BIOSCI 220 : Quantitative Biology

Science

2020 Semester One (1203) (15 POINTS)

Course Prescription

Almost every biological discipline will require computational and analytical skills beyond using point-and-click software to enable the processing of biological data into biological information. Students will learn fundamentals of experimental design, data management, and data visualisation. Additionally, students will gain the skills required to critically analyse and interpret biological experiments, understanding how statistics can be both used and misused in the scientific literature. Recommended preparation: STATS 101

Course Overview

To facilitate accessibility and flexibility in your timetable, lectures are provided as online recordings. The amount of lecture material will vary on a week to week basis. 

Laboratories are two hours long, and the material is designed to be completed within this time. Lab materials will generally be available before the labs, and you may attempt them before coming to class. At the end of each two hour laboratory session is a tutorial session, where the lecturers and TAs will be available to answer any questions, including non-laboratory related questions. Tutorial sessions are not recorded, and are not assessable. Each tutorial session is open to any student from any stream.

Attendance at the laboratories or tutorials is NOT mandatory, however access to the computer laboratory cannot be guaranteed outside of timetabled hours. Please also note that because there are no formal lectures or tutorials in this subject, the labs and tutorials are often your only opportunity for one-on-one interaction with the lecturers and teaching assistants.  

Course Requirements

Prerequisite: BIOSCI 101, and 30 points from BIOSCI 106-109

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 5: Independence and Integrity
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate literacy and proficiency in fundamental computing and programming concepts. (Capability 1)
  2. Understand and apply concepts in experimental design, hypothesis testing, data exploration and analysis, and data visualization. (Capability 1 and 3)
  3. Demonstrate literacy in the use and misuse of graphs and statistics in scientific literature. (Capability 1 and 5)
  4. Demonstrate proficiency and critical thinking in the interpretation of graphs and statistics. (Capability 2)

Assessments

Assessment Type Percentage Classification
Laboratories 60% Individual Coursework
Final Exam 30% Individual Examination
Quizzes 10% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4
Laboratories
Final Exam
Quizzes
Students will be required to pass the final exam in order to pass the course. 

Learning Resources

There is no prescribed text book for this course. Reading material will be provided to students as required. 

Special Requirements

Students are able to apply for swipe card access to the SBS computer lab which will permit them entry during standard University hours. 

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 ~12-24 hours (~1-2 hr per week) of lecture material, 12 hours (~1 hr per week) tutorial, 24 hours (~2 hr per week) laboratories, and 58-70 hours (~5-6 hr per week) of independent study (reading and thinking about the content and assignments).

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.

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.

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.

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 at 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.

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.

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.

Published on 12/02/2020 08:12 p.m.