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.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Evaluate the strengths, weakness, uses and abuses of large observational datasets in biology.
- Use R to read, analyse, and visualise large biological datasets.
- Write clear, concise, and reproducible reports for different audiences, with engaging and informative visualisations, using R and GitHub.
- Generate and test new hypotheses using observational datasets.
- Provide and respond to clear and actionable peer feedback.
- Use online resources to develop new data science skills.
Indicative Assessment
- Weekly quizzes on tools and techniques of data science in Weeks 1-6 (20) [LO 1,2]
- Short data reports, covering different aspects of data analysis, visualisation, and interpretation. Hurdle requirement to submit at least three reports. (45) [LO 1,2,3,4,5]
- Long data report providing an in-depth analysis of a larger dataset. Hurdle requirement to pass this assessment. (35) [LO 1,2,3,4,6]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
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.
Inherent Requirements
To be determined
Requisite and Incompatibility
Prescribed Texts
R for Data Science: Hadley Wickham and Garrett Grolemund.
Available for free online: https://r4ds.had.co.nz/
Assumed Knowledge
Students who have not completed BIOL2202 , or have the equivalent knowledge in R and statistics from other courses or experience can enrol and are encouraged to contact the convenor. You will be accepted to the course if you can show that other courses you have taken cover the relevant material in statistics and the use of R that would have been covered in BIOL2202, or if you are willing to undertake such learning before starting BIOL3207 (resources for this will be provided). This can be determined by discussion with the convenor.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4440 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 3803 | 19 Feb 2024 | 26 Feb 2024 | 05 Apr 2024 | 24 May 2024 | In Person | View |
