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BIOSCI 220 - Quantitative Biology

Course Overview

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Faculty

Science

Department

Biological Sciences

Points:

15

Available Semesters:

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Course Components

Labs

Tutorials

Lectures

Exam

TBLs

Workshops

Description: An introduction to mathematical, statistical and computational literacy as required for contemporary biologists. Topics include fundamentals of experimental design, data exploration and visualisation, model-based inference to process biological data into biological information, comparing statistical models, prediction using mathematical models of biological processes, critical thinking about models and effective communication of findings. Data analysis and generation is taught using the R programming language. Recommended preparation: STATS 101

Prerequisites / Restrictions

Prerequisite: 30 points from BIOSCI 101-109

Average Rating From 3 Reviews

M

Teaching Quality

4.7 / 10

M

Content Quality

4 / 10

H

Workload

8.3 / 10

H

Difficulty

7.7 / 10

33% - Would Recommend

67% - Would Not Recommend

Reviews

Bad. So bad. I can't believe I passed that paper as well as I did. You probably have to take it because it's compulsory for the BioSci major now, but on the off chance you don't: Run, run far away. I probably struggled so much because it was online but the labs are difficult and boring with little assistance. The lectures (videos, there are no in-person classes) varied from 15 minutes a week to about 2 1/2 hours. The different modules have widely different teaching styles and I hear the only good lecturer just quit to move to Australia.

Semester Two - 2020

This course isn't a popular favourite, but it's really not THAT bad. This course builds your skills in R for biological modelling (I highly suggest you practice taking the code provided to you and teasing it apart to see what each bit does. This taught me how to re-assemble the code to succeed in the assignments). It also covers statistical models of biological processes, which isn't intuitive at first, but they do a good job of explaining the logical steps to the outcomes shown in the models. If you have an open mind and want to learn (and don't let yourself immediately freeze when you see code or formulas) you'll come away from this class with really versatile foundational knowledge that comes in handy throughout the rest of your degree.

Semester One - 2022