Hobart
This unit has been discontinued.
Introduction
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.
Summary 2020
Unit name | Data Analytics |
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Unit code | KIT606 |
Credit points | 12.5 |
Faculty/School | College of Sciences and Engineering School of Information and Communication Technology |
Discipline | Information & Communication Technology |
Coordinator | Quan Bai |
Teaching staff | |
Level | Postgraduate |
Available as student elective? | Yes |
Breadth Unit? | No |
Availability
Note
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* 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).
Fees
Requisites
Prerequisites
Co-requisites
Mutual Exclusions
You cannot enrol in this unit as well as the following:
KIT306
Teaching
Teaching Pattern | 2hr Lectures, 2hr online modules for self study, 2hr laboratory classes. |
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Assessment | 40% 2-hr exam, 60% in-semester (4 assignments worth 25%, 2 x 15%, 5%) |
Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
Required | |
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Recommended |
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