Outline

Overview

ENVX3002 is designed to prepare students to analyse complex data typically encountered in the life and environmental sciences. Challenges such as non-normality, missing observations, spatial and temporal correlations, or datasets too large for standard models are addressed. Presented in an applied context, the unit emphasizes the correct analysis of authentic datasets and the interpretation of results. The course begins with the design and analysis of experimental data based on the general linear model. In the second part, students explore the generalization of the general linear model to accommodate non-normal data, focusing on binary and count data. The third part introduces linear mixed models, providing tools to analyze datasets that violate the assumptions of independent and equal errors, such as data correlated in space and time. The unit concludes with an introduction to machine learning and predictive modelling. A key feature of this unit is the use of R programming to develop coding skills that are becoming essential for future careers in the life and environmental sciences.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	ENVX2001 or STAT2X12 or BIOL2X22 or DATA2X02 or QBIO2001
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
Lecturer(s)	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
	Aaron Greenville, aaron.greenville@sydney.edu.au
	Si Yang Han, siyang.han@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Exam Final exam	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Machine Learning: Report 4 Data analysis and written report	10%	STUVAC Due date: 02 Jun 2025 at 23:59	See Canvas instructions
Outcomes assessed:
Assignment	Classical Linear Modelling: Report 1 Data analysis and written report	10%	Week 05 Due date: 24 Mar 2025 at 23:59	See Canvas instructions
Outcomes assessed:
Assignment	Generalised and Additive Modelling: Report 2 Data analysis and written report	10%	Week 08 Due date: 14 Apr 2025 at 23:59	See Canvas instructions
Outcomes assessed:
Assignment	Mixed-Effects Modelling: Report 3 Data analysis and written report	10%	Week 11 Due date: 12 May 2025 at 23:59	See Canvas instructions
Outcomes assessed:
= AI allowed ?

Assessment summary

Assessment tasks: Detailed information for each assessment can be found on the Canvas site.

Final exam: This assessment is compulsory and failure to attend, attempt, or submit will result in the award of an AF grade.

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.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	At HD level, a student demonstrates a flair for the subject as well as a detailed and comprehensive understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to a student who demonstrates the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.
Distinction	75 - 84	At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.
Credit	65 - 74	At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the unit material and can solve routine problems and/or identify and superficially discuss theoretical concepts.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the unit material. A ‘Pass’ reflects satisfactory achievement and is awarded to a student who has threshold knowledge.
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) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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:

Any assessment submitted after the due time and date (or extended due time and date) will incur a late penalty of 5% of the total marks per 24 hour period, or part thereof, late (note that this is applied to the mark gained after the submitted work is marked)

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.

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	Classical Linear Modelling 1	Workshop (5 hr)
Week 02	Classical Linear Modelling 2	Workshop (5 hr)
Week 03	Classical Linear Modelling 3	Workshop (5 hr)
Week 04	Generalised and Additive Modelling 1	Workshop (5 hr)
Week 05	Generalised and Additive Modelling 2	Workshop (5 hr)
Week 06	Generalised and Additive Modelling 3	Workshop (5 hr)
Week 07	Mixed-Effects Modelling 1	Workshop (5 hr)
Week 08	Mixed-Effects Modelling 2	Workshop (5 hr)
Week 09	Mixed-Effects Modelling 3	Workshop (5 hr)
Week 10	Machine Learning 1	Workshop (5 hr)
Week 11	Machine Learning 2	Workshop (5 hr)
Week 12	Machine Learning 3	Workshop (5 hr)

Attendance and class requirements

Under the Faculty of Science Resolutions (Policy):

(1) Students are expected to attend at least 80% of timetabled activities as defined in the unit of study outline, unless granted exemption by the Associate Dean.

(2) A student may fail a unit of study because of inadequate attendance

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. Demonstrate advanced proficiency in experimental design and analysis of variance, including the application of the general linear model, through comprehensive data analyses and report writing.
LO2. Apply generalised linear models and generalised additive models to natural science datasets, critically assessing and interpreting the results within the context of their field-specific data.
LO3. Perform repeated measures analysis and utilise generalised linear mixed models, showcasing their ability to handle complex and dynamic datasets typical in natural sciences research
LO4. Integrate basic machine learning techniques into their data analysis repertoire, demonstrating an understanding of their application and relevance in the natural sciences
LO5. Develop and refine their research skills and coding skills, preparing them for their professional career, honours-level projects and/or postgraduate research, by critically evaluating data analysis methods and results, and effectively communicating their findings in detailed reports.

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.

Overall the feedback was positive for 2024 however there are things we can improve including: 1. Making some of the module material more concise. 2. More breaks during the workshop. 3. Ensure due dates are set correctly in Akari 4. Make the lab component more challenging i.e. to allow students to code more independently. 5. We will try to extend summary notes to more modules.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

ENVX3002: Statistics in the Natural Sciences

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI) and automated writing tools

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links