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BI5012: ECOLOGICAL DATA ANALYSIS USING R (2023-2024)

Last modified: 05 Oct 2023 08:46


Course Overview

This course is uniquely tailored for ecologists and will provide students with the required background theory and practical skills relevant to modern ecology. Our example-led lectures and real-world based practical sessions will provide you with a foundation to become confident and proficient in analysing real data. Throughout this course, we will introduce you to using the programming language R to implement modern statistical modelling techniques. You will use the flexible linear and generalised linear modelling frameworks to analyse ecological data with an emphasis on robust and reproducible statistical methods.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr A Douglas

What courses & programmes must have been taken before this course?

  • One of Any Postgraduate Programme (Studied) or BI4015 Grant Proposal (Passed) or BI4515 Grant Proposal - Semester 2 (Passed)
  • One of Master Of Science In Marine Conservation or MSc Applied Marine and Fisheries Ecology or Master Of Science In Ecology & Conservation or Master Of Science In Environmental Pollution And Remediation or Master Of Science In Environmental Management or MSc Environmental Science or MSci Biological Sciences

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

This course will be divided into themed weeks during which you will gain a foundational understanding of statistical theory through example-led lectures and practical skills by completing computer-based exercises.

Week 1: You are introduced to concepts of statistical inference, uncertainty and using R and RStudio for reproducible data analysis.
Week 2: You will learn about the process of analysing ecological data and are introduced to data exploration and visualisation in R using real-world data.
Week 3: During this week you will learn about the theory and practice of fitting simple linear models in R. You will also learn how to validate and interpret linear models.
Week 4: You will learn how to extend the linear modelling framework and apply it to more complex models and data. You will also learn how to compare different plausible models and select the most informative model.
Week 5: During this week, you will learn how to extend the linear modelling framework to fit generalised linear models (GLMs) to analyse different types of data. Specifically, you will learn how to model discrete count data with a Poisson GLM.
Week 6: In this week you will further extend the GLM framework to fit models to binary (0/1) data with a binomial GLM.

 


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2023 for 1st half-session courses and 22 December 2023 for 2nd half-session courses.

Summative Assessments

2x in-class graded practicals (20% each)

Assessment Type Summative Weighting 40
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Duration of practicals: 75 minutes and 90 minutes

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Report: Individual

Assessment Type Summative Weighting 60
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Data analysis report

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Formative Assessment

There are no assessments for this course.

Resit Assessments

Resubmission of failed elements

Assessment Type Summative Weighting
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
FactualRememberILO’s for this course are available in the course guide.

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