In today's world, the prevalent use of technology and automation have resulted in an explosion in the quantity of data, often referred to as "big data", accumulated by business and by researchers. Data is seen as a critical asset for decision-making. Raw data, however, is of little value. In order to obtain insights from this big data analytical techniques are required to turn the data in the repositories into knowledge, by extracting information and identifying patterns, upon which actions can be taken. This unit will help students appreciate the value of using data mining techniques and information visualisation methods for the analysis of big data. Students will gain an understanding of various methods and techniques and applications for data mining. Students will also investigate information visualisation tools and techniques to represent the big data in forms that more readily convey embedded information. Students will gain an understanding of the major research issues in the area of big data.
|Unit name||Big Data Analytics|
|College/School||College of Sciences and Engineering
School of Information and Communication Technology
|Discipline||Information & Communication Technology|
|Coordinator||Doctor Saurabh Garg|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
|ECA Melbourne||Semester 2||On-Campus||International|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
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- Explain and apply tools, techniques and research skills for analysing data
- Create and evaluate ICT components to support decision making based on user requirements
- Communicate and collaborate with stakeholders during the data analysis and decision-making process.
|Field of Education||Commencing Student Contribution 1,3||Grandfathered Student Contribution 1,3||Approved Pathway Course Student Contribution 2,3||Domestic Full Fee 4|
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
PrerequisitesKIT502 or KIT506
Lecture: 2 hours/week
Tutorial: 2 hours/ week
Self-Study: upto 4 hours/weeks
|Assessment||Test 1 (20%)|Test 2 (25%)|Tutorial Task (25%)|Assignment 1 (30%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
|Links||Booktopia textbook finder|
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