BIOSCI 738 : Advanced Biological Data Analysis


2021 Semester One (1213) (15 POINTS)

Course Prescription

Design and analysis of experiments for both field and bench scientists. Methods for the analysis of designed experiments, including analysis of variance with fixed, random and mixed effects; also, regression analysis and analysis of covariance. Methods for the analysis of multivariate datasets such as cluster analysis, principal components analysis, multidimensional scaling, and randomisation methods. There will be a practical component to this course involving the use of appropriate statistical software.

Course Overview

This is a postgraduate course in statistical methods geared towards the needs of students of biology, ecology, and environmental science. Whether heading to research or industry, it is imperative that biology students have the statistical and computational skills to apply and interpret fundamental statistical concepts and analyses to assess and critique their experiments and other data.

This course is suited to students with an interest in (bio)statistics who would like to equip themselves with the tools and 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. Some basic statistical knowledg is assumed. 

This course will use the programming language R; R is a free software environment for statistical computing and graphics. Students are strongly encouraged to use R through the freely available IDE (integrated development environment) RStudio. Students will have access to R and RStudio in university computing laboratories, but are also encouraged to download and install R and RStudio on their own devices.

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. Create and communicate informative visualisations of data using the R programming language. (Capability 1, 4, 5 and 6)
  2. Design an effective experiment adhering to the fundamental principals of experimental desigh (randomisation, replication, and blocking). (Capability 1 and 2)
  3. Analyse and interpret multivariate data using an appropriate method (e.g., Principal Component Analysis, Discriminant analysis, Principal Coordinates Analysis, Muldimendional scaling, Cluster analysis). (Capability 1, 2 and 3)
  4. Perform and interpret different resampling techniques to carry out hypothesis testing and construct confidence intervals (e.g., randomisation tests, permutation tests, bootstrapping). Students should be able to write R code to perform each technique. (Capability 1, 2, 3 and 4)
  5. Communicate statistical concepts and experimental outcomes clearly using language appropriate for both a scientific and non-scientific audience. (Capability 1, 4, 5 and 6)
  6. Perform, interpret, and critique appropriate statistical regression techniques. Students should be able to write R code to fit each model. (Capability 1, 2, 3 and 4)


Assessment Type Percentage Classification
Final Exam 40% Individual Examination
Workshops 5% Individual Coursework
Assignments 55% Peer Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Final Exam

There will be the option to complete additional assignments for extra credit (capped at 5% in total).


For more information and to find contact details for the School of Biological Sciences Tuākana coordinator, please see

Key Topics

Taught material will be delivered, each week, via 2 hour lectures. Each week there will also be a 1 hour practical  workshop focused on the material covered in the lecture. A list of topics and concepts covert in this course is given below.

Exploratory Data Analysis and Communication
  • Data wrangling 
  • Data visualisation
Experimental Design and Statistical Inference
  • Introduction to design and analysis of experiments
  • Comparison procedures: pairwise comparisons of means, one-way ANOVA
  • Multiple comparison procedures (controlling errors in hypothesis testing) 
  • Multiple regression with continuous and categorical explanatory variables
  • Mixed models; incorporating fixed and random effects
  • Resampling procedures: randomisation, permutation, and bootstrapping
Multivariate Analysis
  • Cluster analysis
  • Unsupervised learning: principal components analysis, dimension reduction
  • Ordination: multidimensional scaling, correspondence analysis
  • Supervised learning: discriminant  analysis
  • Networks and graphs

Special Requirements

  • Students must attend at least 7 out of the 10 workshops. Students who fail to attend at least 7 out of the 10 workshops will receive a DNC grade.
  • Students must complete and submit at least 7 out of the 10 workshops to be awarded the possible 5% of the final grade.
  • Students must complete  the peer review coursework component of the assignments to be awarded the associated grade (up to a possible 5%).

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 2 hours of lectures, a 1 hour practical workshop, 3 hours of reading and thinking about the content and approx 4 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

A remote version of the course can be made available to students located overseas because of border restrictions, or those with an exemption to study remotely.

  • Although designed as a Campus Experience this course is also available for remote students.
  • Attendance is required at least 7 of the 10 at scheduled workshops to complete the course.
  • Lectures will be available as recordings. Other learning activities including workshops will not be available as recordings.
  • The course will not include live online events (e.g., group discussions/tutorials).
  • Attendance on campus is required for the exam, unless enrolled as a remote student.
  • The activities for the course are scheduled as a standard weekly timetable: 2 hour lectures and 1 hour workshops each week.

Learning Resources

  • 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 on short loan: STATISTICAL METHODS IN BIOLOGY: Design and Analysis of Experiments and  Regression, Welham, Gezan, Clark & Mead, 2014.
  • 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.

Digital 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.

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.


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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

  • Level 1: Delivered normally as specified in delivery mode.
  • Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option. The following activities will also have an on-campus/in-person option: lectures, workshops.
  • Level 3/4: All teaching activities and assessments are delivered remotely.

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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 07/01/2021 10:34 p.m.