Big Data Analytics unit (KIT718)

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 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.

Summary

Unit name	Big Data Analytics
Unit code	KIT718
Credit points	12.5
College/School	Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Doctor Wenli Yang
Delivered By	University of Tasmania and Third Party(ies): ECA
Level	Postgraduate

Availability

Specific information on 2027 unit availability will be available in August

Learning Outcomes

• 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.

Fee Information

2027 fee information will be available in August.

Requisites

Prerequisites

KIT500 or KIT502 or KIT506 or KIT509

Teaching

Teaching Pattern	On-Campus enrolments in Hobart will follow the teaching arrangement: Lecture: 2 hours/week Tutorials: 2 hours/ week  Self-Study [Online Module]: up to 4 hours/week On-Campus Melbourne and Sydney teaching arrangements may differ and will be advised by your teaching team. For this unit, students are expected to attend lectures and on-campus classes and are expected to remain up-to-date with content that is delivered asynchronously online.
Assessment	Test 1 (20%)|Test 2 (25%)|Tutorial Task (25%)|Assignment 1 (30%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	Required readings will be listed in the unit outline prior to the start of classes.

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.