Living systems are the most complex systems in science, and biology is naturally variable and noisy due to its many internal and external influences. For these reasons, it is difficult to make inferences from and predictions about biological systems. Understanding biology requires computational skills to effectively analyse and interpret data, and multidisciplinary research approaches are becoming more common as a critical key to solving many of the complex problems of studying life and living organisms in today’s world. So, contrary to the popular undergrad biology student beliefs, statistics, mathematics and computational skills are essential in a biologist’s toolkit.
To understand modern biological research and findings, and to participate in this research (and get jobs!), skills in working with and visualising data, learning from data using models, and generating data using simulations of models are crucial. These might be classic statistical models, mathematical models, or inference with process-based models. Biologists also need to be careful and critical thinkers about data and how it is acquired with a lens on Data Sovereignty, as well as think critically about the models that we use to try to simplify, and thereby understand, the incredible complexity of biology.
BIOSCI 220 Quantitative Biology must be taken by all students in the Biological and Biomedical Sciences majors as a stage two core requirement. STATS 101 is strongly recommended prior to taking this course, although it is not a prerequisite. This course will introduce you to the programming language R to develop the aforementioned skills, with no coding experience assumed or expected. The aim is to give beginners the confidence to continue learning R and not be afraid of statistics and mathematics!