This course will teach all students a common baseline for writing of scientific essays, proposals and papers, oral presentation skills; introduction to the statistical analysis environment R, which is rapidly taking over as the most versatile programme for biological applications; Advanced Generallized Linear Mixed models (working in the R environment), which is critical for analysis of complex datasets; and Experimental design and power analysis, both in terms of being able to plan their own experiments but also being able to critically evaluate the validity of conclusions drawn from data analysed in published papers.

This course will be taught over term 1, with all sessions consisting of a mixture of lectures and hands-on practicals, mostly involving computer exercises. There is a strong emphasis on self-directed learning at the Master's level. Staff are there to guide your learning, but you will require self-motivation and completion of work outside of class to excel in the course. The course is intensive, with sessions every weekday for 6 weeks, plus a final assignment that will take two weeks to complete; each session will involve some preparation time and homework or assignments. We thus strongly discourage planning external work, especially during the initial 6 weeks of the programme. There are about 100 hours of contact time scheduled; most sessions will have a mixture of lectures and interactive components; while lectures will be recorded, we strongly encourage you to participate in the interactive sessions. You are expected to devote a total of ~400 hours for a 40-credit course (including contact hours and formative work outside of class). A rough guideline is that you should plan on approximately 2 hours to prepare for each session and 2 hours for revisions. The remaining time should be devoted to self-study, including preparation of take-home assignments. You should allocate two weeks for the final assignment because it will involve reviewing and applying what you have learned in the entire course. The schedule includes 2.5 weeks for revising the statistics component and for time dedicated to working on the final assignment; this should not be viewed as a holiday.  Contact hours are high in this course to enhance supervised learning of core skills.

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Summative Assessment will be divided into five components: 1) practical skills assessment for scientific communication (15%); 2) practical skills assessment for R (20%); 3) MOODLE test for statistics (10%); 4) engagement mark for submitting formative assessment assignments (10%); and 5) a final written paper that integrates knowledge and skills gained across scientific communication, R and statistics (45%).

The aims of this course are to ensure that all students enrolled in the MSc/PGdip and MRES programmes receive advanced and evidence-based training in the key skills essential for any modern ecology/evolution/epidemiology-based research career and for the courses that they will take later in the programme. All sessions will involve practical hands-on training, as well as lectures introducing the concepts. Sessions are divided broadly into: 1) Scientific Communication and 2) R & Statistics (including Introduction to the Programming Environment R, Experimental Design & Power Analysis, Introduction to General Linear Models, and Advanced Statistics).

By the end of this course students will be able to:

■ Carry out an appropriate and thorough search of the primary literature.

■ Critique scientific evidence

■ Produce well-structured and critical evidence based essays, grant proposals and scientific reports that set the context of the objectives based on a critical review of the primary literature, and clearly describe methodology (including quantitative analyses), present results in an easily understandable format, and discuss results in the context of the broader body of literature in the relevant scientific field

■ Download and install R, along with packages and libraries relevant to the analysis of biological data, import data, use objects, and plot data, acquiring technical help as required from literature and online sources

■ Critically discuss appropriate uses of some of the key features of R including random number generation, data manipulation, input output, and basic descriptive statistics.

■ Use R to implement a wide range of generalised linear mixed models, and discuss critically the justification for choice of models for particular scientific questions

■ Organize data in a form appropriate for further analysis

■ Use the evidence base to formulate null and alternate hypotheses associated with particular statistical tests

■ Critically interpret the output from these analyses, test identified hypotheses and discuss the results in the context of the primary literature

■ Recognize and critically assess the underlying models associated with these statistical analyses

■ Identify and interpret statistical interactions and random effects in the context of real data

■ Conduct a full range of diagnostic tests to ensure the data complies with assumptions of the methodology

■ Take an critical evidence-based approach to designing effective experiments (and other data collection exercises)

■ Critically evaluate other scientists' experimental designs

■ Critically discuss the key concepts in experimental design with reference to the literature

■ Integrate knowledge and skills learned in the analysis of experimental data and scientific writing to write a report using real data to generate a specific hypothesis to be tested in the context of a critique of the existing background in the primary literature, describe the specific methods used to analyse the data, describe and interpret the results based on the evidence base and write a critical discussion that sets the results in the context of the primary literature

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