Outline

Overview

The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	A good understanding of statistics, including hypothesis testing and regression modelling, and substantial statistical computing experience. For example, both ODAT5011 and ODAT5021 or a unit like STAT5002.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Wei Zhang, wei.zhang5@sydney.edu.au

Assessment

The census date for this unit availability is 22 August 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final exam Final written exam	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Contribution	Participation Viewing course content, tutorial participation & interacting in discussions	5%	Multiple weeks	Weekly	AI allowed
Outcomes assessed:
Written work	Project: plan and EDA Project plan & Initial Data Analysis (EDA)	5%	Week 04 Due date: 31 Aug 2025 at 23:59	Maximum 3 pages	AI allowed
Outcomes assessed:
Presentation	Project: presentation Presentation of data and key findings from the project.	10%	Week 06 Due date: 09 Sep 2025 at 19:00	10 minutes	AI allowed
Outcomes assessed:
Written work	Project: report Project final report	10%	Week 07 Due date: 19 Sep 2025 at 23:59	Maximum 10 pages	AI allowed
Outcomes assessed:
Portfolio or journal	Weekly homework Weekly homework submission	30%	Weekly	Weekly	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Weekly homework: You will be required to submit your response(s) to the assigned question.
Group project: More details on the project will be provided during semester.
Final Exam: The main and first replacement exam will be an online supervised exam. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit 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:

Late submissions for tutorial exercises are not permitted as solutions will be posted after the due date each week.

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	Statistical Computing, Data Visualisation & Regression	Independent study (4 hr)
Week 01	Statistical Computing, Data Visualisation & Regression	Workshop (1.5 hr)
Week 02	Classification and Cross-Validation	Independent study (4 hr)
Week 02	Classification and Cross-Validation	Workshop (1.5 hr)
Week 03	Bootstrap, Tree Classifiers, and Ensembles	Independent study (4 hr)
Week 03	Bootstrap, Tree Classifiers, and Ensembles	Workshop (1.5 hr)
Week 04	Feature and Model Selection	Independent study (4 hr)
Week 04	Feature and Model Selection	Workshop (1.5 hr)
Week 05	Unsupervised Learning	Independent study (4 hr)
Week 05	Unsupervised Learning	Workshop (1.5 hr)
Week 06	Statistical thinking	Independent study (4 hr)
Week 06	Statistical thinking	Workshop (1.5 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.

Required readings

Primary text:

An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Second edition, 2021, Springer.
- Available online: https://statlearning.com

Additional references:

Computational Statistics (Second Edition), Geof Givens, Jennifer Hoeting, 2013, Wiley.
Applied Predictive Modeling, Max Kuhn, Kjell Johnson, 2013, Springer.
Introductory Statistics with R, Peter Dalgaard, 2008, Springer.
R for Data Science, Wickham & Grolemund, 2017, O'Reilly.
- Available online: https://r4ds.had.co.nz/
Elements of Statistical Learning, Hasties, Tibsharani, Friedman, 2008, Springer (More advanced textbook)
- Available online: https://web.stanford.edu/~hastie/ElemStatLearn/

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. Formulate domain/context specific questions and identify appropriate statistical analysis.
LO2. Formulate, evaluate and interpret appropriate statistical models to describe the relationships between multiple factors.
LO3. Perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy.
LO4. Understand, perform and interpret various unsupervised machine learning methods
LO5. Construct and implement resampling techniques to understand the behaviour of statistical models.
LO6. Create a reproducible report to communicate outcomes using a programming language.

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 have been made since the last offering.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

OSTA5003: Computational Statistical Methods

PG Online Session 2A, 2025 [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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links