Courses & Units

Modelling Biological Data KSM304

Introduction

Quantitative skills are fundamental to many areas of biology and ecology. With massive increases in the availability of ecological and other kinds of biological data, skill in developing models to describe and understand relationships in data and extrapolate beyond existing data, and an understanding of the nuances of particular kinds of data, is fundamental in modern biological research. This unit builds on the foundation provided in Quantitative Methods in Biology to familiarise students with more advanced model building techniques including generalised linear, mixed and additive models, regression trees, bootstrapping methods, and non-linear regression. The unit will also introduce time series analysis, Bayesian approaches to modelling data, and simulation models including explicitly spatial and individual-based models. It will equip students wanting to understand and/or pursue modern and relevant research in biology and ecology.

Summary

Unit name	Modelling Biological Data
Unit code	KSM304
Credit points	12.5
College/School	College of Sciences and Engineering Institute for Marine & Antarctic Studies
Discipline	College Office - CSE\|Ecology and Biodiversity
Coordinator	Doctor Sophie Bestley
Available as an elective?	Yes
Delivered By	University of Tasmania
Level	Advanced

Availability

This unit is currently unavailable.

* 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 2022 are indicative and subject to change. Finalised census dates for 2022 will be available from the 1st October 2021. Note census date cutoff is 11.59pm AEST (AEDT during October to March).

About Census Dates

Learning Outcomes

Aquire practical knowledge of approaches to statistical modelling of biological and ecological data, and key skills in the operation of statistical software packages.
Develop the ability to construct statistical models that are fit for purpose, and critically evaluate alternative models in the context of a physical problem.
Demonstrate the ability to verbally and graphically communicate the results of statistical analyses clearly and accurately to both technical and non-technical audiences using globally adopted notations and conventions.
Understand the role of statistical modelling of biological and ecological data in a range of contexts and applications, and that the application and interpretation of statistical modelling may have important social and ethical implications.

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
			not applicable

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 UConnect 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.

Requisites

Prerequisites

KSM309 or KMA253

Teaching

Teaching Pattern	1 semester, 2 x 1 hr lectures, 1 x tutorial and 3 hr practical weekly (13 weeks)
Assessment	Exam (30%)\|Assignments 1-4 (60%)\|Bayesian Scripting (10%)
Timetable	View the lecture timetable \| View the full unit timetable

Textbooks

Required	There is no compulsory reading for the unit. For some topics supplementary reading material with be provided in addition to the lecture slides, for those that seek a deeper understanding of the material.
Recommended	Note that no single text covers all the material in this course, but the books listed below may be of use as additional references. The others will available for short-term loan from the lecturers. Maindonald, J. and Braun, W.J. (2010). Data analysis and graphics using R. Cambridge University Press Mattiopoulos, J. (2011). How to be a quantitative ecologist. Wiley, McElreath, R. (2015) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press. Zuur, A.F, Ieno, E.N, Smith, G.M. (2007). Analysing ecological data. Springer.
Links	Booktopia textbook finder

Required

There is no compulsory reading for the unit. For some topics supplementary reading material with be provided in addition to the lecture slides, for those that seek a deeper understanding of the material.

Recommended

Note that no single text covers all the material in this course, but the books listed below may be of use as additional references. The others will available for short-term loan from the lecturers.

Maindonald, J. and Braun, W.J. (2010). Data analysis and graphics using R. Cambridge University Press
Mattiopoulos, J. (2011). How to be a quantitative ecologist. Wiley,
McElreath, R. (2015) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press.
Zuur, A.F, Ieno, E.N, Smith, G.M. (2007). Analysing ecological data. Springer.

Links

Booktopia textbook finder

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