ENVX1002: Semester 1, 2025

Overview

ENVX1002 is an introductory applied statistics and data science course tailored for students in the life and environmental sciences. It provides foundational skills essential for scientific careers and advanced studies in applied statistics and data science. Emphasizing critical and statistical thinking, the course is divided into three modules: exploring data, making decisions with data, and modelling data. Students will apply adaptive problem-solving skills to real-world issues in agriculture, biology, ecology, environmental science and health. Through interactive teaching methods and embedded technology, ENVX1002 enhances critical thinking and data-driven problem-solving in the natural sciences.

Unit details and rules

Academic unit	Life and Environmental Sciences Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ENVX1001 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or DATA1001 or DATA1901 or BUSS1020 or STAT1021 or ECMT1010
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
Lecturer(s)	Januar Harianto, januar.harianto@sydney.edu.au
	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
	Si 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 ?	Final exam Multiple choice & short answer questions	50%	Formal exam period	2 hours
Outcomes assessed:
Small test	Early Feedback Task In class quiz on concepts learnt in weeks 1+2 #earlyfeedbacktask	5%	Week 03	15 minutes
Outcomes assessed:
Assignment	Describing Data Report Report submitted via Turn-it-in	15%	Week 05 Due date: 26 Mar 2025 at 23:59	Please see Assignment outline on Canvas
Outcomes assessed:
Skills-based evaluation	Coding and Data Skills Evaluation You will be required to analyse a dataset with R Studio and answer SAQs.	15%	Week 08	50 minutes
Outcomes assessed:
Presentation	Modelling relationships in data Class presentation + peer review - see Canvas for details	15%	Week 13	5 minutes - see Canvas for details
Outcomes assessed:
= group assignment ? = AI allowed ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Using skills and concepts learnt in Lectures, Tutorials and Practical sessions, there is an Early feedback task and one major task for each of the three modules plus a final exam. All assessments are to be completed individually, with the exception of Groupwork Presentation.

Early Feedback Task: Students will complete a short online quiz at the start of their practical in week three that assesses their understanding of basic Exploratory data analysis and operation of RStudio and Excel.
Individual Report: Students are to select a dataset of interest and write a report summarizing key features of the data using R Studio, ensuring analytical methods are clearly described.
Mid-semester skills test: Students will complete a computer based skills test. Students will analyse a simulated dataset and report on their findings. This task will cover key concepts learnt throughout the first half of the semester and assess skills in analyse data using R Studio.
Groupwork presentation: Student groups will download a "large" data set of their choosing from an online data repository. Using a subset of the data, students will determine whether there is a relationship between at least two continuous variables. Data collection, analysis and presentations are conducted as a group, Students will present their results to their peers in class and students will also review the contribution of their group mates.
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	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.
Distinction	75-84	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.
Credit	65-74	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.
Pass	50-64	To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty
Fail	0-49	To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty.

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.

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 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

Weekly schedule

WK	Topic	Learning activity
Week 01	Reproducible Science	Lecture (2 hr)
	Reproducible Science	Tutorial (1 hr)
	Reproducible Science	Computer laboratory (2 hr)
Week 02	Introduction to Statistical Programming	Lecture (2 hr)
	Introduction to Statistical Programming	Tutorial (1 hr)
	Introduction to Statistical Programming	Computer laboratory (2 hr)
Week 03	Exploring and Visualising Data	Lecture (2 hr)
	Exploring and Visualising Data	Tutorial (1 hr)
	Exploring and Visualising Data	Computer laboratory (2 hr)
Week 04	The Central Limit Theorem	Lecture (2 hr)
	The Central Limit Theorem	Tutorial (1 hr)
	The Central Limit Theorem	Computer laboratory (2 hr)
Week 05	Introduction to Inference	Lecture (2 hr)
	Introduction to Inference	Tutorial (1 hr)
	Introduction to Inference	Computer laboratory (2 hr)
Week 06	Comparing Two Samples	Lecture (2 hr)
	Comparing Two Samples	Tutorial (1 hr)
	Comparing Two Samples	Computer laboratory (2 hr)
Week 07	Non-parametric Tests	Lecture (2 hr)
	Non-parametric Tests	Tutorial (1 hr)
	Non-parametric Tests	Computer laboratory (2 hr)
Week 08	bootstrapping and Randomisation	Lecture (2 hr)
Week 08	bootstrapping and Randomisation	Tutorial (1 hr)
Week 09	Describing relationships	Lecture (2 hr)
	Describing relationships	Tutorial (1 hr)
	Describing relationships	Computer laboratory (2 hr)
Week 10	Simple linear regression	Lecture (2 hr)
	Simple linear regression	Tutorial (1 hr)
	Simple linear regression	Computer laboratory (2 hr)
Week 11	Multiple linear regression	Lecture (2 hr)
	Multiple linear regression	Tutorial (1 hr)
	Multiple linear regression	Computer laboratory (2 hr)
Week 12	Non-linear regression	Lecture (2 hr)
	Non-linear regression	Tutorial (1 hr)
	Non-linear regression	Computer laboratory (2 hr)
Week 13	Revision	Lecture (1 hr)
Week 13	Group presentations	Presentation (2 hr)

Attendance and class requirements

We expect at least 80% attendance to all face-to-face teaching modes including the Lectures and the tutorial.

Attendance will be recorded in practicals with a minimum attendance of 80%. If your attendance falls below 80% you will be at risk of failing the course.

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

See reading list in Canvas.

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. By the end of this course, students will be able to implement basic reproducible research practices—including consistent data organization, documented code, and version-controlled workflows—so that their statistical analyses and results can be readily replicated and validated by others.
LO2. By the end of this course, students will demonstrate proficiency in utilizing R and Excel to effectively explore and describe life science datasets.
LO3. By the end of this course, students will be able to apply parametric and non-parametric statistical inference methods to experimental and observational data using RStudio and effectively interpret and communicate the results in the context of the data.
LO4. By the end ot this course, students will be able to put into practice both linear and non-linear models to describe relationships between variables using RStudio and Excel, demonstrating creativity in developing models that effectively represent complex data patterns.
LO5. By the end of this course, students will be able to articulate statistical and modelling results clearly and convincingly in both written reports and oral presentations, working effectively as an individual and collaboratively in a team, showcasing the ability to convey complex information to varied audiences.

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.

We appreciate the very positive feedback we have received for this unit. We will continue to make improvements based on suggestions by students including: 1. We have introduced a tutorial lecture given by the lecturers to provide more guidance in both coding and understanding statistical concepts and to prepare for the computer labs. 2. We have removed one assessment task to decrease the load on students. 3. We have removed some content at the beginning of the semester and replaced with more material on introductory coding. This will also include using AI to assist in coding. 4. We are looking to upgrade the practicals using learnr to make them more interactive and aid in learning to code but this will be work in progress. 5. We are trying to better consistency between different demonstrators and more interaction with lecturers.

Additional information

Disclaimer

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

This unit of study outline was last modified on 14 Jan 2025.

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

Early feedback task

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

ENVX1002: Introduction to Statistical Methods

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

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect