Cambridge Centre for Research Informatics Training course timetable
September 2025
| Mon 15 |
Data analysis in R (IN-PERSON)
[Places]
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.
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
|
|
| Tue 16 |
Data analysis in R (IN-PERSON)
[Places]
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.
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
|
|
| 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
|
|
| 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
|
|
| 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
|
|
| Mon 22 |
Data analysis in Python (IN-PERSON)
[Places]
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
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
|
|
| Tue 23 |
Data analysis in Python (IN-PERSON)
[Places]
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core concepts of Python including Python syntax, data structures and reading/writing files. These are illustrated by a series of example programs. Upon completion of the course, participants will be able to write simple Python programs.
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
|
|
| 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
|
|
|
Introduction to the Unix command line (IN-PERSON)
Not bookable
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
|
||
| 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
|
|
|
Working on HPC clusters (IN-PERSON)
Not bookable
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
|
||
| 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
|
|
|
Working on HPC clusters (IN-PERSON)
Not bookable
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
|
||
| 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
|
October 2025
| Wed 1 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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
|
|
| Thu 2 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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
|
|
| Fri 3 |
Single-cell RNA-seq analysis (IN-PERSON)
Not bookable
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
|
|
| 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
|
|
| 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
|
|
| 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
|
|
| Thu 16 |
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
|
|
| 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
|
|
| Mon 20 |
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
|
|
| Tue 21 |
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
|
|
| Thu 23 |
Data analysis in R (ONLINE LIVE TRAINING)
Not bookable
R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.
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
|