Quantitative Life Sciences

Quantitative Life Sciences is an interdisciplinary major. Units of study in this major are available at standard and advanced level.

About the major

This interdisciplinary major combines mathematics, statistics and information technology and applies them in areas of biological data analytics. This will give you the opportunity to explore the areas of bioinformatics, mathematical modelling and interpretation of data, all of which have become essential elements of biological research. It is a highly recommended second field of study for all students majoring in the life and environmental sciences.

Requirements for completion

A major in Quantitative Life Sciences requires 48 credit points, consisting of:

(i) 6 credit points of 1000-level selective units
(ii) 6 credit points of 1000-level core units
(iii) 12 credit points of 2000-level selective units
(iv) 6 credit points of 3000-level methodology units
(v) 18 credit points of 3000-level selective specialisation units

A minor in Quantitative Life Sciences is available and articulates to this major.

Pathway through the major

The requirements for a major in Quantitative Life Sciences are spread out over three years of the degree (possibly four years if students are completing a combined Bachelor of Advanced Studies degree).

A sample pathway for the Quantitative Life Sciences major (over three years of a degree) is listed below.

Sample pathway: Quantitative Life Sciences major (48 credit points)

Year	Session	Units of study
First	Semester 1 or 2	Selective: 1000-level units listed for major
	Semester 2	Core: BIOL1XX7 From Molecules to Ecosystems
Second	Semester 1	Selective: 2000-level units listed for major
	Semester 2	Selective: 2000-level units listed for major
Third	Semester 1	Selective: 3000-level units listed for major
	Semester 2	Selective: 3000-level units listed for major

Please Note. This sample progression is meant as an example only. Depending on unit prerequisites, students may be able to complete these units in a different sequence from that displayed in the table above.

For details of the core and selective units of study required for the major or minor please refer to the Quantitative Life Sciences section of the unit of study table, Table S, in this handbook.

Fourth year

The fourth year is only offered within the combined Bachelor of Science/Bachelor of Advanced Studies course.

Advanced Coursework
The Bachelor of Advanced Studies advanced coursework option consists of 48 credit points, which must include a minimum of 24 credit points in a single subject area at 4000-level, including a project unit of study worth at least 12 credit points. Space is provided for 12 credit points towards the second major (if not already completed). 24 credit points of advanced study will be included in the table for 2020.

Honours
Requirements for Honours in the area of Quantitative Life Sciences: completion of 36 credit points of project work and 12 credit points of coursework.

Honours units of study will be available in 2020.

Contact and further information

W http://sydney.edu.au/science/life-environment/
E

T +61 2 9351 3012

Address:
Charles Perkins Centre D17
University of Sydney NSW 2006

Dr Mark Larance
E
T +61 02 8627 5571

Learning Outcomes

Students who graduate from Quantitative Life Sciences will be able to:

1. Recognise when higher order quantitative skills are needed for a systematic approach to the discovery of scientific conclusion based on large volumes of scientific data.
2. Identify at a general level the type of analytical approach that is required, whether that is data analysis, simulation models or equation-based models.
3. Understand the importance of experimental design and its relationship with data output and analysis.
4. Translate questions between disciplines and perform appropriate statistical analysis. To be confident and knowledgeable in using a range of computational resources, including R, Python and other scripting languages (for statistical analysis, remote sensing, machine learning and publication quality visualisation and computational modelling), scientific formats (i.e. netCDF), databases (for storing and accessing metadata) and different approaches to graphical information systems (for mapping and sharing 2 dimensional data).
5. Connect to online data services, for meta-analysis, sub-setting and consumption of large data without the need to “make a local copy”.
6. Represent biological processes as mathematical or computational models and to use these models to explore, explain and predict scientific phenomena.
7. Interpret large-scale data sets and be able to highlight trends of most significance.