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 ?	[6 credit points from (ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001 or DATA1901)] or [3 credit points from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3 credit points from (MATH1XX5)]
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

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Exam Final exam	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Report 4 - Large data paper & predictive modelling Data analysis and written report	10%	STUVAC Due date: 01 Jun 2026 at 17:00	~1500 words	AI allowed
Outcomes assessed:
Written work	Report 1 - Experimental Design Analysis Data analysis and written report	10%	Week 05 Due date: 23 Mar 2026 at 17:00	~1000 words	AI allowed
Outcomes assessed:
Written work	Report 2 - Model-Based Data Analysis Data analysis and written report	10%	Week 08 Due date: 20 Apr 2026 at 17:00	~1500 words	AI allowed
Outcomes assessed:
Written work	Report 3 - Simulated study and modelling evaluation Data analysis and written report	10%	Week 11 Due date: 11 May 2026 at 17:00	~1500 Words	AI allowed
Outcomes assessed:
= hurdle task ?

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)

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:

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 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	Study Design and Data Interpretation 1	Lecture (2 hr)
Week 01	Study Design and Data Interpretation 1	Practical (3 hr)
Week 02	Study Design and Data Interpretation 2	Lecture (2 hr)
Week 02	Study Design and Data Interpretation 2	Practical (3 hr)
Week 03	Study Design and Data Interpretation 3	Lecture (2 hr)
Week 03	Study Design and Data Interpretation 3	Practical (3 hr)
Week 04	Making Sense of Real-World Data 1	Lecture (2 hr)
Week 04	Making Sense of Real-World Data 1	Practical (3 hr)
Week 05	Making Sense of Real-World Data 2	Lecture (2 hr)
Week 05	Making Sense of Real-World Data 2	Practical (3 hr)
Week 06	Making Sense of Real-World Data 3	Lecture (2 hr)
Week 06	Making Sense of Real-World Data 3	Practical (3 hr)
Week 07	Structure, Space & Time in Data 1	Lecture (2 hr)
Week 07	Structure, Space & Time in Data 1	Practical (3 hr)
Week 08	Structure, Space & Time in Data 2	Lecture (2 hr)
Week 08	Structure, Space & Time in Data 2	Practical (3 hr)
Week 09	Structure, Space & Time in Data 3	Lecture (2 hr)
Week 09	Structure, Space & Time in Data 3	Practical (3 hr)
Week 10	Prediction in Complex Natural Systems 1	Lecture (2 hr)
Week 10	Prediction in Complex Natural Systems 1	Practical (3 hr)
Week 11	Prediction in Complex Natural Systems 2	Lecture (2 hr)
Week 11	Prediction in Complex Natural Systems 2	Practical (3 hr)
Week 12	Prediction in Complex Natural Systems 3	Lecture (2 hr)
Week 12	Prediction in Complex Natural Systems 3	Practical (3 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. Analyse complex natural science datasets to answer substantive scientific questions, selecting and justifying appropriate statistical approaches, and critically interpreting results within a relevant disciplinary context.
LO2. Address non-linear, high-dimensional, or poorly behaved data scenarios, evaluating alternative analytical strategies when standard assumptions break down and assessing the strengths and limitations of different approaches.
LO3. Model and interpret structured, repeated, and hierarchical data commonly arising in natural science research, accounting for temporal, spatial, and experimental dependencies to draw valid scientific inferences.
LO4. Incorporate predictive and data-driven modelling approaches where appropriate, demonstrating an understanding of their role, assumptions, and limitations in supporting scientific insight rather than replacing inference.
LO5. Design, implement, and communicate reproducible data analyses, integrating statistical reasoning and coding practices to critically evaluate results and effectively communicate findings in professional-quality scientific 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 2025 however there are things we will improve including: 1. improve marking rubrics. 2. work on assignment spacing. 3. provide more opportunity for students to work on their own during the second half of the workshop and provide more incentive to complete the exercises. 4. give more choice of dataset for assessments

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

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

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, 2026 [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)

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