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 the life and environmental sciences. This will give you the opportunity to explore the areas of ecosystem and molecular scale modelling and interpretation of data, all of which have become essential elements of scientific 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) 6 credit points of 3000-level core interdisciplinary project units
(vi) 12 credit points of 3000-level selective specialisation or interdisciplinary project selective unit

A minor in Quantitative Life Sciences requires 36 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) 6 credit points of 3000-level selective specialisation units

First year

BIOL1XX7 From Molecules to Ecosystems and 6 credit points from a selection of: DATA1001 Foundations of Data Science, ENVX1002 Introduction to Statistical Methods, MATH1015 Biostatistics, MATH1X05 Statistical Thinking with Data, MATH1X02 Linear Algebra, MATH1011 Applications of Calculus, MATH1013 Mathematical Modelling, MATH1014 Introduction to Linear Algebra.

In the first year of study you develop a base level knowledge of biology through BIOL1XX7 which covers scales from the molecules to ecosystems. This is complemented by development of quantitative skills in mathematics, statistics and scripting.

Second year

12 credit points from a selection of: DATA2002 Data Analytics: Learning from Data, BIOL2X22 Biology Experimental Design and Analysis, ENVX2001 Applied Statistical Methods, QBIO2001 Molecular Systems Biology.

In the second year you have a choice of units depending on your future interests. BIOL2X22 and ENVX2001 development skills in experimental design and data analysis that underpin all of the life and environmental sciences. QBIO2001 develops skills in the areas of -omic data analysis and technologies, whereas DATA2002 develops skills in advanced statistical modelling of data.

Third year

6 credit points from a selection of: ENVX3002 Statistics in the Natural Sciences, QBIO3X01 Molecular Systems Biology
and 18 credit points from a selection of: ENVX3002 Statistics in the Natural Sciences, QBIO3X01 Molecular Systems Biology, BCHM3X92 Proteomics and Functional Genomics, BINF3101 Bioinformatics Project, ENVX3001 Environmental GIS, LWSC3007 Advanced Hydrology and Modelling, GEGE3X04 Computational Genomics, PRJT3XXX, AMED3002 Interrogating Biomedical and Health Data, STAT3X14 Applied Statistics.

In the third year you must choose at least one of 2 units, QBIO3X01 which builds on QBIO2x01 in the area of -omics data analysis and ENVX3002 which adds to the toolbox of methods introduced in earlier years with an emphasis on data that is not “well behaved” due to not meeting assumptions or is simply too large in size. In addition you must take at least one designated project unit. The elective choices allow you to develop skills in specialist analysis approaches related to disciplines areas including biochemistry, environmental science, genetics and medical science

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, with a minimum of 24 credit points at 4000-level or above. Of these 24 credit points, you must complete a project unit of study worth at least 12 credit points. Advanced coursework will be included in the table for 2020.

Honours
Meritorious students in the Bachelor of Science/Bachelor of Advanced Studies may apply for admission to Honours within a subject area of the Bachelor of Advanced Studies. Admission to Honours requires the prior completion of all requirements of the Bachelor of Science, including Open Learning Environment (OLE) units. If you are considering applying for admission to Honours, ensure your degree planning takes into account the completion of a second major and all OLE requirements prior to Honours commencement.

Unit of study requirements for Honours in the area of Quantitative Life Sciences: completion of 24 credit points of project work and 24 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 5819

Address:
School of Life and Environmental Sciences
Level 5, Carslaw Building (F07)
Eastern Avenue
The University of Sydney NSW 2006


A/Prof Thomas Bishop
E
T +61 2 8627 1056

Learning Outcomes

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

  1. Exhibit a broad and coherent body in knowledge of foundation scientific concepts and recognise when higher-order quantitative skills are needed for a systematic approach to the analysis and discovery of patterns within large volumes of scientific data.
  2. Exhibit depth of knowledge in the principles and importance of experimental design and its relationship with data output and analysis.
  3. Integrate knowledge of data structure and quantitative methods to identify suitable analytical approaches for various datasets, whether that is data analysis, simulation models or equation-based models.
  4. Translate questions between disciplines and perform appropriate statistical analysis.
  5. Use a range of computational resources including programming languages, databases and graphical information systems, to address questions in the life sciences.
  6. Communicate concepts and findings in quantitative life sciences through a range of modes for a variety of purposes and audiences, using evidence-based arguments that are robust to critique.
  7. Analyse and interpret large-scale data sets, connecting to online data services, and highlight trends of most significance.
  8. Create mathematical or computational models to represent biological processes and use these models to explore, explain and predict scientific phenomena.
  9. Address authentic problems in quantitative life sciences, working professionally and responsibly and with consideration of cross-cultural perspectives, within collaborative, interdisciplinary teams.