Big Data and Cloud Computing unit (KXO318)

Introduction

In recent years, the rise of internet technologies and digital tools in daily life has led to a huge increase in data, known as Big Data. Traditional methods cannot manage this data, so new high-performance and distributed systems like clusters, clouds, MapReduce, and stream computing have been created. The aim of this unit is to give students basic knowledge and understanding of Big Data and distributed computing systems and applications, especially in the context of the Cloud. In other words, the unit will prepare students with the skills needed to build new applications that are scalable, efficient, and able to process Big Data. The key topics are data preparation and exploratory analysis, basics of parallel and distributed systems, Big Data platforms and programming, and basics of cloud computing. The unit will also explain how common cloud infrastructure is changing with these technologies and what future trends may look like.

Summary

Unit name	Big Data and Cloud Computing
Unit code	KXO318
Credit points	12.5
College/School	Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Doctor Matthew Springer
Available as an elective?	Yes
Delivered By	University of Tasmania
Level	Advanced

Learning Outcomes

• Analyse the problems and challenges associated with various datasets, and prepare to apply suitable methods and tools for data processing, analysis, and interpretation.
• Adapt emerging Big Data and cloud technologies to support the building of solutions and applications.
• Design high-performance and cloud applications to support scalable services.

Fee Information

Field of Education	Commencing Student Contribution 1,3	Grandfathered Student Contribution 1,3	Approved Pathway Course Student Contribution 2,3	Domestic Full Fee 4
			not applicable

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.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Requisites

Prerequisites

KXO107

Teaching

Teaching Pattern	On-Campus enrolments in Shanghai:  Lectures: 2 hrs/week Tutorials: 2 hrs/week  (from week 1) Self-study: 2 hrs/week (on average) For this unit, students are expected to attend lectures and tutorial/lab classes, and to keep up to date with MyLO content.
Assessment	Lab Test 1 (20%)|Lab Test 2 (25%)|Tutorial tasks (25%)|Cloud and Big Data Based Processing System (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.