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Unit of study_

AMED3002: Interrogating Biomedical and Health Data

2024 unit information

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

Managing faculty or University school:

Department of Medical Sciences

Code AMED3002
Academic unit Department of Medical Sciences
Credit points 6
Prerequisites:
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DATA1X01 or 48 credit points of any 1000 or 2000-level units in (ANAT, AVBS, BCMB, BIOL, BIOS, CHEM, COMP, COSC, DATA, ENVX, EXSS, FOOD, FMHU, GEGE, HPSC, HSBH, IMMU, MATH, MEDS, MICR, MIMI, PCOL, PHSI, PHYS, PSYC, QBIO, or STAT)
Corequisites:
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None
Prohibitions:
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None
Assumed knowledge:
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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

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.

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 1 2024
Normal day Westmead, Sydney
Session MoA ?  Location Outline ? 
Semester 1 2020
Normal day Westmead, Sydney
Semester 1 2021
Normal day Remote
Semester 1 2021
Normal day Westmead, Sydney
Semester 1 2022
Normal day Remote
Semester 1 2022
Normal day Westmead, Sydney
Semester 1 2023
Normal day Remote
Semester 1 2023
Normal day Westmead, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.