Applied Data Analysis

Module titleApplied Data Analysis
Module codeLESM005
Academic year2019/0
Credits15
Module staff

Professor Dave Hodgson (Convenor)

Duration: Term123
Duration: Weeks

2

Number students taking module (anticipated)

30

Description - summary of the module content

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

Syllabus plan

Topic 1: Linear regression, ten ways. An introduction to the variety of methods for understanding patterns in data.

Topic 2: Simulating data that satisfy assumptions of structure and distribution. Analysing these data to clarify the concepts of significance, goodness of fit and statistical power.

Topic 3: General linear modelling and mixed effects modelling with Normal error structures.

Topic 4: Challenging assumptions of Normality.

Topic 5: Challenging assumptions of independence of data.

Statistics clinics and research seminars will take place throughout the module.

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
301200

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching10Lectures on statistical and quantitative methods
Scheduled Learning and Teaching5Help and review sessions
Scheduled Learning and Teaching15Computer practical sessions
Guided independent study120Additional research and reading, and preparation for module assessments

Assessment

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Short-answer questions during lectures and practical sessionsOngoing throughout the moduleAllOral

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
60400

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Statistical modelling problem sheets 60Question sheets submitted end of week 11-11Written
Test402 hours1-7Online

Re-assessment

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Statistical modelling problem sheets Statistical modelling problem sheets 1-11August assessment period
TestTest1-7August assessment 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%.

Resources

Indicative learning resources - Basic reading

  • Beckerman, Childs, Petchey (2017) Getting started with R. Oxford.

Indicative learning resources - Web based and electronic resources

Module has an active ELE page

Indicative learning resources - Other resources

  • Class contributions to web forum (peer support).           

Key words search

Statistics, ‘R’ software, simulation, replication, independence, general linear modelling, mixed effects modelling, regression, information theory, Bayesian analysis, statistical power

Credit value15
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

08/03/2018