Biological Data Science in R

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Identify relevant statistical procedures for different types of hypotheses and data.
• Interpret the outputs of statistical tests performed in R, and use that interpretation to justify the conclusions you draw.
• Understand the fundamental concepts of biological data, including data formats and common data visualisation techniques.
• Build and critique statistical analyses from integrated workflows that responsibly leverage modern Artificial Intelligence tools.

The course will start by introducing data management and organisation, building confidence in student use through the responsible use of Artificial Intelligence to develop analytical workflows in the R Environment for Statistical and Graphical Computing. Discussions will explore the ethical considerations in leveraging these tools. The focus on developing reproducible research skills promotes transparency, accountability, efficiency, rigour and reliability of data analysis.

Various types of statistical analysis will be revised through that workflow process including parametric and non-parametric tests, correlation, regression, Analysis of Variance and Analysis of Covariance.

The final block of content will expand these tools to a Generalised Linear Mixed Model framework that provides more flexibility in dealing with the nature of many real-world datasets. In-class practicals will recreate analyses published in peer-reviewed manuscripts.

Students will be taught how to analyse data using the analysis in student-led workshops that facilitate peer-to-peer learning. These lessons will be reinforced in “no agenda” interactive feedback sessions and through one-to-one surgeries to assist with the particulars of data sets for programme research projects.

Formal lectures will provide the framework of core concepts and issues, some of which will be pre-recorded and should be viewed prior to discussion sessions.

Online and in-person tutorial discussions driven by student interactions, but with numerous formative assessments to monitor progress.

In-person computer workshops to provide hands-on experience and personalised feedback implementing concepts from lectures and tutorials.

Panopto Lecture capture as a revision aid to assist in independent study.

Summative

This is how we’ll formally assess what you have learned in this module.