Outline

Overview

This is an introductory data science unit for students in the agricultural, life and environmental sciences. It provides the foundation for statistics and data science skills that are needed for a career in science and for further study in applied statistics and data science. The unit focuses on developing critical and statistical thinking skills for all students. It has 4 modules: exploring data, modelling data, sampling data and making decisions with data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem-solving skills in a team setting. Taught interactively with embedded technology, ENVX1002 develops critical thinking and skills to problem-solve with data.

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)	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
	Aaron Greenville, aaron.greenville@sydney.edu.au
	Januar Harianto, januar.harianto@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Multiple choice & short answer questions	45%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Describing Data Report submitted via Turn-it-in	10%	Week 05 Due date: 24 Mar 2023 at 23:59	Please see Assignment outline on Canvas
Outcomes assessed:
Supervised test ?	Mid-semester exam CANVAS timed, open book exam. Analyse a dataset with R with SAQs.	20%	Week 08	1 hour
Outcomes assessed:
Assignment	Comparing two sample populations Report submitted via Turn-it-in	10%	Week 10 Due date: 05 May 2023 at 23:59	Please see Assignment outline on Canvas
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 ?

Assessment summary

Using skills and concepts learnt in Lectures, Tutorials and Practical sessions, there are three main assignments associated with each of the three modules, there a mid-semester test and a final exam. All assessments are to be completed individually, with the exception of Assessment 3 which is a group assignment.

Detailed information for each assessment can be found on Canvas.

Assessment 1: Students are to find 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 exam: will cover key concepts learnt throughout the first half of the semester.
Assessment 2: Students write a report comparing two sample populations by collecting data through an experiment or combining datasets found online, using an appropriate statistical test.
Assessment 3: Requires students to collect data and determine whether there is a relationship between two continuous variables. Data collection, analysis and presentations are conducted as a group, followed by completing a peer review exercise.
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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction and Scientific Method; Lectures 1 & 2	Lecture (2 hr)
	Introduction to statistics and the scientific method - Guided tutorial	Independent study (1 hr)
	Transition & Introduction to statistics and the scientific method	Computer laboratory (2 hr)
Week 02	Exploring Data	Lecture (2 hr)
	Exploring data - Guided tutorial	Independent study (1 hr)
	Exploring data	Computer laboratory (2 hr)
Week 03	Normal and discrete distributions	Lecture (2 hr)
	Normal and discrete distributions - Guided tutorial	Independent study (1 hr)
	Normal and discrete distributions	Computer laboratory (2 hr)
Week 04	Sampling distributions	Lecture (2 hr)
	Sampling distributions - Guided tutorial	Independent study (1 hr)
	Sampling distributions	Computer laboratory (2 hr)
Week 05	1 - sample tests	Lecture (2 hr)
	1 - sample tests - Guided tutorial	Independent study (1 hr)
	1 - sample tests	Computer laboratory (2 hr)
Week 06	2 - sample tests	Lecture (2 hr)
	2 - sample tests - Guided tutorial	Independent study (1 hr)
	2 - sample tests	Computer laboratory (2 hr)
Week 07	Non-parametric tests I	Lecture (2 hr)
	Non-parametric tests I - Guided tutorial	Independent study (1 hr)
	Non-parametric tests I	Computer laboratory (2 hr)
Week 08	Non-parametric tests II	Lecture (2 hr)
	Non-parametric tests II - Guided tutorial	Independent study (1 hr)
	Non-parametric tests II	Computer laboratory (2 hr)
Week 09	Describing relationships	Lecture (2 hr)
	Describing relationships - Guided tutorial	Independent study (1 hr)
	Describing relationships	Computer laboratory (2 hr)
Week 10	Simple linear regression	Lecture (2 hr)
	Simple linear regression - Guided tutorial	Independent study (1 hr)
	Simple linear regression	Computer laboratory (2 hr)
Week 11	Multiple linear regression	Lecture (2 hr)
	Multiple linear regression - Guided tutorial	Independent study (1 hr)
	Multiple linear regression	Computer laboratory (2 hr)
Week 12	Non-linear regression	Lecture (2 hr)
	Non-linear regression - Guided tutorial	Independent study (1 hr)
	Non-linear regression	Computer laboratory (2 hr)
Week 13	Revision	Lecture (1 hr)
Week 13	Group presentations	Presentation (2 hr)

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. Describe the role of statistics, experimentation and hypothesis testing in relation to scientific research.
LO2. Understand the concept of probability and calculate probabilities by applying probability laws and theoretical results
LO3. Perform data exploration using R.
LO4. Understand the concept of experimental inference and select the correct statistical test (among 1-sample, 2-sample, chi-square, and non-parametric tests) appropriate for a particular experiment.
LO5. Demonstrate proficiency in the use R and Excel to describe and analyse data from simple experiments.
LO6. Model relationships between variables using linear and non-linear functions using R and Excel.
LO7. Write and present statistical and modelling results as part of a scientific report and oral presentation as a individual and as a team.

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 such as improving feedback for assessments. We have updated the look of the canvas site to help guide students to relevant sections and the course is designed so that students work through the modules in consecutive order. We have adjusted the assessment weights such that the exam is now worth 45% and not 55% as well as balancing up the assessment weightings. Furthermore, we will decrease the length of the guided tutorials and there are no longer practicals directly after the lecture which will hopefully allow for more time to complete the tutorial prior to the practical. We continue to work towards making classes more engaging.

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.

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

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

ENVX1002: Introduction to Statistical Methods

Semester 1, 2023 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

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