Last modified: 5 Days, 17 Hours, 58 Minutes ago
QABC will teach students contemporary analytical skills to allow them to tackle current and future quantitative problems in conservation and restoration. These skills will also be transferable beyond biology under the broad umbrella of “data science”.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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Through a stepped approach students will gain vital skills in data manipulation, analysis and visualisation, gain skills to analyse modern biodiversity questions such as “will this species flexibly respond to climate change?” and “what new environments would be most suitable for this species?”.
Along with learning how to apply key approaches, students will handle datasets of varying sizes and appreciate their unique challenges, such as fitting complex models to small datasets and how to handle “big data”.
Each topic will be taught using case studies drawn across diverse ecosystems, from terrestrial to freshwater and marine. We will use a variety of data sources such as Aberdeen’s long-term dolphin dataset, download data from the online animal social network repository, and generate our own datasets using state-of-the-art simulation approaches.
Students will gain experience working in R and sample other programming languages including Python. Skills developed in this course will be exceptionally transferable to related disciplines and any “data science” context.
Each week will feature an interactive lecture introducing a biological problem, and how we need a particular analytical approach to solve it. There is then a computer workshop when students are guided through an application of the method, with opportunities to pursue their own interests with optional follow-ons.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 55 | |
|---|---|---|---|---|
| Assessment Weeks | 41 | Feedback Weeks | 44 | |
| Feedback |
Stakeholder report. Students will perform an analysis of a provided dataset for an imaginary stakeholder and report their findings including conservation recommendations. The report will justify the approach taken, describe the application in accessible language, and provide results supported by up to 3 figures or tables. They will express the degree of certainty and notes on key issues in accessible language for stakeholder, showing understanding of the stakeholder’s priorities. Written feedback will assess the approach chosen, the quality of the reporting, and how well complex terms are communicated to the stakeholder. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand advanced statistical concepts such as random effects, relational data, principal component analysis, and simulations |
| Procedural | Apply | Apply statistical concepts to extract genuine insight from complex datasets |
| Procedural | Evaluate | Critically evaluate the use of statistical tools in existing case studies and identify issues and solutions |
| Reflection | Create | Combine skills developed in course in new ways to creatively analyse an existing dataset, allowing student to generate novel findings, and reflect on appropriateness and reliability of own findings |
| Assessment Type | Summative | Weighting | 35 | |
|---|---|---|---|---|
| Assessment Weeks | 32 | Feedback Weeks | 35 | |
| Feedback |
1,000-word dataset analysis. Students will answer important questions in ecological/conservation science using existing big online datasets. They may take any approach they deem suitable. They will produce a report with a Methods, Results, and Conclusions sections, with a max of 2 figures or tables. Written feedback will focus on the appropriateness of the approach they have used and how well it is reported. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand advanced statistical concepts such as random effects, relational data, principal component analysis, and simulations |
| Procedural | Apply | Apply statistical concepts to extract genuine insight from complex datasets |
| Reflection | Create | Combine skills developed in course in new ways to creatively analyse an existing dataset, allowing student to generate novel findings, and reflect on appropriateness and reliability of own findings |
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | 29 | Feedback Weeks | 32 | |
| Feedback |
800-word peer review. Students will review a piece of reported analysis using random effects models, listing strengths and weaknesses of the approach taken. Written feedback will confirm they have assessed key assumptions of the approach and explained accurately. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand advanced statistical concepts such as random effects, relational data, principal component analysis, and simulations |
| Procedural | Evaluate | Critically evaluate the use of statistical tools in existing case studies and identify issues and solutions |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Any passed elements are carried forward. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand advanced statistical concepts such as random effects, relational data, principal component analysis, and simulations |
| Procedural | Apply | Apply statistical concepts to extract genuine insight from complex datasets |
| Procedural | Evaluate | Critically evaluate the use of statistical tools in existing case studies and identify issues and solutions |
| Reflection | Create | Combine skills developed in course in new ways to creatively analyse an existing dataset, allowing student to generate novel findings, and reflect on appropriateness and reliability of own findings |
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