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Unit of study_

DATA5710: Applied Statistics for Complex Data

Intensive March, 2021 [Block mode] - Remote

With explosions in availability of computing power and facilities for gathering data in recent times, a key skill of any graduate is the ability to work with increasingly complex datasets. There may include, for example, data sets with multiple levels of observations gathered from diverse sources using a variety of methods. Being able to apply computational skills to implement appropriate software, as well as bringing to bear statistical expertise in the design of the accompanying algorithms are both vital when facing the challenge of analysing complicated data. This unit is made up of three distinct modules, each focusing on a different aspect of applications of statistical methods to complex data. These include (but are not restricted to) the development of a data product that interrogate large and complicated data structures; using sophisticated statistical methods to improve computational efficiency for large data sets or computationally intensive statistical methods; and the analysis of categorical ordinal data. Across all modules you will develop expertise in areas of statistical methodology, statistical analysis as well as computational statistics. Additional modules may be delivered, depending on the areas of expertise of available staff and distinguished visitors.

Unit details and rules

Unit code DATA5710
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Assumed knowledge

Familiarity with probability theory at 4000 level (e.g., STAT4211 or STAT4214 or equivalent) and with statistical modelling (e.g., STAT4027 or equivalent). Please consult with the coordinator for further information.

Available to study abroad and exchange students


Teaching staff

Coordinator Sally Cripps,
Type Description Weight Due Length
Assignment Take-home examination
Programming and exercises for Bayesian Reg, GPs, Neural Networks & GLMs.
20% Week 03 12 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Take-home examination 2
Programming and written exercises for Graphical Models and Mixture Models.
20% Week 05 12 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Take-home examination 3
Programming and written exercises for Time Series and Deep Learning.
20% Week 06 12 hours
Outcomes assessed: LO1 LO7 LO4 LO3 LO2
Assignment group assignment Case Study Report
Technical report about a case study with real data.
25% Week 07 3000 words
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Presentation group assignment Case Study Presentation
Presentation which explains and shows the technique used in the report.
15% Week 07 30 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?

Assessment summary

3 x Take-home examinations

1 x Group report solving a problem using real data 

1 x Group presentation on problem

Assessment criteria

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



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:

5% per day

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 Bayesian Linear Regression and GPs Lecture and tutorial (5 hr) LO2 LO3 LO4
Week 02 Neural Networks (Stochastic Gradient Descent and ADAM) Lecture and tutorial (5 hr) LO2 LO3 LO4 LO5 LO6
Week 03 Generalised Linear Models Lecture and tutorial (5 hr) LO2 LO3 LO4 LO5
Week 04 Graphical Models (Bayesian Networks) Lecture and tutorial (5 hr) LO2 LO3 LO4 LO5
Week 05 Mixture Models (finite and infinite mixtures) Lecture and tutorial (5 hr)  
Time Series and Longitudinal Models, State Space and Random Effect models. Lecture and tutorial (5 hr) LO2 LO3 LO4 LO5
Week 06 Deep Learning – Advanced Neural Networks (CNN, RNN) Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 07 Recap and Implementation of a probabilistic model for real environmental data. Lecture and tutorial (5 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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 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. Demonstrate a coherent and advanced understanding of key concepts in computational statistics.
  • LO2. Apply fundamental principles and results in statistics to solve given problems.
  • LO3. Distinguish and compare the properties of different types of statistical models and statistical methods applicable to them.
  • LO4. Identify assumptions required for various statistical methods to be valid and devise methods for testing these assumptions.
  • LO5. Devise statistical solutions to complex problems.
  • LO6. Adapt various computational techniques to build software for solving particular statistical problems.
  • LO7. Communicate coherent statistical arguments appropriately to student and expert audiences, both orally and through written work.

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

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

This is the first time this unit has been offered.


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

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