Research Informatics Training 2025-26
(Mon 15 Sep 2025 - Mon 22 Jun 2026)
September 2025
| Mon 15 |
Data analysis in R (IN-PERSON)
Finished
The R programming language is one of the leading languages in the field of data science. It’s widely used for data visualisation, analyses, statistics and machine learning. It is open-source software and all the software we use during the course is free. This course is aimed to provide an introduction to R, focussing on exploratory data analysis and visualisation. By the end of this course you will be able to read in, analyse, interpret and visualise data.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Tue 16 |
Data analysis in R (IN-PERSON)
Finished
The R programming language is one of the leading languages in the field of data science. It’s widely used for data visualisation, analyses, statistics and machine learning. It is open-source software and all the software we use during the course is free. This course is aimed to provide an introduction to R, focussing on exploratory data analysis and visualisation. By the end of this course you will be able to read in, analyse, interpret and visualise data.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Wed 17 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Thu 18 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Fri 19 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Mon 22 |
Data analysis in Python (IN-PERSON)
Finished
The Python programming language is one of the leading languages in the field of data science. It’s widely used for data visualisation, analyses, statistics and machine learning. It is open-source software and all the software we use during the course is free. This course is aimed to provide an introduction to Python, focussing on exploratory data analysis and visualisation. By the end of this course you will be able to read in, analyse, interpret and visualise data.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Tue 23 |
Data analysis in Python (IN-PERSON)
Finished
The Python programming language is one of the leading languages in the field of data science. It’s widely used for data visualisation, analyses, statistics and machine learning. It is open-source software and all the software we use during the course is free. This course is aimed to provide an introduction to Python, focussing on exploratory data analysis and visualisation. By the end of this course you will be able to read in, analyse, interpret and visualise data.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Wed 24 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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The Unix shell (command line) is a powerful and essential tool for modern researchers, especially in computational fields such as bioinformatics and large-scale data analysis. In this course, you will:
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Thu 25 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Fri 26 |
This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences. There are three core goals for this course:
R is an open source programming language so all of the software we will use in the course is free. In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory. After the course you should feel confident to be able to select and implement common statistical techniques using R and moreover know when, and when not, to apply these techniques.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Tue 30 |
Generalised linear models are the kind of models we would use if we had to deal with non-continuous response variables. For example, this happens if you have count data or a binary outcome. This course aims to introduce generalised linear models, using the R software environment. Similar to Core statistics this course addresses the practical aspects of using these models, so you can explore real-life issues in the biological sciences. The Generalised linear models course builds heavily on the knowledge gained in the core statistics sessions, which means that the Core statistics course is a firm prerequisite for joining. There are several aims to this course: 1. Be able to distinguish between linear models and generalised linear models 2. Analyse binary outcome and count data using R 3. Critically assess model fit R is an open-source programming language so all of the software we will use in the course is free. We will be using the R Studio interface throughout the course. Most of the code will be focussed around the tidyverse and tidymodels packages, so a basic understanding of the tidyverse syntax is essential. If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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October 2025
| Wed 1 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Thu 2 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Fri 3 |
Recent technological advances have made it possible to obtain genome-wide transcriptome data from single cells using high-throughput sequencing. This course offers an introduction to single-cell RNA sequencing (scRNA-seq) analysis. Participants will gain hands-on experience with key software packages and methodologies for processing, analyzing, and interpreting scRNA-seq data. Key topics include data preprocessing, quality control, normalization, dimensionality reduction, batch correction and data integration, cell clustering and differential expression and abundance analysis. By the end of the course, students will be equipped with the skills to independently conduct and critically analyse data from scRNA-seq experiments.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Wed 8 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Thu 9 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Fri 10 |
This course gives an introduction to linear mixed effects models, also called multi-level models or hierarchical models, for the purposes of using them in your own research or studies. We emphasise the practical skills and key concepts needed to work with these models, using applied examples and real datasets. After completing the course, you should have:
Please note that this course builds on knowledge of linear modelling, therefore should not be considered a general introduction to statistical modelling.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Tue 14 |
Knowing how to use High Performance Computing (HPC) systems is crucial for fields such as bioinformatics, big data analysis, image processing, machine learning, parallel task execution, and other high-throughput applications. In this introductory course, you will learn the fundamentals of HPC, including what it is and how to effectively utilise it. We will cover best practices for working with HPC systems, explain the roles of "login" and "compute" nodes, outline the typical filesystem organization on HPC clusters, and cover job scheduling with the widely-used SLURM scheduler. This hands-on workshop is designed to be accessible to researchers from various backgrounds, providing numerous opportunities to practice and apply the skills you acquire. As an optional session for those interested, we will also introduce the (free) HPC facilities available at Cambridge University (the course is not otherwise Cambridge-specific).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Wed 15 |
Knowing how to use High Performance Computing (HPC) systems is crucial for fields such as bioinformatics, big data analysis, image processing, machine learning, parallel task execution, and other high-throughput applications. In this introductory course, you will learn the fundamentals of HPC, including what it is and how to effectively utilise it. We will cover best practices for working with HPC systems, explain the roles of "login" and "compute" nodes, outline the typical filesystem organization on HPC clusters, and cover job scheduling with the widely-used SLURM scheduler. This hands-on workshop is designed to be accessible to researchers from various backgrounds, providing numerous opportunities to practice and apply the skills you acquire. As an optional session for those interested, we will also introduce the (free) HPC facilities available at Cambridge University (the course is not otherwise Cambridge-specific).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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In this course you will acquire practical skills in RNA-seq data analysis. You will learn about quality control, alignment, and quantification of gene expression against a reference transcriptome. Additionally, you will learn to conduct downstream analysis in R, exploring techniques like PCA and clustering for exploratory analysis. The course also covers differential expression analysis using the DESeq2 R/Bioconductor package. Furthermore, the course covers how to generate visualisations like heatmaps and performing gene set testing to link differential genes with established biological functions or pathways.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Thu 16 |
The Unix shell (command line) is a powerful and essential tool for modern researchers, especially in computational fields such as bioinformatics and large-scale data analysis. In this course, you will:
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Fri 17 |
Setting up a computer for running bioinformatic analysis can be a challenging process. Most bioinformatic applications involve the use of many different software packages, which are often part of long data processing pipelines. In this course we will teach you how to overcome these challenges by using package managers and workflow management software. We will have examples of software and pipelines for processing different types of data (RNA-seq, ChIP-seq, variant calling and viral genomes), making this course appealing to researchers working in a wide range of applications. However, please note that we will not cover the details of any specific type of bioinformatic analysis. The idea of this course is to introduce the computational tools to get your work done, not to teach how those tools work. We will also not teach you how to write your own pipelines, or create your own software containers, but rather on how to use existing tools to boost your bioinformatic analysis.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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This course on unsupervised learning provides a systematic introduction to dimensionality reduction and clustering techniques. The course covers fundamental concepts of unsupervised learning and data normalization, then progresses through the practical applications of Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and hierarchical clustering algorithms. The course emphasizes both theoretical understanding and hands-on application, teaching students to recognize when different techniques are appropriate and when they may fail. A key learning objective is understanding the limitations of linear methods like PCA. Students learn to evaluate the performance of unsupervised learning methods across diverse data types, with the ultimate goal of generating meaningful hypotheses for further research.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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| Mon 20 |
This course covers the potential pitfalls of short-read sequencing studies and provides options for visualisation and quality control (QC) for early detection and diagnosis of issues. You will gain an understanding of Illumina sequencing and different QC metrics that can be extracted from sequencing reads, such as base quality scores. The course also covers how QC metrics vary across different library types and thus distinguish between expected and unexpected QC results. You will be introduced to key software tools including FastQC, FastQ Screen, and MultiQC to carry out quality assessment of your sequencing data. Note that the main focus of this course is on how to interpret quality reports produced by these tools, not on how to run them (although we do provide the basic commands you need to do it).
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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This course on unsupervised learning provides a systematic introduction to dimensionality reduction and clustering techniques. The course covers fundamental concepts of unsupervised learning and data normalization, then progresses through the practical applications of Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and hierarchical clustering algorithms. The course emphasizes both theoretical understanding and hands-on application, teaching students to recognize when different techniques are appropriate and when they may fail. A key learning objective is understanding the limitations of linear methods like PCA. Students learn to evaluate the performance of unsupervised learning methods across diverse data types, with the ultimate goal of generating meaningful hypotheses for further research.
If you do not have a University of Cambridge Raven account please book or register your interest here. If for any reason the above links do not work, please email Research Informatics Training Team with details of your course enquiry. Additional information
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