Informatics 5: Applied Analytics

Subject INFO30002 (2011)

Note: This is an archived Handbook entry from 2011.

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

This subject is not offered in 2011.
Time Commitment: Contact Hours: 2 x one hour lectures per week, 1 x 1 hour workshop per week
Total Time Commitment: Estimated total time commitment of 120 hours.
Prerequisites:

None

Subject
Study Period Commencement:
Credit Points:
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements: For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry.The University is dedicated to provide support to those with special requirements. Further details on the Disability support scheme can be found at the Disability Liaison Unit Website:http://www.services.unimelb.edu.au/disability

Contact

Associate Professor James Bailey

email:baileyj@unimelb.edu.au

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.
Objectives:

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
Assessment:

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).

Please note: As this subject will not be offered in 2011. Students who have an appropriate background may enrol in COMP90049, Knowledge Technologies.
Related Course(s): Bachelor of Science
Diploma in Informatics
Related Majors/Minors/Specialisations: Science Informatics

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