Outline

Overview

Biotechnological advances have given rise to an explosion of original and shared public data relevant to human health. These data, including the monitoring of expression levels for thousands of genes and proteins simultaneously, together with multiple databases on biological systems, now promise exciting, ground-breaking discoveries in complex diseases. Critical to these discoveries will be our ability to unravel and extract information from these data. In this unit, you will develop analytical skills required to work with data obtained in the medical and diagnostic sciences. You will explore clinical data using powerful, state of the art methods and tools. Using real data sets, you will be guided in the application of modern data science techniques to interrogate, analyse and represent the data, both graphically and numerically. By analysing your own real data, as well as that from large public resources you will learn and apply the methods needed to find information on the relationship between genes and disease. Leveraging expertise from multiple sources by working in team-based collaborative learning environments, you will develop knowledge and skills that will enable you to play an active role in finding meaningful solutions to difficult problems, creating an important impact on our lives.

Unit details and rules

Unit code	AMED3002
Academic unit	Science Faculty
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Exploratory data analysis, sampling, simple linear regression, t-tests, confidence intervals and chi-squared goodness of fit tests, familiar with basic coding, basic linear algebra.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Ellis Patrick, ellis.patrick@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Lab report: module 1 Report	2.5%	Week 03	4 pages
Outcomes assessed:
Assignment	Lab report: module 2 Report	2.5%	Week 06	4 pages
Outcomes assessed:
Assignment	Multimedia Project	10%	Week 07	3 minutes, 500 words
Outcomes assessed:
Presentation	Oral presentation 1 Oral presentation	10%	Week 07	10 minutes
Outcomes assessed:
Assignment	Reflection 1 Written reflection	2.5%	Week 07	2 pages
Outcomes assessed:
In-semester test	Skill-based exam Skill based examination	40%	Week 08	2 hours
Outcomes assessed:
Assignment	Lab report: module 3 Report	2.5%	Week 09	4 pages
Outcomes assessed:
Assignment	Lab report: module 4 Report	2.5%	Week 12	4 pages
Outcomes assessed:
Presentation	Oral presentation 2 Oral presentation	10%	Week 13	10 minutes
Outcomes assessed:
Assignment	Reflection 2 Written reflection	2.5%	Week 13	2 pages
Outcomes assessed:
Assignment	Project report Report	15%	Week 14 (STUVAC)	15 pages
Outcomes assessed:
= group assignment ?

Assessment summary

Skilled-based exam. Students will be given an opportunity to demonstrate individual mastery of foundation analytic skills and coding ability in an exam based setting.
Multimedia. Students will source a publicly available dataset of their choice, formulate and test appropriate scientific hypotheses and create a multimedia (pamphlet, website, Youtube video etc.) to communicate their findings.
Reproducible report. Students will identify a manuscript that uses publicly available high-dimensional biomedical data. They will generate an Rmarkdown report which reproduces the analysis in the manuscript and comment on any discrepencies or interesting extensions of the analysis.
Lab reports. Students will submit reports summarising the work they have completed in class every 4 weeks to ensure they are keeping up with the course content.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a High distinction indicates work of an exceptional standard, a Distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Mastery of topics showing extensive integration and ability to transfer knowledge to novel contexts; treatment of tasks shows an advanced synthesis of ideas; demonstration of initiative, complex understanding and analysis; work is very well presented; all criteria addressed and learning outcomes achieved to an outstanding level.

Distinction

75 - 84

Excellent achievement, consistent evidence of deep understanding and application of knowledge in medical science; treatment of tasks shows advanced understanding of topics; demonstration of initiative, complex understanding and analysis; work is well-presented; all criteria addressed and learning outcomes achieved to a superior level.

Credit

65 - 74

Confident in explaining medical science processes, with evidence of solid understanding and achievement; occasional lapses indicative of unresolved issues; treatment of tasks shows a good understanding of topic; work is well-presented with a minimum of errors; all criteria addressed and learning outcomes achieved to a high level.

Pass

50 - 64

Satisfactory level of engagement with and understanding of topic; some inconsistencies in understanding and knowledge of medical science; work is adequately presented, with some errors or omissions, most criteria addressed and learning outcomes achieved to an adequate level.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades

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:

All assignments must be submitted by the due date and quizzes and exams attended when they are scheduled. Students are expected to manage their time and to prioritise tasks to meet deadlines. Assessment items submitted after the due date without an approved extension using a special consideration or special arrangement form or request will incur penalties.

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.

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	Importance of data: investigating biomedical and health data	Seminar (5 hr)
Week 02	Importance of data: data structure and bias	Seminar (5 hr)
Week 03	Importance of data: starting a project	Seminar (5 hr)
Week 04	Clinical data analysis: understanding your data	Seminar (5 hr)
Week 05	Clinical data analysis: analysis of variance	Seminar (5 hr)
Week 06	Clinical data analysis: regression	Seminar (5 hr)
Week 07	Omics data analysis: omics	Seminar (5 hr)
Week 08	Omics data analysis: testing in high dimensions	Seminar (5 hr)
Week 09	Omics data analysis: interpretation	Seminar (5 hr)
Week 10	Big data: concepts behind statistical and machine learning	Seminar (5 hr)
Week 11	Big data: prediction and classification	Seminar (5 hr)
Week 12	Big data: model evaluation: model evaluation	Seminar (5 hr)

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator, and attend another session, if available.

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. design and evaluate an appropriate modelling approach to analyse a variety of experimental designs that address different complex biomedical questions
LO2. extract, utilise and combine data from multiple public data resources
LO3. formulate scientific questions and use coding language to produce, interpret and compare numerical and graphical summaries of the corresponding complex biomedical data
LO4. justify the appropriate statistical tests to analyse biomedical data using coding language and judge the robustness and stability of the chosen tests
LO5. construct infographics to explore, interpret and communicate big data
LO6. formulate, evaluate and interpret appropriate linear models to describe the relationships between multiple variables
LO7. perform statistical machine learning using an existing classifier, and create a cross-validation scheme to calculate the prediction accuracy
LO8. identify and critique the different cross-validation strategies commonly used in the literature, and critically assess their validity
LO9. use coding to create and generate a reproducible report to communicate outcomes.

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.

No substantial changes have been made since this unit was last offered.

Additional information

Science student portal

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.

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

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

Media

Student links

About us

Connect

Current students

AMED3002: Interrogating Biomedical and Health Data

Semester 1, 2020 [Normal day] - Westmead, 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

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

Media

Student links

About us

Connect