Analysis of Biological Data
| Module title | Analysis of Biological Data |
|---|---|
| Module code | BIO2426 |
| Academic year | 2021/2 |
| Credits | 15 |
| Module staff | Dr Kelly Moyes (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 235 |
|---|
Module description
An understanding of the analysis of biological data is an essential tool in biological research. During this module you will become familiar with the World’s most powerful, flexible and free (!) statistical analysis software. You will work through example problems during lectures and practical sessions, learn how to choose and apply a variety of analytical approaches and learn how to design experiments to maximise the chance of making discoveries.
Module aims - intentions of the module
The aim of this module is to provide training in the collection and analysis of biological datasets, recognising that statistics is a tool for understanding data and not an end in itself. The module is based around understanding the logic behind statistical tests and applying them using the open-access statistics program ‘R’. We use an interactive approach working through examples of analyses using R. The module aims to provide you with early training for the outputs expected from your research projects, particularly in the final year.
You will gain knowledge in and experience of the modern scientific method as applied in ecology and evolution, including hypothesis formulation, experimental design and modern techniques for collecting and analysing data. We use real research data that you collect in our practicals, and discuss examples from contemporary biological research in lectures. Data analysis is becoming an increasingly important aspect of the running of modern businesses of all types. An understanding of the framework within which we can ask questions using data is an important skill for a growing range of careers, and competence with R is an increasingly valuable asset.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Discuss the modern scientific process, demonstrating an appreciation of the inherent constraints on the generation of objective knowledge via subjective experience
- 2. Formulate testable hypotheses about ecological processes given practical limitations on data collection
- 3. Recognise, contrast and apply methods of data collection and analysis, including non-parametric and parametric methods, recognising the assumptions and limitations of these approaches
- 4. Use the statistics program R to conduct a range of analyses, and to produce simple plots in order to visualise data prior to more quantitative analyses
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 5. Describe in some detail essential facts and theory across a sub-discipline of biosciences
- 6. Identify critical questions from the literature and synthesise research-informed examples from the literature into written work
- 7. With some guidance, deploy established techniques of analysis, practical investigation, and enquiry within biosciences
- 8. Describe and evaluate approaches to our understanding of biosciences with reference to primary literature, reviews and research articles
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 9. Develop, with some guidance, a logical and reasoned argument with valid conclusions
- 10. Communicate ideas, principles and theories fluently using a variety of formats in a manner appropriate to the intended audience
- 11. Collect and interpret appropriate data and complete research-like tasks, drawing on a range of sources, with limited guidance
- 12. Evaluate own strengths and weaknesses in relation to professional and practical skills, and apply own evaluation criteria
- 13. Reflect effectively on learning experiences and summarise personal achievements
Syllabus plan
Lectures will include topics such as:
- the philosophy of science and experimental design
- types of data and summary statistics
- entering and analysing data in R
- testing assumptions and hypotheses in R
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 27 | 123 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching | 15 | Lectures including material covering the language used in R, data import, manipulating data, ANOVA, correlation, regression and graphics |
| Scheduled learning and teaching | 12 | Computer practical sessions training in the use of R programming language and statistical tests (4 x 3 hours) |
| Guided independent study | 123 | Additional reading, research and preparation for module assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Short answer questions during the lectures and practical sessions | Ongoing throughout the module | All | Oral |
| MCQ statistics tests | 20 problems | 1-12 | Written individual feedback on all questions |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 40 | 60 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Class test | 60 | 2 hours | 1-12 | Written individual feedback on all questions |
| Data analysis 1 | 10 | ~10 problems | 1-12 | Written individual feedback on all questions |
| Data analysis 2 | 15 | ~10 problems | 1-12 | Written individual feedback on all questions |
| Data analysis 3 | 15 | ~10 problems | 1-12 | Written individual feedback on all questions |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Class test | Examination | 1-12 | August examination period |
| Data analyses | Data analyses | 1-12 | August examination period |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to sit a further examination. The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 40%.
Indicative learning resources - Basic reading
- Dytham, C. 2010. Choosing and using statistics: a biologist's guide. Wiley-Blackwell.
- Crawley, M.J. 2005. Statistics: An introduction using R. Wiley.
Indicative learning resources - Web based and electronic resources
- ELE page: http://vle.exeter.ac.uk/course/view.php?id=330 (includes 7 sets of downloadable help sheets designed for this module)
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 01/10/2011 |
| Last revision date | 19/08/2020 |
