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||Faculty of Science, Engineering & Technology
School of Engineering & ICT
|Discipline||Computing and Information Systems|
|Available as student elective?||Yes|
This unit is currently unavailable.
Units are offered as On-campus where the majority of teaching will occur at the campus identified. Units offered Off-campus generally have no requirement for attendance at a physical university campus unless the unit has practical or fieldwork components*: the campus indicated for an Off-Campus unit is the one at which teaching is administered from.
*Please read the Unit Introduction in the Course and Unit Handbook for attendance requirements for units offered in Off-campus mode.
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).
Unit census dates currently displaying for 2017 are indicative and subject to change. Finalised census dates for 2017 will be available from the 1st October 2016.
|Band||CSP Student Contribution||Full Fee Paying (domestic)||Field of Education|
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 (3 assignments worth 10%, 10%, 20%)
|Timetable||View the lecture timetable | View the full unit timetable|
|Required||To be advised by Unit Coordinator|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.