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Biology Teaching and Learning Centre · ANU College of Science and Medicine
Quantitative biology and bioinformatics demand a combination of technical skills and domain knowledge. These data-driven sciences require both a contextual understanding of the research problem and an intuitive grasp of data in the research context. In response, this course is organised around scientific reasoning across the holistic research process, from the initial motivation to the communication of findings. With data as the focus and R as the tool, students are exposed and trained in a unified view of experimental design and data analysis. Students are taught to manipulate wild data and learn from it through visualisation and statistical modelling, setting up structures to ensure transparent and reproducible analyses. Students will be challenged with real data sets from academic journal articles where they test their skills in critiquing data organisation and analytic strategies from published data. Students will complete all assignments in Rmarkdown, thereby gaining a high level of proficiency in creating well documented and reproducible analysis workflows.
Short report that demonstrates basic R programming skills
Learning outcomes 2
Short report that demonstrates ability to comprehend and critically interpret a manuscript
Learning outcomes 2, 3
Short report that demonstrates ability to design an experiment and simulate data consistent with design
Learning outcomes 2, 3, 4, 5
Short report that demonstrates ability to design and implement an appropriate data analysis workflow
Learning outcomes 2, 3, 4, 5
Short report that demonstrates effective communication about data analysis with scientists outside of their discipline
Learning outcomes 1, 2, 3, 4, 5, 6
Summative task: Report that demonstrates ability to carry out a complete study, from motivation and design through analysis and interpretation (HURDLE ASSESSMENT)
Learning outcomes 1, 2, 3, 4, 5, 6
The expected workload will consist of approximately 130 hours throughout the semester including: - Three hours of "lectorial-style" workshops per week (36 hours total) - Approximately 94 hours of self-directed study which will include preparation for class and other assessment tasks as well as synthesis of prior material. Students are expected to attend the workshops and have their laptops, as they will regularly code along with the instructor or break into groups to run numerical experiments. Work Integrated Learning Projects Several assessments, including the final project, parallel ongoing research at ANU. Students gain valuable experience on how to conduct a research study from start to finish, from the perspective of a quantitative biologist.
Nil Preliminary Reading R for Data Science (https://r4ds.had.co.nz/)