Unit Outline

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.

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	Explain and apply tools, techniques and research skills for analysing data
2	Create and evaluate ICT components to support decision making based on user requirements
3	Communicate and collaborate with stakeholders during the data analysis and decision-making process.

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KIT500 or KIT502 or KIT506 or KIT509

Alterations as a result of student feedback

Nil

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Lecture (On Campus)	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
	Computer Laboratory	A structured real-time (i.e. synchronous) computer-based 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 teacher supported and may involve student-teacher and/or student-student interaction and dialogue for achievement of its learning outcomes. The students enrolled in the class are expected to attend.	2	Weekly
	Independent Learning	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.	4	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:	Test 1	Week 6	20 %	LO1, LO2
Assessment Task 2:	Test 2	Week 13	25 %	LO1, LO2
Assessment Task 3:	Assignment 1	Week 14	30 %	LO1, LO2, LO3
Assessment Task 4:	Tutorial Task	Refer to Assessment Description	25 %	LO1, LO2, LO3

Assessment details

Assessment Task 1: Test 1

Task Description:

In tutorial test to assess a student’s knowledge of data handling techniques and how to analyse user needs and use them to create and evaluate appropriate ICT components to justify decision making.
This test in Week 6 during tutorial

Task Length:

1 hour

Due Date:

Week 6

Weight:

20 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain data handling techniques for cleaning, sampling, modelling, and mining data	LO1
2	Select and apply techniques for cleaning, sampling, modelling, and mining data	LO1, LO2

Assessment Task 2: Test 2

Task Description:

Tutorial test to assess a student’s knowledge of big data handling techniques and their evaluation to analyse to enable decision making.
This test in Week 13 is conducted within tutorial time

Task Length:

1 hours

Due Date:

Week 13

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain and dissect data handling techniques for cleaning, sampling, modelling, mining and analysing data	LO1
2	Apply techniques for cleaning, sampling, modelling, mining and analysing data	LO1, LO2
3	Analyse user needs and create and evaluate ICT components to justify decision making	LO2

Assessment Task 3: Assignment 1

Task Description:

Data analytics project

This assessment consists of analysing real data and implementing the system for handling a large amount of data. Students need to write a research report like a document based on their analysis, and also submit the Jupyter notebook that can automatically analyse the data.

Task Length:

about 4000 words for a research report and also an associated Jupyter notebook.

Due Date:

Week 14

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Write a research paper that addresses the research questions using the results of the data analysis	LO1, LO2, LO3
2	Apply tools and techniques for cleaning, sampling, modelling, mining and analysing data	LO1
3	Analyse user needs and create and evaluate ICT components to analyse large data sets	LO2, LO3

Assessment Task 4: Tutorial Task

Task Description:

Weekly tutorials will allow students to develop skills using data handling tools and techniques. Each tutorial will include a short assessment task or a quiz to assess a student’s knowledge of data handling techniques and how to use them to support decision making. Tutorial work will be marked during the tutorial.

Task Length:

2hr tutorial

Due Date:

Refer to Assessment Description

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply tools and techniques for cleaning, sampling, modelling, mining and analysing data	LO1
2	Analyse user needs and create and evaluate ICT components to support decision making	LO2, LO3

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.