Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Hume Winzar
Room 732, 4ER
15:00 to 17:00, Tuesdays
Tutor
Arv Hughes
Online
TBA
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---|---|
Credit points |
Credit points
10
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Prerequisites |
Prerequisites
(STAT270 or STAT2170) and (MGMT220 or BUSA2020)
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This is an advanced applied-skills unit which extends concepts and analytical techniques from earlier units. Students will use data to create graphical representations of data for analysis. Students will clean data in commonly-used spreadsheet formats and make extensive use of proprietary software from big-data orientated companies. Students will develop skills in data visualisation that can be applied to competitive behaviour, target customer analysis, criminology and security intelligence problems. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Assessment criteria for all assessment tasks will be provided on the unit iLearn site.
It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.
Late submissions and extensions
Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for special consideration is made and approved. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Social Network Analysis | 20% | No | Friday Week #3 (12 March) |
Predictive Analytics | 20% | No | Friday Week #6 (2 April) |
Clustering & Segmentation | 20% | No | Friday Week #9 (7 May) |
Group Project | 40% | No | Friday Week #13 (4 June) |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 15 hours
Due: Friday Week #3 (12 March)
Weighting: 20%
Data visualisation and key node identification, with explanatory notes.
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Friday Week #6 (2 April)
Weighting: 20%
Data extraction, visualisation and assessment of alternative software: Use prediction tools from two or more alternative software programs to recommend which program is most useful to a client.
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 15 hours
Due: Friday Week #9 (7 May)
Weighting: 20%
Use appropriate data reduction tools to create a data set with a manageable number of variables, then use appropriate clustering tools to find meaningful groups.
Assessment Type 1: Report
Indicative Time on Task 2: 35 hours
Due: Friday Week #13 (4 June)
Weighting: 40%
Data wrangling and Predictive analysis: Group will work together on an allocated project/case and submit a 1,000 - 1,500 word group report.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Students will learn to use spreadsheet (MS-Excel), Tableau and Gephi. Students will choose to become intermediate-skilled at one of the existing Data Mining/ Analytics packages, such as SPSS Modeler, RapidMiner, Orange, Knime, R statistical package, and others. They will also be exposed to data editing software such as OpenRefine, EasyMorph and Tableau Data Editor.
The web page for this unit can be found at: iLearn http://ilearn.mq.edu.au
This unit is lecture- and tutorial-based. Typically, the class-time structure will be like this:
Lecture notes will be posted after each lecture on iLearn
Time spent on individual topics and exercises may change as we progress during the session, so some topics may vary from this schedule.
Week # |
Topic |
Notes |
1 |
Why is analytics so important to business? (Vidgen, Kirshner & Tan, (VKT) Chapter 1) Define Business needs (VKT Chapter 2) Social Network Analysis (VKT, Chapter 12) |
(23 February) Assignment: Social Network Analysis briefing |
2 |
Social Network Analysis continued (VKT, Chapter 13) |
(2 March) |
3 |
Determine the analytic application/key audience (VKT, Chapter 3) Data visualisation (VKT, Chapter 4) |
(9 March) Assignment: Predictive Analytics briefing Assignment: Social Network Analysis due 23:55 Friday 12 March |
4 |
Build the Analysis data set Predictive models with Regression (VKT, Chapter 6) |
(16 March) |
5 |
Predictive models with Logistic Regression (VKT, Chapter 7) Predictive models with classification & regression trees (VKT, Chapter 8) |
(23 March) Linking business objectives to predictor value. Introducing software: SPSS Modeler, Rapidminer, WEKA, Orange, and others |
6 |
Neural Networks & Automated learning (VKT, Chapter 10) Non-linear models, Neural Networks, and exotica |
(30 March) Assignment: Predictive Analytics due 23:55 Friday 2 April |
7 |
Clustering techniques (VKT, Chapter 5) |
(20 April) Assignment: Clustering & Segmentation briefing |
8 |
Data Reduction Simplifying data for Clustering |
(27 April) |
9 |
More on Clustering techniques
|
(4 May) Assignment: Clustering & Segmentation due 23:55 Friday 7 May |
10 |
Working in Teams Bringing it together - Business Analytics process (VKT, Chapter 14) |
(11 May) Assignment Group Project briefing |
11 |
Combining analytical techniques Idea generation and problem statements (VKT, Chapter 15) |
(18 May) |
12 |
Group project consultation session Ethical issues in data gathering and processing (VKT, Chapter 16) |
(25 May) |
13 |
|
(1 June) Assignment Group Project: due 23:55 Friday 4 June |
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Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
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Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
The Library provides online and face to face support to help you find and use relevant information resources.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
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When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Updated textbook, and and more specific weekly readings for a tighter unit structure.
Some minor changes to assessment criteria, and more details on assessment expectations.
This unit teaches Analytics that can be applied in a global context.
Unit information based on version 2021.02 of the Handbook