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

The Quantitative Life Sciences major and minor requirements are listed in the Quantitative Life Sciences unit of study table.

Contact and further information

School of Life and Environmental Sciences
Level 5, Carslaw Building F07
University of Sydney NSW 2006

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

A/Prof Thomas Bishop
T +61 2 8627 1056

School of Mathematics and Statistics
Level 5, Carslaw Building F07
University of Sydney NSW 2006

Professor Jean Yang
T +61 2 9351 3012

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.