
This is the 2026 handbook entry. See the 2027 entry.
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Requisites
Before ITO5212
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
After ITO5212
No unit lists ITO5212 as a prerequisite in the 2026 handbook.
Equivalent units
The same content under another code. Only one of them counts.
Overview
Semi-structured data is one of the fastest growing kinds of data in both the public and private sector, for instance in health. Email collections with sender-recipient graphs, metadata and text content is one example. This unit will explore basic forms of semi-structured data: text, time-sequence data, graphs and multiple relations in a database. Basic machine learning algorithms for these kinds of data will be analysed and applied. Some characteristic industry problems for the application of semi-structured data will also be investigated.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Teaching period 5 | Monash Online | Mo |
Assessment
- Quiz 1Quiz / Test15%
- Text ClassificationArtefact35%
- Quiz 2Quiz / Test15%
- Recommender System ChallengeArtefact35%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Appraise what kinds of semi-structured data exist and the problems they present for analysis;
- 2
Analyse different kinds of algorithms for different kinds of semi-structured data;
- 3
Develop and modify some standard algorithms for semi-structured data;
- 4
Examine some characteristic industry problems involving semi-structured data, and analyse the suitability of different algorithms.
Workload and teaching
- Workshops12 hours
- Teaching approachOnline learning
A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.
Learning resources
Technology resources
You will be using Python and Jupyter Notebook for assignments and laboratories. You are recommended to bring your own laptop for these.
Contacts
- Chief Examiners
- Professor Wray Buntine
Common questions
What are the prerequisites for ITO5212?
You need ITO5197 before you enrol.
When is ITO5212 offered?
ITO5212 has no offerings listed in the 2026 handbook.
Does ITO5212 have an exam?
No. ITO5212 has 4 assessment tasks and no exam.