R for data science
Date: 21 - 24 November 2023
Timezone: London
Duration: 4 Days
A 4-day intensive course that builds on the content from the course R for beginners, providing an excellent toolbox for multidisciplinary scientists in areas of life sciences and biomedicine (particularly oriented to omics datasets). The course covers a wide range of topics from programming concepts to data normalisation, functional enrichment or introduction to machine learning. All learning objectives are advertised in the web. The course is designed to be undertaken at the student’s pace with 1-1 support available during the course and online support available up to two months post-course. All delegates are invited to join a peer-peer support community upon completion of the course to enhance their research/analytical work. This course runs normally twice a year around April and November.
Contact: Dr Eva Caamano-Gutierrez: [email protected]
Keywords: R Programming, R language, R Studio, Data Analysis, Data Science, Statistics, Programming, Research, Computational biology, Bioinformatics
Prerequisites:
Delegates must have prior knowledge of R and there is a focus on learning programming concepts that allow delegates to speed up and increase the reproducibility of their analyses, as well as build custom functions. There is also plenty of hands on exercises to cover the research pipelines from normalisation to functional enrichment.
Learning objectives:
- To write your own custom functions.
- To use for loops, apply functions and if statements.
- To analyse large matrices of data in a semi-automated way.
- To normalise data.
- To quantify and correct batch effects.
- To undertake the most common clustering algorithms including k-means and hierarchical.
- To perform variable selection and present these results in different plots including heatmaps.
- To do 2-way ANOVA.
- To undertake multivariate modelling.
- A brief introduction to machine learning.
- A brief introduction to functional enrichment with R using the package clusterProfiler.
Organizer: Computational Biology Facility, University of Liverpool: https://www.liverpool.ac.uk/computational-biology-facility/
Host institutions: University of Liverpool
Eligibility:
- Registration of interest
Target audience: PhD Students, Masters students, PostDocs, ECR, Academics, Researcher in life sciences
Tech requirements:
Access to a moderately powerful computer with R and RStudio installed.
Cost basis: Cost incurred by all
Activity log
