Key Information
Please read this page carefully as it contains important information about the course structure, delivery, and assessment.
Please note that we do not send an order to UBIQ to print this course guide for a few reasons:
- we can make updates to the course guide during semester when hosted online like it is,
- printing it loses some of the functionality of the course guide with the way the code is presented once printed, and
- 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!
R and RStudio
Throughout this course, you will be learning how to program using R and RStudio weekly. You are expected to install and familiarize yourself with these environments before or during the first week of the course. We provide help sessions during week 1 and easy-to-follow instructions for this, please see CANVAS and/or Installing R and RStudio to get started.
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, 2024
| Module Number | Module Content | Weeks taught | Lecturer |
|---|---|---|---|
| 1 | R programming and data exploration | 1 - 3 | Dr Charlotte Jones-Todd |
| 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 | Dr Nick Matzke |
Lectures
- Mondays and Tuesdays 9-10 am in 201-393
You should attend the lectures in-person as there will be in-class activities to assist you with your learning. However, lecture recordings will be available in CANVAS via the Panopto video tab after the lecture has been delivered (typically within 24 hours).
Assessment
Assessment structure
| 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 |
The test will be held on Monday 9th September 6.30-8.15pm. The test will cover all of Module 1 taught material only. The exam will cover both Module 2 & 3 taught material only. The delivery mode for the test and the exam is an on-campus, invigilated Inspera assessment.
Laboratories
There are eight labs in total held in weeks 2, 3, 5, 6, 8, 9, 11 and 12 (i.e., in each three week lecture block there are labs in the last two weeks). Each lab is worth 5% of your final grade (40% in total).
Labs will be held in 106-014 (Biology Building, Room 014) during your scheduled SSO lab time, which will be one of- Monday 2-5pm,
- Tuesday 10am-1pm,
- Tuesday 2-5pm, or
- Friday 10am-1pm.
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 is teaching staff to support you through the tasks and answer your course-related questions.
Weekly quizzes
There are eleven weekly quizzes in total. Each quiz is worth 1% and (if you have completed all quizzes) your one (1) quiz that has the lowest mark will be dropped from your final grade. Therefore your weekly quizzes will total 10% towards final grade.
The weekly quizzes are due at the end of each week from week 2 - week 12 (due Friday 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.