Informatics 5: Applied Analytics

Subject INFO30002 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 3 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 2 x one hour lectures per week, 1 x two hour workshop per week
Total Time Commitment: Estimated total time commitment of 120 hours.
Prerequisites: .
Study Period Commencement:
Credit Points:
Semester 1, Semester 2
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements: It is University policy to take all reasonable steps to minimise the impact of disability upon academic study and reasonable steps will be made to enhance a student’s participation in the University’s programs.
This subject requires all students to actively and safely participate in laboratory activities. Students who feel their disability may impact upon their participation are encouraged to discuss this with the subject coordinator and the Disability Liaison Unit.


Assoc Prof James Bailey


Department of Information Systems
Level 4, 111 Barry Street, Carlton
Subject Overview:

This subject introduces students to advanced data analysis and information management techniques. It includes areas such as automated knowledge discovery (finding relationships and patterns in large and complex data sets), data mining techniques such as clustering, classification, regression and association rules; data mining platforms; spreadsheets as modelling and analysis tools; and decision making technologies and systems.


On completion of this subject students should be able to:

Understand the technologies available for advanced data analysis;

Work with a number of advanced technologies for data manipulation;

Select and implement an appropriate data analysis method for a particular problem

Analyse and solve real-world problems with large, complex data sets.


Two projects, one mid-semester (20%) and one end-of -semester (20%), expected to take about 20 hours each and require a report of about 1000 words each; and a 2-hour end-of-semester written examination (60%).

Prescribed Texts:
  • Ian H. Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Ed, ISBN 0-12-088407-0
Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On completion of this subject students should have developed the following generic skills:

  • the ability to synthesise information and communicate results effectively
  • the ability to engage with unfamiliar problems, and identify relevant strategies for problem solving through the collection and evaluation of information
  • the capacity for critical and independent thought and reflection
  • the ability to plan and manage time
Notes: This subject is available for science credit to students enrolled in the BSc (new degree).
Related Course(s): Bachelor of Information Systems
Bachelor of Science
Bachelor of Science and Bachelor of Information Systems
Related Majors/Minors/Specialisations: Science Informatics

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