Centre for Research Informatics Training
Events
Training events organised and delivered by the Cambridge Centre for Research Informatics Training.
Core statistics
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Places available
Classical statistical analysis techniques including hypothesis testing, linear models and power analyses, with an emphasis on practical implementation and robust analysis skills.
Data analysis in R
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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.
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.