Applied Data Analysis
| Module title | Applied Data Analysis |
|---|---|
| Module code | LESM005 |
| Academic year | 2025/6 |
| Credits | 15 |
| Module staff | Dr Erik Postma (Lecturer) Professor Mario Recker (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 12 |
| Number students taking module (anticipated) | 30 |
|---|
Module description
Biological, ecological and environmental data are complicated. Experiments, surveys and databases provide opportunities to test hypotheses through deduction or inference. The inexperienced data analyst is faced with choices of how to handle data; how to match data collected on different scales of space or time; how to describe hypotheses as statistical models; how to test for significance or credibility; how to measure goodness of fit. Modern analysts are also faced with choices of statistical philosophy and algorithms for analysis. This module uses a series of lectures, practical work and discussion sessions to guide you through modern statistical philosophies and methods. The main software platform for the module is ‘R’, which is powerful, flexible and free. By the end of the module, you will understand how to handle and analyse data, interpret results of statistical models and provide graphical summaries. A core concept of the module will be the simulation of data that matches the assumptions of statistical models. However, throughout the module you will use real datasets related to cutting edge research in ecology, evolutionary biology and environmental science.
Module aims - intentions of the module
Data analysis is an integral part of all quantitative research. This module provides key transferable skills in data handling, statistical modelling and programming. More generally, it will promote quantitative and logical thinking alongside computing skills.
A variety of data analysis approaches and techniques will be taught using a mixture of lectures and computer exercises, using the ‘R’ programming language and software environment. Using a combination of real and simulated data, the module will emphasise the possibilities and limitations of the various statistical approaches, without losing sight of their real-world application, and the importance of careful experimental design, survey and data collection.
Applied Data Analysis will provide information on the use of classical hypothetico-deductive tests of significance; permutation tests; information-theoretic approaches and Bayesian algorithms for data analysis. You will also learn about online, open platforms for the sharing of data and algorithms for analysis.
Skills in data analysis are in high demand in the biological and environmental sectors, ranking as #1 employment skills on LinkedIn and valued by the UK research councils as essential skills for employment in research.
The content of this module is inspired directly by research at the cutting edge of ecology, evolutionary biology and environmental sciences. Content will evolve as the modern field of applied data analysis evolves, and you will be involved in discussions of data analyses used in recent scientific papers.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Discuss, with a scientific vocabulary, the philosophy of statistical analysis in research
- 2. Debate the relative merits of different analyses to test relevant hypotheses
- 3. Analyse and interpret the results of analyses
- 4. Criticise, and adapt, statistical models to cope with atypical error structures and non-independence
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 5. Communicate knowledge and understanding in ecology, evolution and environmental sciences
- 6. Describe and critically evaluate aspects of research and communication with reference to reviews and research articles
- 7. With limited guidance, deploy established techniques of analysis and enquiry in scientific endeavour
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 8. Communicate ideas effectively and professionally by written, oral and visual means
- 9. Study autonomously and undertake projects with minimum guidance
- 10. Select and properly manage information drawn from books, journals, and the internet
- 11. Interact effectively in a group
Syllabus plan
The module will be delivered using a hybrid approach of pre-recorded and in-person lectures and face-to-face Q&A sessions. Students complete practicals independently, with help available online and during regular in-person help sessions. Each practical is concluded by a face-to-face discussion session.
Topic 1: Principles of statistical modelling: Lectures on null-hypothesis testing, p-values and their limitations, statistical power, different types of data, mean and variance, the normal distribution. Computer practical introducing the R statistical software platform.
Topic 2: Basic statistical tests: Lectures on correlation, regression, t-test and ANOVA, including interpretation, significance testing, model diagnostics, and multiple testing. Computer practical focussing on their practical implementation, as well as data handling and plotting.
Topic 3: Linear models, including continuous and categorical predictors and their interactions: Lectures on interpretation of model output, significance testing, model simplification and selection, prediction, model diagnostics. Computer practical focussing on their practical implementation in R and the reporting of results.
Topic 4: Generalised linear models: Lecture on Poisson and binomial error structures and link functions, significance testing, overdispersion and prediction. Computer practical on fitting generalised linear models and visualising results in R
Topic 5: Mixed models: Lectures on non-independence and pseudo-replication, fixed versus random effects, interpretation of model output, significance testing. Computer practical on the practical implementation of mixed models and dealing with non-independence in R.
Topic 6: Advanced statistical modelling tools: Lectures on resampling methods, the concept of likelihood and its use in statistical inference, and Bayesian data analysis. Computer practicals on the practical implementation of these concepts and methods in R and interpretation of results.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 53 | 97 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching activities | 10 | Lecture Q&A sessions |
| Guided independent study | 10 | Pre-recorded lectures |
| Scheduled Learning and Teaching activities | 10 | Practical Q&A session |
| Scheduled Learning and Teaching activities | 3 | In-person lecture |
| Scheduled Learning and Teaching activities | 14 | Help and review session |
| Scheduled Learning and Teaching activities | 6 | Face-to-face computer practicals |
| Guided independent study | 97 | Engage with practical material, additional research and reading, preparation for module assessment |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Short-answer questions during Q&A sessions | Ongoing throughout the module | 1-11 | Oral |
| Lecture tasks available on ELE | Made available throughout the module | 1-11 | Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 50 | 50 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Short-answer questions during practical sessions | 10 | ELE quiz | 1-8 | Written |
| Statistical modelling problem sheet | 40 | Question sheet | 1-8 | Written |
| IShort-answer test | 50 | Time (1h) ELE quiz | 1-8 | Written |
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 |
|---|---|---|---|
| Short-answer questions during practical sessions | Written short-answer questions | 1-8 | Referral/Deferral period |
| Statistical modelling problem sheet | Statistical modelling problem sheet | 1-8 | Referral/Deferral period |
| Short-answer test | ITimed (1h) ELE quiz | 1-8 | Referral/Deferral 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 50%) you will be required to redo the original assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 50%.
Indicative learning resources - Basic reading
- Beckerman, Childs, Petchey (2017) Getting started with R. Oxford.
- David Spiegelhalter (2020) The Art of Statistics. Penguin Books (also available as PDF)
- Wickham & Grolemund (2017) R for Data Science. O’Reilly (also available as free online version)
Indicative learning resources - Web based and electronic resources
Indicative learning resources - Other resources
- Class contributions to web forum (peer support).
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 02/03/2018 |
| Last revision date | 15/09/2025 |
