Skip to main content
Unit of study_

STAT5003: Computational Statistical Methods

Semester 1, 2021 [Normal evening] - Camperdown/Darlington, Sydney

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 STAT5003
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
Final exam (Record+) Type B final exam Final exam
Final written exam
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Group project
Multi-stage project
25% Multiple weeks Reports and presentation
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Tutorial quiz Computer quiz
Online computer quiz
20% Week 07
Due date: 21 Apr 2021 at 18:00
2 hours
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Small continuous assessment Tutorial exercises
Weekly homework submission
15% Weekly Weekly
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Type B final exam = Type B final exam ?

Assessment summary

  • Tutorial exercises: You will be required to submit your tutorial solution within six days of each tutorial. However, only selected questions will be marked and contribute to your total marks for this course.
  • Computer quiz: Will be held in class in Week 7. You will be given a dataset to analyse and answer brief questions. You are required to submit your answers to Canvas at the end of the test.
  • Group project: More details on the project will be provided during semester.

Detailed information for each assessment 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.

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 sydney.edu.au/students/guide-to-grades.

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 are for tutorial exercises are not permitted as solutions will be posted seven days after the tutorial. The computer quiz is completed during class time and therefore cannot be submitted late.

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.

WK Topic Learning activity Learning outcomes
Week 01 Basics of statistical computing and visualisation Lecture and tutorial (3 hr) LO1 LO6
Week 02 Regression and smoothing Lecture and tutorial (3 hr) LO1 LO2 LO6
Week 03 Density estimation Lecture and tutorial (3 hr) LO1 LO2 LO6
Week 04 High-dimensional visualisation and analytics in R Lecture and tutorial (3 hr) LO1 LO4 LO6
Week 05 Classification with R Lecture and tutorial (3 hr) LO1 LO4 LO6
Week 06 Cross-validation and bootstrapping Lecture and tutorial (3 hr) LO1 LO2 LO3 LO5 LO6
Week 07 Treatment of missing values Lecture and tutorial (3 hr) LO1 LO2 LO5 LO6
Week 08 Feature and model selection Lecture and tutorial (3 hr) LO1 LO2 LO3 LO5 LO6
Week 09 Tree classifiers and ensembles Lecture and tutorial (3 hr) LO1 LO4 LO5 LO6
Week 10 Monte Carlo methods 1 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO5 LO6
Week 11 Monte Carlo methods 2 Lecture and tutorial (3 hr) LO1 LO2 LO3 LO5 LO6
Week 12 Final presentation Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Revision Block teaching (3 hr) LO1 LO2 LO3 LO4 LO5

Attendance and class requirements

Students are expected to attend the weekly tutorial labs and participate in the group assignment. 

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:

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.
  • 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.

Content has been adjusted to reduce overlap with other units in their degree program.

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.