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 warehouses have been used to set up repositories for this big data. 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 business intelligence tools, data mining techniques and information visualisation methods for the analysis of big data. In this unit students will explore the concepts and technology of business intelligence and experience designing and building business intelligence systems. Students will also gain an understanding of various methods and techniques and applications for data mining. Students will also investigate information visualization 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 the characteristics of a data set and the objectives of analysing the data set
2.	Apply methodologies and techniques to clean, sample, model, mine and analyse a data set
3.	Evaluate and improve the performance of different machine learning algorithms
4.	Answer a research question after justifying the methodologies and techniques chosen in the process of analysing a data set

Requisites


REQUISITE TYPE	REQUISITES
Pre-requisite	KIT205 OR KIT206 OR KIT214

Alterations as a result of student feedback

N/A

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Workshop	A structured real-time (i.e. synchronous) activity that involves a mix of presentation of new information/ideas/skills and guided activities related to that information/ideas/skills. 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	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.	1	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, it is expected you will engage in all those activities as indicated in the Unit Outline, 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:	Project Phase 1	Week 7	25 %	LO1, LO2, LO4
Assessment Task 2:	Project Phase 2	Week 13	35 %	LO2, LO3, LO4
Assessment Task 3:	MyLO Quiz	Refer to Assessment Description	10 %	LO1, LO2
Assessment Task 4:	Tutorial Tasks	Refer to Assessment Description	30 %	LO1, LO2, LO3

Assessment details

Assessment Task 1: Project Phase 1

Task Description:

Each student will analyse a data set. Each student will produce an exploratory report that describes the data set and details the objectives of the analysis (identify research questions). Each student will be required to correctly prepare the data set by cleaning it and handling missing data. Each student will then use basic statistics to explore and analyse data patterns. Each student will present the results in a report using some appropriate visualisation techniques.

Task Length:

10 pages

Due Date:

Week 7

Weight:

25 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain the data set characteristics and identify the objectives of the analysis	LO1
2	Justify and detail the methodology used to prepare and clean the data and which features are selected to answer questions	LO2, LO4
3	Justify the selected statistical analysis techniques and report the results using visualisation and referencing	LO2, LO4

Assessment Task 2: Project Phase 2

Task Description:

Each student will further analyse the chosen data set. Based on their phase 1 report students will now apply machine learning algorithms to build a data model and evaluate the performance and analyse the answers to the research questions. The report should include referencing, visualisation and justification of methods chosen and answers to the research questions.

Task Length:

10 pages

Due Date:

Week 13

Weight:

35 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Justify and detail the selected machine learning techniques with referencing	LO2, LO4
2	Evaluate the performance of different algorithms and improve accuracy	LO3
3	Justify the conclusions to the identified research questions with visualisations	LO4

Assessment Task 3: MyLO Quiz

Task Description:

This assessment demonstrates understanding of the lecture material. Students must correctly demonstrate understanding of the material through quiz questions directly aligned with the lecture material.

Task Length:

2 quizzes in week 4 and week 9 (20 minutes for each )

Due Date:

Refer to Assessment Description

Weight:

10 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Correctly answer questions relating to a range of data analysis methodologies and techniques	LO1, LO2

Assessment Task 4: Tutorial Tasks

Task Description:

This continuous assessment demonstrates completion and understanding of the tutorial tasks from weeks 2-11. Students must correctly demonstrate application of data analysis methodologies and techniques to answer specified questions throughout a tutorial. Each tutorial is worth 3%.

Task Length:

Due Date:

Refer to Assessment Description

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Apply data analysis methodologies and techniques to correctly answer specified questions	LO1, 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.

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.

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.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.