Quantitative Methods in Marine and Antarctic Science unit (KSM721)

Hobart

Introduction

Quantitative skills are fundamental tools for marine and Antarctic scientists. They are necessary to design studies, analyse data, and to assess and interpret published studies. This unit provides a solid grounding in appropriate ways to collect and analyse and present data in formats that are likely to be encountered by marine and Antarctic scientists. The unit emphasises hands-on, practical experience with widely used statistical software and addresses the common problems often encountered in dealing with biological, ecological and socioeconomic data. There is close integration of the lecture and practical components of the unit. The unit covers basic sampling and experimental design, data analysis using standard techniques, hypothesis testing and progresses on to developing models to describe and understand relationships in data and extrapolate beyond existing data.The unit will equip students wanting to understand and/or pursue research in marine and Antarctic science.

Summary

Unit name	Quantitative Methods in Marine and Antarctic Science
Unit code	KSM721
Credit points	12.5
College/School	Sciences and Engineering Institute for Marine & Antarctic Studies
Discipline	Ecology and Biodiversity|Fisheries and Aquaculture|Oceans Ice and Climate
Coordinator	Associate Professor Christopher Brown
Delivered By	University of Tasmania
Level	Postgraduate

Availability

Location	Study period	Attendance options		Available to
Hobart	Semester 1

Note

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Key Dates

Study Period	Start date	Census date	WW date	End date
Semester 1	23/02/2026	17/03/2026	20/04/2026	14/06/2026

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).

Unit census dates currently displaying for 2026 are indicative and subject to change. Finalised census dates for 2026 will be available from the 1st October 2025. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

• Compare and contrast robust experimental design practise to help design and analyse future projects
• Analyse biological, ecological and socioeconomic data using basic techniques through R gui.
• Interpret the results of statistical analyses using plots and basic written summaries.
• Present the results from statistical analyses to both technical and non-technical audiences.

Fee Information

Field of Education	Commencing Student Contribution 1,3	Grandfathered Student Contribution 1,3	Approved Pathway Course Student Contribution 2,3	Domestic Full Fee 4
010907	$1,192.00	$1,192.00	not applicable	$2,862.00

• Available as a Commonwealth Supported Place
• HECS-HELP is available on this unit, depending on your eligibility³
• FEE-HELP is available on this unit, depending on your eligibility⁴

¹ Please refer to more information on student contribution amounts.
² Please refer to more information on eligibility and Approved Pathway courses.
³ Please refer to more information on eligibility for HECS-HELP.
⁴ Please refer to more information on eligibility for FEE-HELP.

If you have any questions in relation to the fees, please contact UniConnect or more information is available on StudyAssist.

Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.

Teaching

Teaching Pattern	Lectures (maximum of 2 hr total per week, ~20 mins each component) Lectures will be pre-recorded weekly and available via MyLO.
Assessment	Quizzes (15%)|Linear Modelling (25%)|Major Assignment (25%)|Oral examination (35%)
Timetable	View the lecture timetable | View the full unit timetable

Textbooks

Required	The reading material will include htmls of prac code and outputs which students may wish to print. Scientific papers for seminars and assignments.
Recommended	The recommended texts are: 1. Quinn & Keough (2023) Experimental design and data analysis for biologists. second edition. Cambridge Press, UK. 2. Wickham, Cetinkaya-Rundel, Grolemund (2023) R for Data Science. O'Reilly Press. https://r4ds.hadley.nz/

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.