Search Course Outline
12 course outlines found
1
BIOSCI 220
: Quantitative Biology2025 Semester Two (1255)
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
Prerequisite: 30 points from BIOSCI 101-109
2
BIOSCI 220
: Quantitative Biology2025 Semester One (1253)
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
Prerequisite: 30 points from BIOSCI 101-109
3
BIOSCI 220
: Quantitative Biology2024 Semester Two (1245)
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
Prerequisite: 30 points from BIOSCI 101-109
4
BIOSCI 220
: Quantitative Biology2024 Semester One (1243)
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
Prerequisite: 30 points from BIOSCI 101-109
5
BIOSCI 220
: Quantitative Biology2023 Semester Two (1235)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: 30 points from BIOSCI 101-109
6
BIOSCI 220
: Quantitative Biology2023 Semester One (1233)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: 30 points from BIOSCI 101-109
7
BIOSCI 220
: Quantitative Biology2022 Semester Two (1225)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: 30 points from BIOSCI 101-109
8
BIOSCI 220
: Quantitative Biology2022 Semester One (1223)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: 30 points from BIOSCI 101-109
9
BIOSCI 220
: Quantitative Biology2021 Semester Two (1215)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109
10
BIOSCI 220
: Quantitative Biology2021 Semester One (1213)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109
11
BIOSCI 220
: Quantitative Biology2020 Semester One (1203)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109
12
BIOSCI 220
: Quantitative Biology2020 Semester Two (1205)
Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101
Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109
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