BIOSCI 738 : Advanced Biological Data Analysis
Science
2022 Semester One (1223) (15 POINTS)
Course Prescription
Course Overview
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 |
Learning Outcomes
- Discuss and critically evaluate the provenance of data and create informative visualisations. (Capability 1, 4, 5 and 6)
- Develop and demonstrate effective R programming and version control skills. Create and maintain a reproducible project directory. (Capability 1, 3, 5 and 6)
- 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, 3 and 6)
- Perform, interpret, and critique multivariate data techniques. (Capability 1, 2, 3 and 4)
- Communicate statistical concepts and experimental outcomes clearly using language appropriate for both a scientific and non-scientific audience. (Capability 1, 4, 5 and 6)
- Perform, interpret, and critique appropriate statistical regression techniques. (Capability 1, 2, 3 and 4)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 60% | Peer Coursework |
| Laboratories | 5% | Individual Coursework |
| Presentations | 10% | Individual Coursework |
| Project | 25% | Individual Coursework |
| 4 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Assignments | ||||||||||
| Laboratories | ||||||||||
| Presentations | ||||||||||
| Project | ||||||||||
- 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 awarded their assignment grade.
Key Topics
- Data sovereignty
- Data visualisation and wrangling
- Reproducibility and version control
- 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
- Design and analysis of experiments
- Linear regression cont.
- Mixed models (e.g., incorporating fixed and random effects)
- Introduction to generalised linear models
- Unsupervised and supervised learning (e.g., principal components analysis, dimension reduction, discriminant analysis)
- Ordination (e.g., multidimensional scaling, correspondence analysis)
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 awarded their assignment grade.
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 lab, 4 hours of reading and thinking about the content and 3 hours of work on assignments and/or test preparation.
Delivery Mode
Campus Experience
Attendance is required at scheduled activities including labs to complete and receive credit for components of the course.
The course may include live online events including group discussions.
The 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 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
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.
Other Information
Students will have access to R and RStudio 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.
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.
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 http://disability.auckland.ac.nz
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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.
Disclaimer
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.