Key Information

Overview

This is a postgraduate course geared towards students of biology, ecology, and environmental science. Building on a strong foundation in quantitative biology, fundamental statistical methods and basic R programming, you 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.

Prerequisites

BIOSCI738 is a postgraduate course and so is pitched at a level where some fundamentals of R and stats are already assumed (e.g., topics covered in BIOSCI220). Topics (e.g., linear regression, hypothesis testing etc.) are recapped in the material, but the pace is quite a bit quicker than for a UG course (as we do then delve further into them). It is expected that you will do quite a bit of further reading/working through problems in your own time. How much extra time this takes up will depend on what baseline knowledge you come into the course with. We will use the programming language R throughout the course (through RStudio) and you are expected to be familiar with data import, manipulation, and visualization. If you are unfamiliar with R it is expected that you will prepare accordingly before the semester begins. If you find yourself struggling a lot with basic R tasks then you’ll likely have to spend a lot more time tacking the questions etc. The provided materials will definitely help you here, but you’d likely find that you’d need to recap & cover them in more detail than we might cover in the lectures/labs.

These tasks are designed for you to self-assess your R and statistics knowledge. If you fail to complete the coding task and score less than 70% on the quiz you will likely struggle with the pace and content of the course. This does not preclude you from taking the course, however, you should expect to invest time on top of what is expected for a postgraduate course into ensuring that you understand the material. If you feel the need to brush up on your R skills here are two resources I’d recommend giving a whorl 1) RStudio Education and 2) R for Data Science. There are plenty of others out there so if these don’t suit you then I’d strongly encourage you to try some others until you find something that suits your learning style.

Lectures

Lectures are weekly (weeks 2–12)
  • Mondays: 9–11 am (Sci Maths & Physics, Room B05), and
  • Fridays: 2–4 pm (B201, Room 3429).

You are expected to attend all lectures and to bring along your own device (BYOD) with the appropriate software installed. Each 2-hour lecture will involve a mixture of group work and practical tasks that focus on building computational and inference skills.

Office hours

I hold weekly consultation hours during the semester:

  • Mondays & Tuesdays 12—1pm, &
  • Fridays 1—2pm.

These are held in my office in building 303, room 318. If you are unable to make these times just email me @ c.jonestodd@auckland.ac.nz and we can sort something else out.

Assessment summary

Overview of assessment timeline

Course policies

Academic honesty

TLDR: Don’t cheat!

You are expected to abide by the following as you work on assignments in this course:

  • All submitted work must be your own. Copying verbatim from sources (e.g., internet, or AI-generated content, the courseguide) without proper citation will be treated as plagiarism.
  • You may discuss assignments with other students; however, you may not directly share (or copy) code or write up with other students. Unauthorized sharing (or copying) of the code or write up will be considered a violation for all students involved.
  • Unless explicitly stated otherwise you may make use of online resources (e.g., StackOverflow or Copilot etc.) for coding examples on assignments. If you directly use code from an outside source (or use it as inspiration), you must explicitly cite where you obtained the code. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

Extensions

Note that extensions will only be granted in exceptional circumstances (e.g., illness with a doctor’s note). Last-minute requests due to poor time management, workload from other courses, or minor inconveniences will not be considered sufficient to grant an extension. The goal is to keep everyone on track together;due dates for assignments are there to help you keep up with the course material and to ensure the I can provide feedback within a timely manner.

No single assessment in this course is worth more than 15% of your final grade. This is by design so that 1) no one assignment should feel overwhelming, and 2) you have multiple opportunities to demonstrate your learning in a variety of ways (see Assessment summary for a breakdown of assessment design). Because of this, deadlines for other courses or a busy schedule are not valid reasons for extensions. You’re given plenty of time to complete each assignment, so I encourage you to start early and plan ahead! If you’re facing a genuine issue that will prevent you from submitting your work it is imperative that you get in contact with me as soon as possible so that we might make alternative arrangements and figure out the best way to support you.

FAQs

What are some recommended resources to help me on my R learning journey? Some to start out with: 1) RStudio Education, 2) R for Data Science, and 3) Modern Statistics for Modern Biology.

How do I receive communications and updates on assignments for this course? All course communication will be via Canvas Announcement, I expect you to keep up to date with these!

Do I need to attend all lectures? Yes. I expect students to attend all lectures. You should only miss class in cases of emergencies or serious illness. Otherwise, I expect you to be there, just as you’d expect me to be!

Are the classes recorded? No, all lectures are in-person and will not be recorded.

What if I miss a lecture? The expectation is that you catch up on the materials and content yourself before the next session.

Do I need to being a laptop to class? Yes, please bring along your own device (BYOD) with the appropriate software installed as each lecture will likely involve practical tasks that focus on building computational and inference skills.

I don’t have my own laptop. No worries, please let me know and I can organise a loan laptop for you.

Can I use AI tools in this class? It depends! We will discuss this in more detail when lectures start. Please refer to the Academic honesty section with regards to submitted work.

I need an extension for an assignment, how do I do that? Please read the Extensions section.

When will I get my assignment grades? Feedback and solutions will be provided as soon as possible and uploaded via CANVAS. Be sure to review your feedback carefully and take some time to reattempt the tasks in line with the solutions as often there are multiple ways of doing the same thing!

What if I have a query about my assignment grade? Firstly, please read your feedback carefully and take some time to reattempt the tasks in line with the solutions. If you still have concerns, please email me @ c.jonestodd@auckland.ac.nz with a clear explanation as to what specific part of the grade or feedback you are querying.

How can I get help if I’m struggling with the content? Reach out! Ask me questions in class, attend my Office hours, or email me @ c.jonestodd@auckland.ac.nz. You are also welcome to drop by my office unnanounced and check if I’m free, but please note I may be in a meeting or working from home some days.

Any other queries or concerns? Please contact me @ c.jonestodd@auckland.ac.nz

Support

The Te Papa Manaaki | Campus Care team provides a safe, confidential and free service that supports the health, well-being and safety of everyone at the University.

Several crisis helplines are available if you are worried about your safety or the safety of someone else, including the Mental Health Crisis Service (Phone: 0800 800 717). Rather text? Need to talk @ 1737

There are many personal, academic and learning, financial (including emergency funds), and tech support services available to all students—learn more here.

Whai Ora Be Well offers a range of tools and information to help you care for your physical, emotional and spiritual wellbeing, so you can thrive.