Unit Outline

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.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


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

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KIT107

Alterations as a result of student feedback

Nil.

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Lecture (Online)	A real-time (i.e. synchronous) interactive activity involving the whole class whose primary purpose is the presentation and structuring of information/ideas/skills to facilitate student learning. All students are expected to attend.	2	Weekly
	Tutorial	A structured real-time (i.e. synchronous) activity in a small-group setting where the primary purpose is the clarification, exploration or reinforcement of subject content presented or accessed at another time or place (e.g. lecture, preparatory work). It is reliant on student-teacher and student-student interaction and dialogue for achievement of its learning outcomes. The students enrolled in the tutorial are expected to attend.	2	Weekly
	Independent Learning	Online modules Involving reading, listening to audio, watching video, and/or completing exercises and/or quizzes, self-study is individual work undertaken when the student chooses (i.e. asynchronous), most likely through engagement with MyLO. The content is examinable, and may need to be completed prior to attending classes and/or attempting assessment tasks.	2	Weekly

Attendance / engagement expectations

If your unit is offered On campus, it is expected that you will attend all on-campus and onsite learning activities. This is to support your own learning and the development of a learning community within the unit. If you are unable to attend regularly, please discuss the situation with your course coordinator and/or our UConnect support team.

If your unit is offered Online or includes online activities, it is expected you will engage in all those activities as indicated in the Unit Outline or MyLO, including any self-directed learning.

If you miss a learning activity for a legitimate reason (e.g., illness, carer responsibilities) teaching staff will attempt to provide alternative activities (e.g., make up readings) where it is possible.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Lab Test 1	Week 6	20 %	LO1, LO2
Assessment Task 2:	Lab Test 2	Week 11	25 %	LO2, LO3
Assessment Task 3:	Cloud and Big Data Based Processing System	Week 13	30 %	LO1, LO2, LO3
Assessment Task 4:	Tutorial tasks	Refer to Assessment Description	25 %	LO1, LO2, LO3

Assessment details

Assessment Task 1: Lab Test 1

Task Description:

This test will check students’ understanding of basic data preparation, data analytics, and distributed computing applications, in both theory and practice.

Task Length:

75 mins

Due Date:

Week 6

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Ability to analyse and explore various data sets using suitable methods and tools.	LO1
2	Analyse the requirements of big data and cloud computing technologies in context of various applications	LO2
3	Ability to understand the computing performance of a given distributed computing application.	LO2

Assessment Task 2: Lab Test 2

Task Description:

This test will check students’ ability to solve and analyse distributed computing and cloud computing applications, in both theory and practice.

Task Length:

75 mins

Due Date:

Week 11

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Ability to design big data and cloud applications	LO2
2	Analyse the requirements of big data and cloud computing technologies in the context of business applications	LO2
3	Ability to analyse the performance issues with a given application and suggest an appropriate solution.	LO3

Assessment Task 3: Cloud and Big Data Based Processing System

Task Description:

Students need to utilise a Big Data platform to solve specific problems. A detailed description will be posted on Mylo. Students need to submit their code and also a video showing a demonstration of the entire program.

Task Length:

Refer to Assessment Description

Due Date:

Week 13

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Evaluate and analyse the performance issues of distributed computing application	LO1, LO2
2	Implement big data and cloud computing application	LO3
3	Develop solutions to tackle performance issue in distributed computing application	LO1, LO2, LO3

Assessment Task 4: Tutorial tasks

Task Description:

Each tutorial will be assessed based on the completion of the tutorial task, as well as the lecture and tutorial concepts taught. This tutorial task may involve solving a short problem and/or completing tutorial work.

Task Length:

Refer to Assessment Description

Due Date:

Refer to Assessment Description

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply Big Data and Cloud Technologies	LO2
2	Implement simple big data and cloud applications based on business scenario	LO3
3	Configure and deploy real big data and cloud technologies according to various scenarios	LO1, LO2
4	Ability to analyse and explore various data sets using suitable methods and tools.	LO1

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Academic progress review

The results for this unit may be included in a review of your academic progress. For information about progress reviews and what they mean for all students, see Academic Progress Review in the Student Portal.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Academic integrity

Academic integrity is about acting responsibly, honestly, ethically, and collegially when using, producing, and communicating information with other students and staff members.

In written work, you must correctly reference the work of others to maintain academic integrity. To find out the referencing style for this unit, see the assessment information in the MyLO site, or contact your teaching staff. For more detail about Academic Integrity, see Important Guidelines & Support.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.