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Overview

Course Prescription

Advanced biostatistical techniques, including data visualisation, experimental analysis methods, regression analysis (e.g., mixed-effects models) and multivariate data analysis (e.g., supervised and unsupervised learning techniques) are taught and applied to a myriad of biological datasets. Students entering this course are expected to have a firm grounding in quantitative biology, basic statistical methods and R programming. Recommended preparation: BIOSCI 220.

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.
The concepts taught will build on BIOSCI220 (or equivalent), so students are expected to be comfortable with the content covered in this course. 

Key Topics

Each 2-hour lecture will focus on a mixture of group work and practical tasks that focus on building computational and inference 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

Course Contacts

Course Director: Charlotte Jones-Todd (c.jonestodd@auckland.ac.nz)

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; as this course has no associated exam 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.

Locations and Semesters Offered

LocationSemester
City

Teaching and Learning

Campus Experience

Attendance is required at all scheduled activities to complete and receive credit for components of the course.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including 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.

Additional Information on 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 https://education.rstudio.com/learn/beginner/
  • Available as an e-book: Modern Statistics for Modern Biology, Susan Holmes, Wolfgang Huber, 2018. https://web.stanford.edu/class/bios221/book/introduction.html

Copyright

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.

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.

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 as these software will often be used in classroom activities. 

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations 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.


Assessment and Learning Outcomes

Special Requirements

Compulsory participation in all timetabled sessions.

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3
4
5
6

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Assessment to CLO Mapping

Assessment Type123456

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions. Your feedback helps teachers to improve the course and its delivery for future students. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

Based on feedback from students the assessment schedule has been modified.

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.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

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.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work 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.

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.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.