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Biology Teaching and Learning Centre · ANU College of Science and Medicine
Answering big questions in biology increasingly relies on analysing huge datasets. Modern biology relies on the generation and sharing huge databases of species traits and occurrences, environmental measurements, remote sensing data, public health information, and countless other data types from a wide array of sources. These data have enormous value for basic and applied biology. This course will teach you the skills necessary to analyse and interpret these data for a variety of audiences. In this course you will learn how to analyse, visualise, and make sense of big data in biology. You will learn the tools and techniques you need to draw robust conclusions from big data, avoid common pitfalls, and communicate your findings clearly and concisely to diverse audiences. This course will use real data from a wide range of sources. As large data sets are now commonly used to drive policy, for example in health or environmental management, the skills and knowledge you gain in this course will set you up for a successful career in many areas or further research in biology. Note: Graduate students attend joint classes with undergraduates but will be assessed separately.
Weekly quizzes on tools and techniques of data science in Weeks 1-6.
Learning outcomes 1, 2
Short data reports, covering different aspects of data analysis, visualisation, and interpretation. Hurdle requirement to submit at least three reports.
Learning outcomes 1, 2, 3, 4, 5
New method tutorial: to produce an interactive tutorial on one of a range of methods, following guided reading.
Learning outcomes 6
Long data report providing an in-depth analysis of a larger dataset. Hurdle requirement to pass this assessment.
Learning outcomes 1, 2, 3, 4, 5, 6
The expected workload will consist of approximately 130 hours throughout the semester including: - Face-to-face component, which may consist of 1 x 3 hour workshop plus 1 x 1 hour tutorial per week. - Approximately 1 hour of pre-recorded lecture material per week. - Approximately 70 hours of self-directed study (independent work), which will include preparation for workshops, tutorials and other assessment tasks. Independent work includes guided reading, watching videos, listening to podcasts, coding in R, using GitHub, and writing data reports.
R for Data Science: Hadley Wickham and Garrett Grolemund. Available for free online: https://r4ds.had.co.nz/