Key Information

Please note that we do not print this course guide for a few reasons:

  1. we can make updates to the course guide during semester when hosted online like it is,
  2. printing it loses some of the functionality of the course guide with the way the code is presented once printed, and
  3. we are also keen to save some trees from unnecessary printing!

However, if you want to print it, you’re more than welcome to do so. The easiest way to do this is printing to PDF from your browser.

Course outline

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 visualizing 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, 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 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!

Installing R and RStudio

Note that lab computers will already have R andRStudio installed; however, if you plan to use your personal computer then you will need to install both R and RStudio yourself. Follow the directions in Installing R and RStudio to do so (or come along to a dedicated help session).

Another option available is to use RStudio Cloud where, everything is run in a web browser (on a remote server) and doesn’t require you to download the software onto your personal computer.

Your teaching team

Semester 2, 2026

Module Number Module Content Weeks taught Lecturer
1 R programming and data exploration 1 - 3 Assoc. Prof. Beatrix Jones
1 Statistical inference 4 - 6 Jenn Jury (course coordinator)
2 Mathematical modelling in biology 7 - 9 Dr Nobuto Takeuchi
3 Model-based inference and critical thinking 10 - 12 Assoc. Prof. Dave Aguirre

Lectures

You should stay on top of lectures as this will assist you with your learning. Please attend lectures if in L01C, but lecture recordings will also be available in CANVAS via the Panopto Video tab after the lecture has been delivered (within 24 hours, usually earlier than this).

Assessment

Assessment structure

## Loading required package: tidyverse
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.2.1     ✔ readr     2.2.0
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.3     ✔ tibble    3.3.1
## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: knitr
Week Laboratories Quizzes Inspera
1 R help sessions
2 lab quiz
3 lab quiz
4 quiz
5 lab quiz
6 lab quiz
Mid-semester break
7 quiz Test
8 lab quiz
9 lab quiz
10 quiz
11 lab quiz
12 lab quiz
Exam Period Exam

Assessment summary

Week of semester Weighting per assessment (%) Total weighting (%)
Laboratory 2, 3, 5, 6, 8, 9, 11, 12 5 40
Weekly quiz 2 - 12 inclusive 1 10
Test 7 20 20
Exam Exam period 30 30

Laboratories

Labs are three (3) hours and the material is designed to be completed in this time.

You are scheduled into a lab stream in SSO. While lab attendance is not compulsory, it is highly recommended you attend your scheduled lab stream. There will be an assessed in-class discussion in the week 6 lab, so attendance and a preparatory worksheet for that lab will be required to gain the grades for that part of the assessment. There is teaching staff to support you through the tasks and answer your course-related questions.

Weekly quizzes

The weekly quizzes are due at the end of each week from week 2 - week 12 (due Sunday at 10pm). These are designed for you to solidify concepts and practice skills taught each week and thereby keep up with the material.

There are practice quizzes for you to attempt prior to sitting the weekly quiz, these are not assessed. There is also a practice quiz for week 1.

RStudio Accessibility

RStudio has many features and options to help you work the way that best suits your needs. Here is a great article outlining RStudio accessibility features. If you need further support, please reach out to the BIOSCI 220 teaching staff.