Outline

Overview

In the era of big data, the increasingly vast amount of collected data enables many underlying complex structures among observations and variables to be discovered. Examples of such complex structures include sparsity, network interdependence, and heterogeneity. Understanding these structures provides valuable insights into both scientific phenomena and social behaviours. This unit will introduce mathematical and statistical techniques to model these complex structures. A particular focus will be developing the computational skills to analyse huge datasets, interpret and communicate the results of these analyses.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Strong statistical knowledge and coding experience. E.g. ODAT5011, ODAT5021, and OSTA5003.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Linh Nghiem, linh.nghiem@sydney.edu.au

Assessment

The census date for this unit availability is 21 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Data analysis	Assignment 1 Directed questions on given datasets and/or understanding literature	10%	Week 03 Due date: 17 Aug 2026 at 23:59 Closing date: 21 Aug 2026	~5-6 pages	AI allowed
Outcomes assessed:
Data analysis	Assignment 2 Directed questions on the given dataset and/or understanding literature	10%	Week 05 Due date: 31 Aug 2026 at 21:59 Closing date: 04 Sep 2026	~5-6 pages	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Group project presentation Oral presentation and Q&A	30%	Week 07 Due date: 17 Sep 2026 at 19:30 Closing date: 18 Sep 2026	15-20 minutes	AI prohibited
Outcomes assessed:
Written work	Group project report Written report	25%	Week 08 Due date: 24 Sep 2026 at 23:59 Closing date: 27 Sep 2026	5-7 pages	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly online quizzes Weekly online quizzes	10%	Weekly	5 questions per quiz	AI allowed
Outcomes assessed:
Contribution	Contribution Contribution	15%	Weekly	1.5 hours per week	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Contribution:听you will attend and engage in the workshop and then submit your attempt at the workshop questions in Canvas afterwards. If you can't join a workshop in a given week you have until Sunday 11:59pm following the workshop to submit your attempt at the workshop questions through Canvas.
听
Online quizzes:听each week there will be a Canvas quiz due by Sunday 11:59pm.
听
Assignments:听the two assignments are designed to test your understanding of specific topics covered in the unit and provide feedback on your progress. The questions are more specific and directed than the project.
听
Project:听the project is听open-ended data exploration based on your domain interest. There are two components to the project. For the听presentation听component you will present to staff and other students and get feedback on your approach. For the听report听component you will write up a final report using the same data set as the presentation.听

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Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the听learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

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听Academic Integrity Policy.

Visit the听Current Students website听for more information on AI in assessments, including听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听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听Academic Honesty Education Module (AHEM)听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听Current Students website听for more information on AI in assessments, including听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听simple extension.鈥� 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鈥檛 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听special consideration or special arrangements.

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

Using AI responsibly

Co-created with students,听听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听reflects the University鈥檚 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	Review of linear model and logistic regression model; variable selection through lasso	Lecture (6 hr)
Week 02	Clustered data settings: linear and logistic mixed effects models	Lecture (6 hr)
Week 03	Review of matrix and linear algebra; review of principal component analysis (PCA) and sparse PCA; canonical correlation analysis (CCA) and sparse CCA	Lecture (6 hr)
Week 04	Multivariate reduced rank regression models	Lecture (6 hr)
Week 05	Introduction to network analysis; random network and exponential family random graph models	Lecture (6 hr)
Week 06	Introduction to graphical models: Gaussian, Ising and mixed graphical models	Lecture (6 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. Explain mathematical and statistical concepts used to describe complex structures in the data
LO2. Apply and implement appropriate models to extract complex structures in datasets
LO3. Evaluate the applicability and efficiency of complex models in a variety of contexts
LO4. Justify and communicate results and insights from statistical models to interdisciplinary audiences

Graduate qualities

The graduate qualities are the qualities and skills that all 爆料王 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鈥檚 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 the unit is offered.

Disclaimer

Important: the 爆料王 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 .

爆料王

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

爆料王

ODAT5031: Modelling Complex Data Structures

PG Online Session 2A, 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