Centre for Research Informatics Training
Events
Training events organised and delivered by the Cambridge Centre for Research Informatics Training.
Core statistics
Bookable Register interest
Places available
Classical statistical analysis techniques including hypothesis testing, linear models and power analyses, with an emphasis on practical implementation and robust analysis skills.
AI prompting for biologists
Bookable Register interest
Places available
An introductory course on AI chatbots for biologists, covering what they are, how they work, and how to use prompting effectively in research.
Data analysis in R
Bookable Register interest
Places available
Practical introduction to data analysis and visualisation in R, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and packages.
Fundamentals of Python Programming
Bookable Register interest
Places available
Beginner-friendly introduction to programming with Python, covering core programming concepts and practical problem-solving skills through hands-on exercises. Participants learn how to write simple programs, work with data and control structures, and build a foundation for further computational and research applications.
Introduction to the Unix command line
Bookable Register interest
Places available
Practical introduction to the Unix command line for researchers working with computational tools and large datasets. Participants learn core command-line skills for navigating filesystems, manipulating text-based data and building efficient workflows for computational research and data analysis.
Survival (time-to-event) analysis
Bookable Register interest
Places available
Applied use of survival analysis techniques for analysing time-to-event data, with an emphasis on experimental design, interpretation and model quality, and visualisation.
Single-cell RNA-seq analysis
Bookable Register interest
Places available
Practical introduction to the analysis of single-cell RNA sequencing data, covering preprocessing, quality control, dimensionality reduction, data integration, clustering, and differential analysis using R/Bioconductor.
Working on HPC clusters
Register interest
Practical introduction to High Performance Computing (HPC) systems for researchers working with computationally intensive analyses and large datasets. Participants learn how to access remote systems, manage files, run jobs using SLURM and develop scalable workflows for computational research.
Extracting biological information from gene lists
Bookable Register interest
Places available
Practical introduction to gene set enrichment analysis, covering functional annotation resources, statistical methods for enrichment testing, web-based tools, and programmatic analysis of gene lists in R.
Scientific Figure Design
Bookable Register interest
Places available
Practical introduction to designing clear, effective and publication-ready scientific figures. Participants learn transferable principles of visual communication to improve figure clarity, accessibility and impact across publications, presentations and other research outputs.
Bulk RNA-seq analysis
Register interest
Practical introduction to bulk RNA sequencing data analysis, covering quality control, quantification of gene expression, exploratory analysis, differential expression, visualisation, and functional interpretation of results using R and Bioconductor.
Working on HPC clusters
Register interest
Practical introduction to High Performance Computing (HPC) systems for researchers working with computationally intensive analyses and large datasets. Participants learn how to access remote systems, manage files, run jobs using SLURM and develop scalable workflows for computational research.
Quality Control in Sequencing Experiments
Bookable Register interest
Places available
Practical introduction to quality control for short-read sequencing experiments, focusing on the interpretation of quality metrics and reports generated by commonly used quality assessment tools.
Introduction to the Unix command line
Register interest
Practical introduction to the Unix command line for researchers working with computational tools and large datasets. Participants learn core command-line skills for navigating filesystems, manipulating text-based data and building efficient workflows for computational research and data analysis.
Fundamentals of Python Programming
Register interest
Beginner-friendly introduction to programming with Python, covering core programming concepts and practical problem-solving skills through hands-on exercises. Participants learn how to write simple programs, work with data and control structures, and build a foundation for further computational and research applications.
Generalised linear models
Bookable Register interest
Places available
Practical application of generalised linear models for analysing binary, proportional, and count data, with an emphasis on interpretation, diagnostics, and robust analysis skills.
Data analysis in R
Register interest
Practical introduction to data analysis and visualisation in R, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and packages.
Protein Structure Prediction and Analysis
Bookable Register interest
Places available
Practical introduction to computational protein structure prediction, covering structure prediction with AlphaFold, model evaluation, visualisation, multimer prediction, ligand binding site prediction, and molecular docking.
Linear mixed effects models
Bookable Register interest
Places available
Applied use of linear mixed effects models for analysing clustered and hierarchical data, with an emphasis on interpretation, model structure, and robust analysis skills using R.
Data analysis in Python
Bookable Register interest
Places available
Practical introduction to data analysis and visualisation in Python, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and libraries.
Single-cell RNA-seq analysis
Register interest
Practical introduction to the analysis of single-cell RNA sequencing data, covering preprocessing, quality control, dimensionality reduction, data integration, clustering, and differential analysis using R/Bioconductor.
Expression proteomics analysis in R
Register interest
Practical introduction to the analysis of expression proteomics data in R and Bioconductor, covering data import, quality control, visualisation, differential abundance analysis, and biological interpretation of results.
Data analysis in R
Register interest
Practical introduction to data analysis and visualisation in R, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and packages.
Introduction to the Unix command line
Register interest
Practical introduction to the Unix command line for researchers working with computational tools and large datasets. Participants learn core command-line skills for navigating filesystems, manipulating text-based data and building efficient workflows for computational research and data analysis.
Working on HPC clusters
Register interest
Practical introduction to High Performance Computing (HPC) systems for researchers working with computationally intensive analyses and large datasets. Participants learn how to access remote systems, manage files, run jobs using SLURM and develop scalable workflows for computational research.
Data analysis in Python
Register interest
Practical introduction to data analysis and visualisation in Python, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and libraries.
Core statistics
Register interest
Classical statistical analysis techniques including hypothesis testing, linear models and power analyses, with an emphasis on practical implementation and robust analysis skills.
Bulk RNA-seq analysis
Register interest
Practical introduction to bulk RNA sequencing data analysis, covering quality control, quantification of gene expression, exploratory analysis, differential expression, visualisation, and functional interpretation of results using R and Bioconductor.
Quality Control in Sequencing Experiments
Register interest
Practical introduction to quality control for short-read sequencing experiments, focusing on the interpretation of quality metrics and reports generated by commonly used quality assessment tools.
Extracting biological information from gene lists
Register interest
Practical introduction to gene set enrichment analysis, covering functional annotation resources, statistical methods for enrichment testing, web-based tools, and programmatic analysis of gene lists in R.
Linear mixed effects models
Register interest
Applied use of linear mixed effects models for analysing clustered and hierarchical data, with an emphasis on interpretation, model structure, and robust analysis skills using R.
Generalised linear models
Register interest
Practical application of generalised linear models for analysing binary, proportional, and count data, with an emphasis on interpretation, diagnostics, and robust analysis skills.
Data analysis in R
Register interest
Practical introduction to data analysis and visualisation in R, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and packages.
Protein Structure Prediction and Analysis
Register interest
Practical introduction to computational protein structure prediction, covering structure prediction with AlphaFold, model evaluation, visualisation, multimer prediction, ligand binding site prediction, and molecular docking.
Survival (time-to-event) analysis
Register interest
Applied use of survival analysis techniques for analysing time-to-event data, with an emphasis on experimental design, interpretation and model quality, and visualisation.
Data analysis in Python
Register interest
Practical introduction to data analysis and visualisation in Python, covering core skills for working with scientific datasets through exploratory analysis, data manipulation and reproducible workflows. Participants learn to inspect, transform and analyse data, and create clear visualisations using widely used open-source tools and libraries.
Introduction to the Unix command line
Register interest
Practical introduction to the Unix command line for researchers working with computational tools and large datasets. Participants learn core command-line skills for navigating filesystems, manipulating text-based data and building efficient workflows for computational research and data analysis.
Scientific Figure Design
Register interest
Practical introduction to designing clear, effective and publication-ready scientific figures. Participants learn transferable principles of visual communication to improve figure clarity, accessibility and impact across publications, presentations and other research outputs.
Working on HPC clusters
Register interest
Practical introduction to High Performance Computing (HPC) systems for researchers working with computationally intensive analyses and large datasets. Participants learn how to access remote systems, manage files, run jobs using SLURM and develop scalable workflows for computational research.
Single-cell RNA-seq analysis
Register interest
Practical introduction to the analysis of single-cell RNA sequencing data, covering preprocessing, quality control, dimensionality reduction, data integration, clustering, and differential analysis using R/Bioconductor.
Foundations of phylogenetic inference
Register interest
Practical introduction to molecular phylogenetics, covering sequence alignment, phylogenetic tree reconstruction, maximum likelihood and Bayesian inference methods, and interpretation of evolutionary relationships.
Bulk RNA-seq analysis
Register interest
Practical introduction to bulk RNA sequencing data analysis, covering quality control, quantification of gene expression, exploratory analysis, differential expression, visualisation, and functional interpretation of results using R and Bioconductor.
Core statistics
Register interest
Classical statistical analysis techniques including hypothesis testing, linear models and power analyses, with an emphasis on practical implementation and robust analysis skills.
Working with bacterial genomes
Register interest
Practical introduction to bacterial genomics analysis using Illumina sequencing data, covering genome assembly, annotation, phylogenetic analysis, strain typing, and antimicrobial resistance detection using standardised workflows and community tools.
Metagenomics data analysis
Register interest
Practical introduction to metagenomics approaches for studying complex microbial communities, covering amplicon sequencing, shotgun and Hi-C metagenomics, quality control, assembly, and downstream analysis and interpretation of metagenomic data.