• Class Number 3804
  • Term Code 3430
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Prof Robert Lanfear
  • Class Dates
  • Class Start Date 19/02/2024
  • Class End Date 24/05/2024
  • Census Date 05/04/2024
  • Last Date to Enrol 26/02/2024
SELT Survey Results

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.

Learning Outcomes

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

  1. Evaluate the strengths, weakness, and uses, and abuses of large observational datasets in biology.
  2. Use R to read, analyse, and visualise large biological datasets.
  3. Write clear, concise, and reproducible reports for different audiences, with informative visualisations, using R and GitHub.
  4. Generate and test new hypotheses using observational datasets.
  5. Provide and respond to clear and actionable peer feedback.
  6. Use online resources to develop new data science skills.

Research-Led Teaching

Research in biology and data science drives this entire course. The first half of the courses focusses on developing the skills and expertise required to do data science, using applied large datasets from recently published research to motivate many of the sessions. The second half of the course focusses on exploring biological datasets and problems.

Additional Course Costs

None. All textbook referred to in the course are free online.

Examination Material or equipment

No examination. A laptop computer with R, R Studio, and internet access will be required for the in-class quizzes. In-class quizzes will all occur in the scheduled tutorial or workshop slots.

Required Resources

You'll need a modern laptop with internet access, ANU VPN connectivity, and RStudio installed. The laptop's battery should be good enough for at least 3 hours of work, and you should bring a charger just in case.

Whether you are on campus or studying online, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Intro and R basics Quiz 1
2 Exploratory Data Analysis Quiz 2
3 Data Visualisation Quiz 3;
4 Version Control Quiz 4;
5 Programming better Quiz 5; short report details released
6 Data Wrangling Short report 1 hand in
7 Applied topics 1 Short report 2 hand in
8 Applied topics 2 Short report 3 hand in
9 Applied topics 3
10 Final project 1
11 Final project 2 Final project hand in
12 Final project 3 Final project poster session

Tutorial Registration

Tutorials are run as a single class with all students. Registration is not necessary. Attendance is required. Tutorial time is on the timetable.

Assessment Summary

Assessment task Value Learning Outcomes
Technical quizzes 30 % 1,2,3,4
Short Data Reports 30 % 1,2,3,4,5
Final Project 40 % 1,2,3,4,5,6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


It is expected that all students participate in tutorials and workshops. Because both tutorials and workshops are central to the course, attendance and participation is a minimum expectation. Tutorials and workshops will be delivered in the classroom.

Assessment Task 1

Value: 30 %
Learning Outcomes: 1,2,3,4

Technical quizzes

Each quiz will be 25 minutes with 10-20 questions. Quizzes will occur in weeks 1-5. Each quiz will cover the material in the relevant week of the course, and will contain a collection of questions that gradually increase in difficulty. You must be present at the start of the quiz to receive a grade.

The quizzes combined make up 30% of your final grade and are weighted equally, thus each of the 5 quizzes is worth 6% of your final grade.

Assessment Task 2

Value: 30 %
Learning Outcomes: 1,2,3,4,5

Short Data Reports

Short reports are designed for you to hone your abilities in different areas (e.g. GitHub, Data Wrangling, Data Visualisation).

Details of these assessments will be released to you in week 5

The total weight of the short reports is 30% and each is worth 10% of your final grade. Each short report is accompanied by an oral assessment where you discuss your work in person with a tutor or the convenor.

Barring any unforeseen circumstances, your marks will be returned to you within two weeks of the submission deadline.

Assessment Task 3

Value: 40 %
Learning Outcomes: 1,2,3,4,5,6

Final Project

The final project is worth 40% of your final grade. The final project will be released in Week 7, and will be due at the end of semester.

You will be asked to use your data science skills to develop your own questions from the primary literature, and then to explore and analyse a relevant biological dataset to answer those questions. You will be asked to write this up as a report in R markdown using GitHub to version control your work. Finally, you will be asked to make a poster of your work, and to discuss that poster in the final session of the semester.

Barring any unforseen circumstances, your marks will be returned to you within two weeks of the submission deadline.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof.
  • Late submission is not accepted for in-class quizzes. If you have documented and appropriate medical evidence that demonstrates you were not able to attend a quiz then you may request a similar quiz on the same topic.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Returning Assignments

Assignments will be marked and returned on Wattle via Turnitin

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

No resubmission of assignments is allowed.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Prof Robert Lanfear

Research Interests

Phylogenetics, molecular evolution, developmental biology, evolution

Prof Robert Lanfear

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions