Outline

Overview

This OLE gives students a simple, transferable approach to the exploration of multivariate data in everyday life using simple coding in R. You will investigate the relationship between variables in spreadsheet like data, learning what questions to ask, what techniques to use, and what mistakes to avoid. Focused on concepts, not formulae, the OLE is accessible for students from any discipline. You will focus on studies, including: How does the Australian public respond to shark bites? Is mobile phone usage related to higher incidence of brain tumors?

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	2
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Year 10 mathematics or equivalent
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Diana Warren, diana.warren@sydney.edu.au

Assessment

The census date for this unit availability is 3 July 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Out-of-class quiz	Individual: Mastery Quiz 0 #earlyfeedbacktask, Due before Workshop 1	6.25%	Progressive Due date: 29 Jun 2026 at 23:59 Closing date: 06 Jul 2026	Self-paced	AI allowed
Outcomes assessed:
Out-of-class quiz	Individual: Mastery Quizzes 1-3 Designed to be finished before Workshop 1, but due by Workshop 3	18.75%	Progressive Due date: 06 Jul 2026 at 23:59 Closing date: 13 Jul 2026	Self-paced	AI allowed
Outcomes assessed:
Presentation	Group: Project -Presentation Delivered in Workshop 3, slides due the night before Workshop 3	40%	Progressive Due date: 05 Jul 2026 at 23:59 Closing date: 12 Jul 2026	See marking rubric	AI allowed
Outcomes assessed:
Written work	Group: Project – Report Due before Workshop 3	35%	Progressive Due date: 05 Jul 2026 at 23:59 Closing date: 12 Jul 2026	See marking rubric	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Mastery Quizzes

All MasteryQuizzes are found in Canvas. Mastery Quiz 0 is designated the 'Early Feedback Task' (EFT).
The Mastery Quizzes are eligible for special consideration, up to maximum of 7 days, given the intensive nature of the unit delivery.

Attendance

While attendance at Workshops is expected, you do not need to submit special consideration if you miss a Workshop.

Projects

The Group: Project is submitted through the Canvas site. Late penalities apply. It is your responsibility to check that your project files have been submitted correctly, otherwise your work will not be marked.
The Group: Project has 2 components. Both the Report and Presentation are not generally eligible for special consideration as they are group work, and the project is presented in Workshop 3.
In rare situations, if a special consideration is deemed necessary, then a maximum of 7 days extension can be given.

None of the tasks are hurdle tasks - an overall final mark of 50 is sufficient to pass the unit.

Detailed information for each assessment task can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Progressive	Introduction to data and R	Self-directed learning (2 hr)
	Data detective	Self-directed learning (1.5 hr)
	Data exploration	Self-directed learning (4.5 hr)
	Data interpretation	Self-directed learning (2 hr)
	3x Workshops	Practical (6 hr)

Attendance and class requirements

Students must attend the Workshops on Campus.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Required readings

No extra reading is required, just what is provided in the Canvas site.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. perform data analysis on multivariate data in a team, and communicate the findings through reproducible statistical reports and oral presentations
LO2. perform data analysis on multivariate data
LO3. perform initial data analysis on multivariate data
LO4. choose appropriate statistical tools for multivariate data
LO5. recognise common mistakes in interpretation.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes needed.

Additional information

Science student portal

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

OLET1632: Shark Bites and Other Data Stories

Intensive June - July, 2026 [Block mode] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links