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.

Research-Led Teaching

This course builds quantitative and computational skills that are core prerequisites to the prosecution of modern biological research. Key aspects include the development of statistical thinking, the translation of a biological research problem into a cognate data science challenge and the building of skills to address that challenge. Most assignments will use authentic data from biological research projects. The summative project with involve the replication of published results from published data.

Required Resources

Students must have access to a laptop computer and are expected to have it with them in class.

Recommended Resources

Recommended student system requirements 

ANU courses commonly use a number of online resources and activities including:

• video material, similar to YouTube, 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 photo/scan for handwritten work
• home-based assessment.

To fully participate in ANU learning, students need:

• A computer or laptop. Mobile devices may work well but in some situations a computer/laptop may be more appropriate.
• Webcam
• Speakers and a microphone (e.g. headset)
• Reliable, stable internet connection. Broadband recommended. If using a mobile network or wi-fi then check performance is adequate.
• Suitable location with minimal interruptions and adequate privacy for classes and assessments.
• Printing, and photo/scanning equipment

For more information please see https://www.anu.edu.au/students/systems/recommended-student-system-requirements

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.

Other Information

This course is graded as CRS/NCN and the Final Project is a hurdle assessment. To receive course credit, students must receive an overall passing grade as well as a passing grade on the Final Project.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	R Bootcamp
2	R Bootcamp	Assignment 1
3	The role of statistics in biology	Assignment 2
4	Statistical Inference: evidence and hypothesis testing	Assignment 3
5	Statistical inference: linear models	Assignment 4
6	Statistical modelling: mixed models	Assignment 5
7	Experimental design
8	Generalised linear models
9	Final project topics: power analysis and error rates
10	Final project topics: Simulating from more complex models
11	Final project topics: as required
12	Final project presentations	Student presentations and final project report due

Tutorial Registration

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.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Assignment 1: Using R for data analysis	10 %	09/08/2024	16/08/2024	1,2,3,4,5,6
Assignment 2: Data simulation	10 %	16/08/2024	23/08/2024	1,2,3,4,5,6
Assignment 3: Statistical inference using ANOVA	10 %	23/08/2024	30/08/2024	1,2,3,4,5,6
Assignment 4: Generate data from an experiment	10 %	01/09/2024	06/09/2024	1,2,3,4,5,6
Assignment 5: Analyse the data	10 %	20/09/2024	28/09/2024	1,2,3,4,5,6
Final Project	50 %	25/10/2024	*	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

Policies

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:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

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.

Participation

This course is run as a workshop and attendance is mandatory and will be recorded.

Assessment Task 1

Assignment 1: Using R for data analysis

This assignment will be completed in two parts. Students will be required to demonstrate use in R and R Markdown for data wrangling and visualisation. 

Assessment Task 2

Assignment 2: Data simulation

Report and R code showing simulation of data inspired by a real experiment.

Assessment Task 3

Assignment 3: Statistical inference using ANOVA

Report and R code showing analysis of data using ANOVA.

Assessment Task 4

Assignment 4: Generate data from an experiment

Report and R code showing simulation of a more complicated experiment.

Assessment Task 5

Assignment 5: Analyse the data

Report and R code showing data exploration and analysis of a dataset. Analysis extended to include linear mixed models.

Assessment Task 6

Final Project

A major assignment including designing a study, simulating data and analysis. Students will need to turn in draft components of the report to check progress. Students will give in class presentations in week 12 with the final report due at the end of semester. THIS IS A HURDLE ASSESSMENT AND MUST BE PASSED TO PASS THE CLASS.

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

Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

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. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Assignments will be returned online through Wattle.

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

A resubmission may be granted by the convenor provided the first submission represents a genuine attempt at the assignment. The goal is to master the material and the instructors welcome repeated efforts when required.

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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students