• Offered by Biology Teaching and Learning Centre
  • ANU College ANU Joint Colleges of Science
  • Course subject Biology
  • Areas of interest Statistics, Bioinformatics, Biology
  • Work Integrated Learning Projects
  • Academic career PGRD
  • Course convener
    • Prof Eric Stone
  • Mode of delivery In Person
  • Offered in Second Semester 2024
    See Future Offerings
  • STEM Course

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.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Communicate effectively an professionally about data analysis with scientists outside of their discipline;
  2. Translate biological goals and challenges into underpinning quantitative/computational problems;
  3. Engage in an independent investigation and evaluation of quantitative/computational solutions to biological problems;
  4. Describe and defend a chosen methodology in the context of the problem being solved;
  5. Estimate the difficulty of a project and the time commitment that will be required;
  6. Identify when additional skills and/or expertise will be required to solve a problem.

Work Integrated Learning


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.

Indicative Assessment

  1. Short report that demonstrates basic R programming skills (10) [LO 2]
  2. Short report that demonstrates ability to comprehend and critically interpret a manuscript (10) [LO 2,3]
  3. Short report that demonstrates ability to design an experiment and simulate data consistent with design (10) [LO 2,3,4,5]
  4. Short report that demonstrates ability to design and implement an appropriate data analysis workflow (10) [LO 2,3,4,5]
  5. Short report that demonstrates effective communication about data analysis with scientists outside of their discipline (10) [LO 1,2,3,4,5,6]
  6. Summative task: Report that demonstrates ability to carry out a complete study, from motivation and design through analysis and interpretation (HURDLE ASSESSMENT) (50) [LO 1,2,3,4,5,6]

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.


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.

Inherent Requirements

No specific inherent requirements have been identified for this course

Requisite and Incompatibility

You will need to contact the Biology Teaching and Learning Centre to request a permission code to enrol in this course.

Prescribed Texts


Preliminary Reading

R for Data Science (https://r4ds.had.co.nz/)

Assumed Knowledge

This course assumes some familiarity with the fundamentals of biology and statistics. The programming language R is used extensively, and while no prior knowledge of R is assumed, students lacking programming skills should consider some basic training (e.g. https://swirlstats.com/) in advance. This is a postgraduate course that moves at a postgraduate pace, with each week building on those preceding it. Students should be prepared to invest consistent effort, and those with thinner backgrounds will have to work to keep pace in the first few weeks.


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:
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.

6.00 0.12500
Domestic fee paying students
Year Fee
2024 $4440
International fee paying students
Year Fee
2024 $6360
Note: Please note that fee information is for current year only.

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.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7608 22 Jul 2024 29 Jul 2024 31 Aug 2024 25 Oct 2024 In Person View

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