Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor/Lecturer
Nino Kordzakhia
Contact via E-mail
639 L6, 12 Wally's Walk
To be announced on the Unit's iLearn site
Lecturer
Houying Zhu
Contact via E-mail
638 L6, 12 Wally's Walk
To be announced on the Unit's iLearn site
Houying Zhu
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Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
Admission to MRes
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Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit provides students with a comprehensive overview of multivariate data analysis and visualisation. Through hands-on experience with R and other tools, students will learn to manipulate, summarize, and visualise data with multiple variables. They will explore a range of multivariate graphical techniques, such as grouping, faceting, clustering, and time-dependent graphs, and will be introduced to modern methods for hypothesis testing, including MANOVA and multivariate regression. The unit will also cover the creation of interactive dashboards. Students will develop the ability to use statistical graphics to explore data, check statistical model assumptions, and effectively communicate results to diverse audiences. By the end of the unit, students will have a solid understanding of the differences between univariate and multivariate analysis, and will be equipped with valuable skills for working with complex data sets and creating informative dashboards. |
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:
Requirements to pass this unit:
ASSIGNMENT SUBMISSION: Assignment submission will be online through the iLearn page. Submit assignments online via the appropriate assignment link on the iLearn page.
You may submit as often as required before the due date/time. Please note that each submission will completely replace any previous submissions. It is in your interests to make frequent submissions of your partially completed work as insurance against technical or other problems near the submission deadline.
Late Assessment Submission Penalty
From 1 July 2022, Students enrolled in Session based units with written assessments will have the following university standard late penalty applied.
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 apply for Special Consideration.
Assessments where Late Submissions will be accepted
In this unit, late submissions will be accepted as follows:
The University Key Dates
Information about important academic dates including deadlines for withdrawing from units is available at https://www.mq.edu.au/study/calendar-of-dates
Name | Weighting | Hurdle | Due |
---|---|---|---|
Quiz | 10% | No | Week 4 |
Group Project | 30% | No | Week 8 |
Quantitative Data Analysis task | 20% | No | Week 11 |
Final Exam | 40% | No | University Examination Period |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 1 hours
Due: Week 4
Weighting: 10%
Quiz on infographics
Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: Week 8
Weighting: 30%
The group project will involve creating a dashboard on a case study and a media presentation targetting a general audience
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 15 hours
Due: Week 11
Weighting: 20%
Written report
Assessment Type 1: Examination
Indicative Time on Task 2: 2 hours
Due: University Examination Period
Weighting: 40%
An invigilated exam is to be scheduled in the university exam period.
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
Lectures (commencing Week 1): two-hour lecture per week.
SGTA classes (commencing Week 2): one-hour class per week.
- iLearn
All unit-related materials including lecture notes, SGTA's, and instructions for assessment tasks and administrative updates, will be published on iLearn https://ilearn.mq.edu.au/login/
- Software
The statistical software R will be used. This is a free software environment for statistical computing and graphics and can be downloaded from the website https://www.r-project.org/
As GUI you will also need to download RStudio
https://www.rstudio.com/products/rstudio/download/#download
There is no required textbook for this unit.
- Recommended reference sources
Rahlf, T. (2017), Data Visualisation with R. Springer International Publishing AG.
Sievert, C. (2020) Interactive Web-Based Data Visualization with R, plotly, and Shiny, Chapman and Hall/CRC.
Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis. Springer International Publishing.
Wickham, H. and Grolemund, G. (2017) R for Data Science Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, Inc, USA.
Johnson, R. A. and Wichern D. W. Applied Multivariate Statistical Analysis. 6th edn. [electronic copy is available];
Manly, B. and Navarro Alberto J. A. (2016) Multivariate Statistical Methods: A Primer. 4th edn. Chapman and Hall/CRC.
Everitt, B. and Hothorn T. ( 2011) An introduction to applied multivariate analysis with R. Springer.
We will communicate with you via your university email or through announcements on iLearn. Queries to lecturers can be sent through direct email using the University email account.
Students can access the iLearn page by logging on at https://ilearn.mq.edu.au. Students must log in regularly to read the Announcements and access the teaching material.
If there are any changes to this unit concerning COVID-19, these will be communicated to you.
Study Week |
Lecture topics |
1 |
A Brief History of Data Visualisation and Principles of Statistical Graphs |
2 |
Visualisation of Data from Univariate, Bivariate to Multivariate Plots |
3 |
Maps and Time-Dependent Graphs |
4 |
Interactive Graphs |
5 |
Dashboard Creation Using PowerBI |
6 |
Dashboard Creation Using PowerBI cont-ed |
7 |
Introduction to multivariate analysis |
8 |
Multivariate sample statistics; Some useful multivariate distributions |
Mid-Session Break |
|
9 |
Inference: estimation and hypothesis testing |
10 |
MANOVA |
11 |
Multivariate regression |
12 |
Principal component analysis (PCA); Factor analysis (FA) |
13 |
Revision |
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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/
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.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via AskMQ, or contact Service Connect.
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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.
This is the first offering of STAT7126 and we encourage students to provide their constructive feedback via FSE Student Experience & Feedback link on iLearn.
Unit information based on version 2024.02 of the Handbook