This course provides an overview of skills needed for reproducible research and open science using the statistical programming language R. Students will learn about data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows.

1 x 2 hour lecture per week for 10 weeks

Assessment will be a lab portfolio consisting of reproducible reports demonstrating class skills taught in the weeks previous to the assessment. The specific content will be independent of the specific content of the formative exercises.

This course aims to teach students the basic principles of reproducible research and to provide practical training in data processing and analysis in the statistical programming language R.

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

■ Draw on a range of specialised skills and techniques to inform and critique various kinds of questions in psychology and neuroscience

■ Write scripts in R to organise and transform data sets using best accepted practices;

■ Explain basics of probability and its role in statistical inference;

■ Critically analyse data and report descriptive and inferential statistics in a reproducible manner.

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