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 warehouses have been used to set up repositories for this big data. 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 business intelligence tools, data mining techniques and information visualisation methods for the analysis of big data. In this unit students will explore the concepts and technology of business intelligence and experience designing and building business intelligence systems. Students will also gain an understanding of various methods and techniques and applications for data mining. Students will also investigate information visualization 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||Data Analytics|
|Faculty/School||College of Sciences and Engineering
School of Technology, Environments and Design
|Discipline||Information & Communication Technology|
Dr Saurabh Garg
|Available as student elective?||Yes|
|Location||Study period||Attendance options||Available to|
|Hobart||Spring school (late)||On-Campus||International||Domestic|
- International students
- Domestic students
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|Band||CSP Student Contribution||Full Fee Paying (domestic)||Field of Education|
|2||2019: $1,169.00||2019: $2,321.00||029999|
Fees for next year will be published in October. The fees above only apply for the year shown.
Please note: international students should refer to this page to get an indicative course cost.
KIT102 and (KIT206 or KIT202 or KIT205 or KXT201 or KXT209)
You cannot enrol in this unit as well as the following:
2hr lectures, 2hr online modules for self study, 2hr laboratory classes
60% exam, 40% in-semester (2 assignments worth 15%, 25%)
|Timetable||View the lecture timetable | View the full unit timetable|
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