Outline

Overview

Impactful data analysis requires more than just technical skills - it demands critical thinking, the ability to navigate complex, real-world problems, and strong communication skills. In this unit you will develop these capabilities by engaging in a range of data-driven problems. You will learn to assess the scope of a project, reformulate it into an analytical framework, and identify and apply appropriate statistical methods in different contexts. The unit emphasises collaboration, with students working in teams to develop solutions to authentic data problems. Clear and effective communication is also a key focus, ensuring that insights are conveyed effectively to diverse audiences. By the end of the unit, you will be equipped to investigate and solve data problems with confidence across multiple contexts.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	30 credit points of 5000-level units
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Content from the core units for the Master of Data Analytics
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Andy Tran, andy.t@sydney.edu.au

Assessment

The census date for this unit availability is 16 October 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Contribution	Workshop Contribution Weekly workshop contributions including analysis plans and peer reviews	10%	Multiple weeks	1 page	AI allowed
Outcomes assessed:
Presentation	P1 EDA Presentation P1 EDA Presentation	5%	Week 02 Due date: 06 Oct 2026 at 18:00	5 minutes	AI allowed
Outcomes assessed:
Data analysis	Project 1 Report Project 1 Report	10%	Week 03 Due date: 18 Oct 2026 at 23:59 Closing date: 28 Oct 2026	1000 words	AI allowed
Outcomes assessed:
Presentation	P1 Final Presentation P1 Final Presentation	10%	Week 03 Due date: 13 Oct 2026 at 18:00	5 minutes	AI allowed
Outcomes assessed:
Presentation	P2 EDA Presentation P2 EDA Presentation	5%	Week 04 Due date: 20 Oct 2026 at 18:00	5 minutes	AI allowed
Outcomes assessed:
Data analysis	Project 2 Report Project 2 Report	10%	Week 05 Due date: 01 Nov 2026 at 23:59 Closing date: 11 Nov 2026	1000 words	AI allowed
Outcomes assessed:
Presentation	P2 Final Presentation P2 Final Presentation	10%	Week 05 Due date: 27 Oct 2026 at 18:00	5 minutes	AI allowed
Outcomes assessed:
Presentation	P3 EDA Presentation P3 EDA Presentation	5%	Week 06 Due date: 03 Nov 2026 at 18:00	5 minutes	AI allowed
Outcomes assessed:
Data analysis	Project 3 Report Project 3 Report	10%	Week 07 Due date: 15 Nov 2026 at 23:59 Closing date: 25 Nov 2026	1000 words	AI allowed
Outcomes assessed:
Written work	Reflection Reflection on the skills and experiences you have gained throughout your Master of Data Analytics.	5%	Week 07 Due date: 15 Nov 2026 at 23:59 Closing date: 25 Nov 2026	500 words	AI allowed
Outcomes assessed:
Oral exam	Final Discussion Discussion about submitted P3 Report and Reflection.	20%	Week 08	20 minutes (oral)	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Final Discussion: This task will include a discussion on the contents of the Project 3 Final Report and Reflection.
Report: This task is an opportunity to report the final findings of your analysis.
Project EDA Presentation: This task is an opportunity for you to present the initial findings of your analysis to your peers. This includes an initial data quality assessment, visualising the key variables in the data and revising the analysis plan if required.
Project Final Presentation: This task is an opportunity to present the final findings of your analysis.
Reflection: This task is an opportunity for you to reflect on the skills and experiences you have gained throughout your Master of Data Analytics. These will be discussed in the Discussion during exam week.
Workshop Contribution: This involves analysis plans and peer reviews that are produced during the workshops.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	The learning outcomes of the unit of study have not been met to a satisfactory 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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

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
Week 01	Project 1 Development	Workshop (1.5 hr)
Week 01	Project 1 Development	Self-directed learning (4 hr)
Week 02	Project 1 Refinement	Workshop (1.5 hr)
Week 02	Project 1 Refinement	Self-directed learning (4 hr)
Week 03	Project 2 Development	Workshop (1.5 hr)
Week 03	Project 2 Development	Self-directed learning (4 hr)
Week 04	Project 2 Refinement	Workshop (1.5 hr)
Week 04	Project 2 Refinement	Self-directed learning (4 hr)
Week 05	Project 3 Development	Workshop (1.5 hr)
Week 05	Project 3 Development	Self-directed learning (4 hr)
Week 06	Project 3 Refinement	Workshop (1.5 hr)
Week 06	Project 3 Refinement	Self-directed learning (4 hr)

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 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

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. Assess the requirements and scope of a data project
LO2. Identify and apply appropriate statistical methods across various domains
LO3. Analyse and visualise data using modern statistical software
LO4. Develop solutions for authentic data problems by collaborating with diverse groups
LO5. Evaluate and communicate outcomes effectively to a range of stakeholders

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.

This is the first time this unit has been offered.

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

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ODAT5888: Data Sleuthing

PG Online Session 2B, 2026 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links