Courses & Units

Big Data and Cloud Computing KIT318

Introduction

In recent years, due to advancement of internet technologies and instrumentation of every part of our life, we have noticed a huge surge in data available to us. This revolution is termed as Big Data. This Big Data cannot be processed or managed by any traditional methods of processing. This has led to development of several high performance and distributed computing platforms and programming frameworks. The design of such platforms relies on distributed computing concepts which are implemented in the form of systems such as Clusters and Clouds, and Big Data frameworks such as MapReduce and Stream Computing. These systems play an important role in todays' research, academia or industries by providing the processing of data generated from a variety of networked resources, e.g. large data stores and information repositories, expensive instruments, social media, sensors networks, and multimedia services for a wide range of applications. The aim of this unit is to provide students with the foundation knowledge and understanding of Big Data and distributed computing systems and applications especially in context of Cloud. In other words, this unit will equip students with essential knowledge that is needed for building next-generation applications that are scalable and efficient and can process Big Data. Key topics that will be covered: parallel and distributed systems basics, Big Data platforms and programming and Cloud computing. The unit will also explain how the business models of enterprises are changing with these forms of computing that provide large storage and computation space without purchasing expensive computer systems.

Summary

Unit name	Big Data and Cloud Computing
Unit code	KIT318
Credit points	12.5
College/School	College of Sciences and Engineering School of Information and Communication Technology
Discipline	Information & Communication Technology
Coordinator	Doctor Saurabh Garg
Available as an elective?	Yes
Delivered By	University of Tasmania
Level	Advanced

Availability

Location	Study period	Attendance options		Available to
Hobart	Semester 1	On-Campus		International	Domestic
Launceston	Semester 1	On-Campus		International	Domestic

Key

: On-campus
: Off-Campus
: International students
: Domestic students

Note

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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 1	26/2/2024	22/3/2024	15/4/2024	2/6/2024

* 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 (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2024 are indicative and subject to change. Finalised census dates for 2024 will be available from the 1st October 2023. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

Analyse the problems and challenges associated with building distributed computing applications;
Adapt the emerging big data and cloud technologies to support business applications.
Design high performance and cloud applications to support scalable online services.
Design big data processing applications to efficiently process high volume and velocity data

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
029999	$1,118.00	$1,118.00	not applicable	$2,648.00

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 UConnect 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

KIT107

Teaching

Teaching Pattern	Lectures: 2 hr/wk Tutorials: 3 hr/wk - For this unit, students are expected to attend on-campus tutorials; an online tutorial will be available for students with special circumstances (permission is required to attend the online tutorial) For this unit, students are expected to attend lectures and labs classes and are expected to remain up-to-date with MyLo content.
Assessment	Big Data Assignment (15%)\|Cloud and Big Data Based Processing System (20%)\|Tutorial work (30%)\|Test (35%)
Timetable	View the lecture timetable \| View the full unit timetable

Teaching Pattern

Lectures: 2 hr/wk
Tutorials: 3 hr/wk - For this unit, students are expected to attend on-campus tutorials; an online tutorial will be available for students with special circumstances (permission is required to attend the online tutorial)

For this unit, students are expected to attend lectures and labs classes and are expected to remain up-to-date with MyLo content.

Assessment

Big Data Assignment (15%)|Cloud and Big Data Based Processing System (20%)|Tutorial work (30%)|Test (35%)

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.
Links	Booktopia textbook finder

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