This course will introduce students to the principles and best practices of programming reproducibly for biological data analysis, prediction, and validation in R.

This course will consist of 13 sessions last 4-5 hours each, supplemented with additional help sessions.

None

The course is structured into three thematic series, each culminating in a practical assessment that directly addresses the intended learning outcomes.

For each practical assessment, students will submit annotated R scripts and a written report:

■ Practical 1 (10%): Focuses on foundational data types and programming structures, and basic reporting. (ILO 1 and ILO 2)

■ Practical 2 (20%): Builds on prior skills with more complex program design, introduces common algorithms for statistical identification and advanced data structures, with enhanced reporting and evaluation of model outputs to address typical biological data analysis problems. (ILO 2 and ILO 3)

■ Practical 3 (20%): Integrates all prior concepts and introduces conservation, spatial, and movement ecological data analysis. (ILO 1, ILO 3, and ILO 4)

The remaining 50% of the final grade will be based on an independent assignment that integrates elements from the practicals, to be completed after the last day of class. This assignment will require students to independently apply all four learning outcomes by designing, implementing, annotating, and reporting on a novel biological problem. (ILO 1, ILO 2, ILO 3, and ILO 4)

The aim of this course is to provide hands-on training in programming techniques; to write comprehensible and reproducible programmes that can be understood by other people who examine it; and to apply those skills to advanced data analysis using the R programming language.

With reference to the evidence base, by the end of this course students will be able to:

1. Select, design, and justify the appropriate program structures when solving a problem

2. Design simple computer programs to solve specified problems

3. Demonstrating best practicing in R coding utilising data structures, code annotation, and documentation

4. Generate reports in R and evaluate the output.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.