Requirements to pass this unit

• Achieve a total mark equal to or greater than 50%

ASSIGNMENT SUBMISSION: Assignment submission will be online through the iLearn page. Submit assignments online via the appropriate assignment link on the iLearn page.

• Assignment submission is via iLearn. You should upload this as a single scanned PDF file.
• It is your responsibility to make sure your assignment submission is legible.

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.

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through https://connect.mq.edu.au.

Assessments where Late Submissions will be accepted

In this unit, late submissions will be accepted as follows:

• Group Project - YES, Standard Late Penalty applies
• Quantitative Data Analysis task - YES, Standard Late Penalty applies
• Final Examination - NO, unless Special Consideration is Granted

Participation

• We strongly encourage all students to actively participate in all learning activities.  Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material and receive valuable feedback from instructors, to assist in completing the unit assessments.

• Your active participation is not only beneficial to your learning but is also critical to the successful completion of the unit.

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

Classes

Lectures (commencing Week 1):  two-hour lecture per week. 

SGTA classes (commencing Week 2): one-hour class per week.

• The unit timetable can be found at: https://publish.mq.edu.au/
• To manage your enrollment, follow the link: https://students.mq.edu.au/support/technology/systems/estudent

Technologies used and required

- 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: R; Mathematica

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

Mathematica will be employed to enhance understanding of statistical principles important in data analysis, through visualisation and interactive examples that illustrate the underlying analytical concepts.

Mathematica can be downloaded from https://www.wolfram.com/siteinfo/

Texts and materials

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.

Methods of Communication 

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.

COVID Information 

 If there are any changes to this unit concerning COVID-19, these will be communicated to you.

To enable students more time to focus on learning, understanding, and reflecting on the content of the unit, we have revised the assessment structure as follows. There are now only three assessments: a group project, an assignment on a quantitative data analysis task, and a final exam. Although no marks are associated with attendance, all unit activities provide the content designed to support your success in the assessments and the unit overall.