Students

STAT6102 – Graphics, Multivariate Methods and Data Mining

2022 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor/Lecturer
Nino Kordzakhia
nino.kordzakhia@mq.edu.au
Contact via E-mail
Please refer to iLearn
Credit points Credit points
10
Prerequisites Prerequisites
STAT6170 or STAT670
Corequisites Corequisites
STAT6180 or STAT680
Co-badged status Co-badged status
STAT3102
Unit description Unit description

This unit introduces statistical tools for multivariate data analysis such as statistical graphics, discriminant analysis, principal component analysis, cluster analysis and an introduction to data mining, especially classification. Statistical packages are used extensively to illustrate the concepts.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

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

  • ULO1: Interpret and apply principles underlying statistical data visualisation, multivariate methods and data mining to problems arising from diverse fields of research.
  • ULO2: Choose appropriate graphical techniques for displaying data.
  • ULO3: Choose the appropriate statistical analysis, for a given data set, from a wide range ofmethods based on multivariate methods and data mining.
  • ULO4: Use a statistical computer package to carry out chosen analyses and interpret the results;present the results of analyses in a form which is suitable for technical report or publication.

General Assessment Information

From 1 July 2022, Students enrolled in Session based units with written assessments will have the following late penalty applied. Please see https://students.mq.edu.au/study/assessment-exa ms/assessments for more information. Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.  For any late submission of time-sensitive tasks, such as scheduled tests/ exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special  Consideration.

Late Submissions in this unit:

  • SGTA Works – YES (the late penalty applies)
  • Mid-Semester Test – NO (unless Special Consideration is granted to sit supplementary Mid-Semester Test)
  • Practical Test– NO (unless Special Consideration is granted to sit supplementary Practical Test).

Assessment Tasks

Name Weighting Hurdle Due
SGTA Works 10% No Week 3; 5; 7; 10
Mid-Semester Test 30% No Week 8
Practical Test 60% No Week 12

SGTA Works

Assessment Type 1: Qualitative analysis task
Indicative Time on Task 2: 40 hours
Due: Week 3; 5; 7; 10
Weighting: 10%

 

The tasks given during four SGTA computer lab sessions are to be completed within the allocated time and submitted via iLearn. The four SGTA Works are worth 10% in total.

 
On successful completion you will be able to:
  • Choose appropriate graphical techniques for displaying data.
  • Choose the appropriate statistical analysis, for a given data set, from a wide range ofmethods based on multivariate methods and data mining.
  • Use a statistical computer package to carry out chosen analyses and interpret the results;present the results of analyses in a form which is suitable for technical report or publication.

Mid-Semester Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 1 hours
Due: Week 8
Weighting: 30%

 

Further information will be provided in the iLearn site of the unit.

 
On successful completion you will be able to:
  • Interpret and apply principles underlying statistical data visualisation, multivariate methods and data mining to problems arising from diverse fields of research.
  • Choose appropriate graphical techniques for displaying data.
  • Choose the appropriate statistical analysis, for a given data set, from a wide range ofmethods based on multivariate methods and data mining.

Practical Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 2 hours
Due: Week 12
Weighting: 60%

 

This is an open book style practical exam. The practical test is designed to examine the use of software for data analysis and the software output interpretation skills taught in the unit.

 
On successful completion you will be able to:
  • Interpret and apply principles underlying statistical data visualisation, multivariate methods and data mining to problems arising from diverse fields of research.
  • Choose appropriate graphical techniques for displaying data.
  • Choose the appropriate statistical analysis, for a given data set, from a wide range ofmethods based on multivariate methods and data mining.
  • Use a statistical computer package to carry out chosen analyses and interpret the results;present the results of analyses in a form which is suitable for technical report or publication.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Software: SPSS and R

The recommended references are

Chambers J M et al (1983) Graphical Methods for Data Analysis;

Cleveland W S (1994) Elements of Graphing Data;

Tufte E R (2001) The Visual Display of Quantitative Information;

Everitt B S et al (2001) Applied multivariate data analysis;

Johnson, R.A. & Wichern, D.W. (2002) Applied Multivariate Statistical Analysis;

Manly, B F J (2004) Multivariate Statistical Methods - A  Primer.

 

Unit Schedule

Week

Topic

Due

1

Introduction

  

2

Different graphical displays

  

3

Displaying multivariate data

SGTA Work

4

Similarities and distances

  

5

Hierarchical cluster analysis

SGTA Work

6

K-means clustering

  

7

Eigenvalues and eigenvectors

SGTA Work

8

Principal component analysis

Mid-Semester Test

9

Principal component analysis cont.

  

10

Discriminant analysis

SGTA Work

11

Classification Trees Revision

  

12

Final assessment:

Practical Test

 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

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.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

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IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

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Unit information based on version 2022.02 of the Handbook