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Unit of study_

OSTA5003: Computational Statistical Methods

Semester 1b, 2024 [Online] - Online Program

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

Unit code OSTA5003
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
None
Assumed knowledge
? 

STAT5002 or equivalent introductory statistics course with a statistical computing component

Available to study abroad and exchange students

No

Teaching staff

Coordinator Justin Wishart, justin.wishart@sydney.edu.au
Lecturer(s) Justin Wishart, justin.wishart@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Final written exam
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Participation Participation
Viewing course content, tutorial participation & interacting in discussions
5% Multiple weeks Weekly
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Project: plan and EDA
Project plan & Initial Data Analysis (EDA)
5% Week 04
Due date: 10 May 2024 at 23:59
Maximum 3 pages
Outcomes assessed: LO1 LO2 LO6
Presentation group assignment Project: presentation
Presentation of data and key findings from the project.
10% Week 06
Due date: 21 May 2024 at 19:00
10 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment group assignment Project: report
Project final report
10% Week 07
Due date: 31 May 2024 at 23:59
Maximum 10 pages
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Small continuous assessment Weekly homework
Weekly homework submission
30% Weekly Weekly
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
group assignment = 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.

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 Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 Statistical Computing, Data Visualisation & Regression Online class (2 hr) LO1 LO2 LO6
Statistical Computing, Data Visualisation & Regression Computer laboratory (1.5 hr) LO1 LO2 LO6
Week 02 Classification and Cross-Validation Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Classification and Cross-Validation Computer laboratory (1.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Bootstrap, Tree Classifiers, and Ensembles Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Bootstrap, Tree Classifiers, and Ensembles Computer laboratory (1.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 04 Feature and Model Selection Online class (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Feature and Model Selection Computer laboratory (1.5 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 05 Unsupervised Learning Online class (2 hr) LO1 LO2 LO4 LO6
Unsupervised Learning Computer laboratory (1.5 hr) LO1 LO2 LO4 LO6
Week 06 Statistical thinking Online class (2 hr) LO1 LO2 LO4 LO6
Statistical thinking Computer laboratory (1.5 hr) LO1 LO2 LO4 LO6

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.

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. 
  • Elements of Statistical Learning, Hasties, Tibsharani, Friedman, 2008, Springer (More advanced textbook) 

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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

This is the first time this unit has been offered in S2B, it was first offered in S1B. No changes to the S1B at this stage.

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.