BIOSCI 738 : Advanced Biological Data Analysis


2023 Semester One (1233) (15 POINTS)

Course Prescription

Building on a strong foundation in quantitative biology, fundamental statistical methods and basic R programming, students will learn an array of advanced biostatistical methods for data analysis. Topics covered include: data wrangling, methods for the analysis of designed experiments, regression analysis, including mixed effect models, and the analysis of multivariate data, including advanced supervised and unsupervised learning techniques. Requires students to apply their knowledge across a myriad of complex biological datasets.

Course Overview

This is a postgraduate course geared towards students of biology, ecology, and environmental science. It is suited to students with an interest in (bio)statistics who would like to equip themselves with the know-how to be able to correctly prepare experiments, analyse data, interpret their results and draw valid conclusions. 
The statistical concepts and methods taught in this course will provide students with the tools to make and evaluate scientific discoveries as well as propose and justify decisions based on data. The course builds on assumed knowledge of some fundamental statistical concepts. It is expected that students are comfortable with the statistical content covered in a typical core (bio)statistics course (e.g., linear regression, hypothesis testing etc.). 
This course will use the programming language R (through RStudio) and students are expected to be familiar with data import, manipulation, and visualisation using R. If students are unfamiliar with R it is expected that students will prepare accordingly before the semester begins. The course will also introduce students to version control (via git and GitHub); no previous experience with these systems is expected.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Capability 6: Social and Environmental Responsibilities
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Discuss and critically evaluate the provenance of data and create informative visualisations. (Capability 1, 2, 4, 5 and 6)
  2. Develop and demonstrate effective R programming and version control skills. Create and maintain a reproducible project directory. (Capability 1, 2 and 3)
  3. Describe, analyse and interpret different types of experimental designs identifying the potential sources of variation. Formulate an appropriate hypothesis associated with an experimental design. (Capability 1, 2 and 3)
  4. Perform, interpret, and critique multivariate data techniques. (Capability 1, 2 and 3)
  5. Communicate statistical concepts and experimental outcomes clearly using language appropriate for both a scientific and non-scientific audience. (Capability 1, 4 and 5)
  6. Perform, interpret, and critique appropriate statistical regression techniques. (Capability 1, 2 and 3)


Assessment Type Percentage Classification
Assignments 60% Peer Coursework
Laboratories 5% Individual Coursework
Presentations 10% Individual Coursework
Project 25% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
  • Students must attend and complete all workshops. Students who fail to attend at least 7 out of the 10 workshops will receive a DNC grade.
  • Students must deliver the final presentation on their project to complete the course. Failure to do so will result in a DNC grade.
  • Students must complete the peer review coursework component of the assignments to be fully awarded their assignment grade.


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Key Topics

Taught material will be typically delivered, each week, via pre-recorded lectures. Each 2-hour lecture will focus on a mixture of group work and practical tasks that build on the pre-recorded lecture material. Each week there will also be a 2-hour practical lab focused on building computational skills. A list of topics and concepts covered in this course is given below.

Module 1
  • Data sovereignty
  • Data visualisation and wrangling
  • Reproducibility and version control
Module 2
  • Multiple comparison procedures (e.g., pairwise, and multiple, comparisons of means)
  • Resampling procedures (e.g., randomisation, permutation, and bootstrapping)
  • Introduction to linear regression with continuous and categorical explanatory variables
Module 3
  • Design and analysis of experiments
  • Linear regression cont.
Module 4
  • Mixed models (e.g., incorporating fixed and random effects)
  • Introduction to generalised linear models
Module 5
  • Unsupervised and supervised learning (e.g., principal components analysis, dimension reduction,  discriminant analysis)
  • Ordination (e.g., multidimensional scaling, correspondence analysis)
Module 6
  • Least squares estimation
  • Maximum likelihood estimation
  • Introduction to Bayesian statistics

Special Requirements

  • Students must attend and complete all workshops. Students who fail to attend at least 7 out of the 10 workshops will receive a DNC grade.
  • Students must deliver the final presentation on their project to complete the course. Failure to do so will result in a DNC grade.
  • Students must complete the peer review coursework component of the assignments to be fully awarded their assignment grade.

Workload Expectations

This course is a standard 15-point course, which represents approximately 150 hours of study. A typical semester including the study/exam period totals approximately 15 weeks. However, this course is delivered from week two of the semester due to field trips and has no associated exam, therefore students should expect the weekly commitment to be a little higher throughout the core semester.

For this 15-point course, you should expect to commit 45 hours to the in-person delivery of the course. You can also reasonably expect to commit approximately 105 hours to independent learning. This may include watching and reviewing pre-recoded material, additional reading, face-to-face and/or online discussion, writing and assignment completion etc.

Delivery Mode

Campus Experience

Attendance is required at all scheduled activities to complete and receive credit for components of the course. The face-to-face activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

  • If you are new to R or just want to refresh your skills try one or more of the Introductory R Tutorials listed here
  • Available as an e-book: Modern Statistics for Modern Biology, Susan Holmes, Wolfgang Huber, 2018.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

The assessment format of this course has been updated this year: there is no longer an exam and all assessment is coursework based. 

Other Information

Students will have access to R, RStudio, and git in university computing laboratories but are strongly encouraged to download and install R, RStudio, and git on their own devices.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 01/11/2022 09:36 a.m.