skip to navigation skip to content

Bioinformatics Training

Bioinformatics course timetable

Show:

Sat 11 Jul – Tue 29 Sep

Now Today

[ No events on Sat 11 Jul ]

July 2020

Mon 13
An Introduction to Solving Biological Problems with Python (ONLINE LIVE TRAINING) (1 of 2) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customize more complex code to fit their needs.

Course materials are available here.

Please note that the content of this course has recently been updated. This course now mostly focuses on core concepts including Python syntax, data structures and reading/writing files. Concepts and strategies for working more effectively with Python are now the focus of a new 2-days course, Data Science in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Tue 14
An Introduction to Solving Biological Problems with Python (ONLINE LIVE TRAINING) (2 of 2) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customize more complex code to fit their needs.

Course materials are available here.

Please note that the content of this course has recently been updated. This course now mostly focuses on core concepts including Python syntax, data structures and reading/writing files. Concepts and strategies for working more effectively with Python are now the focus of a new 2-days course, Data Science in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Wed 15
An Introduction to Machine Learning (ONLINE LIVE TRAINING) (1 of 3) Finished 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Thu 16
An Introduction to Machine Learning (ONLINE LIVE TRAINING) (2 of 3) Finished 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Fri 17
An Introduction to Machine Learning (ONLINE LIVE TRAINING) (3 of 3) Finished 09:30 - 17:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.

Please be aware that the course syllabus is currently being updated following feedback from the last event; therefore the agenda below will be subjected to changes.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Wed 29
KNIME: Practical introduction to KNIME Analytics platform and its application in bioinformatics (Webinar) (1 of 2) Finished 14:00 - 16:00 Bioinformatics Training Facility - Webinar (Time Zone = BST)

This event introduces participants to the KNIME Analytics Platform, an open source data science platform with a visual workflow editor, that can be used by users without prior programming experience or integrated with existing scripts written in R or Python.

These sessions are aimed towards anyone who has an interest in building data science workflows with different kinds of life science data. The sessions will cover how to aggregate data from different sources (e.g., files, databases, web services), how to calculate simple statistics (e.g., for data exploration), network mining (e.g., protein-protein interactions) and big data analytics (e.g., next-generation sequencing data).

The webinar will combine practical and taught content to demonstrate how users can use KNIME to design and utilise reproducible data science workflows, such as analytics tasks, and better explore and understand their data.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Thu 30
Inferring Co-Expressing Genes and Regulatory Networks from RNA-Seq Data (Webinar) Finished 11:00 - 13:00 Bioinformatics Training Facility - Webinar (Time Zone = BST)

One of the most important tasks of systems biology is to create explanatory and predictive models of complex biological systems. Availability of gene expression data in different conditions has paved the way for reconstructing direct or indirect regulatory connections between various genes and gene products. Most often, we are not interested in single interactions between gene products; instead, we try to reconstruct networks that provide insights into the investigated biological processes.

This webinar will introduce the importance and applications of Gene Expression Datasets (Microarrays and RNA-Seq), followed by methods of extraction and analysis of Co-Expression Networks and Transcriptional Regulatory Networks from these datasets. The webinar will focus on the pros and cons of Weighted and Unweighted Networks, citing examples to aid decisions about which networks to use and when.

The webinar will be presented in the form of a lecture and tutorial with screenshots that enable listeners to emulate the protocols in R. Note that this is a webinar and not a coding exercise. Links to further reading and practice will be shared.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

KNIME: Practical introduction to KNIME Analytics platform and its application in bioinformatics (Webinar) (2 of 2) Finished 14:00 - 16:00 Bioinformatics Training Facility - Webinar (Time Zone = BST)

This event introduces participants to the KNIME Analytics Platform, an open source data science platform with a visual workflow editor, that can be used by users without prior programming experience or integrated with existing scripts written in R or Python.

These sessions are aimed towards anyone who has an interest in building data science workflows with different kinds of life science data. The sessions will cover how to aggregate data from different sources (e.g., files, databases, web services), how to calculate simple statistics (e.g., for data exploration), network mining (e.g., protein-protein interactions) and big data analytics (e.g., next-generation sequencing data).

The webinar will combine practical and taught content to demonstrate how users can use KNIME to design and utilise reproducible data science workflows, such as analytics tasks, and better explore and understand their data.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

August 2020

Mon 10
Identification of Eigen-genes, consensus modules and Network Motifs in co-expression (or other biological) networks. (Webinar) Finished 11:00 - 13:00 Bioinformatics Training Facility - Webinar (Time Zone = BST)

One of the most important tasks of systems biology is to create explanatory and predictive models of complex biological systems. Availability of gene expression data in different conditions has paved the way for reconstructing direct or indirect regulatory connections between various genes and gene products. Most often, we are not interested in single interactions between gene products; instead, we try to reconstruct networks that provide insights into the investigated biological processes or the entire system as a whole.

This webinar will expand upon the concept of Gene Co-expression Networks to elucidate Weighted Gene Co-expression Network Analysis (WGCNA), and introduce the importance of visualising clustered gene expression profiles as single ‘Eigengenes’. It will describe the complete protocol for WGCNA analysis starting from normalised Gene Expression Datasets (Microarrays or RNA-Seq). This will be followed by a discussion on methods of extraction and analysis of consensus modules and Network motifs from Gene Co-Expression Networks and Transcriptional Regulatory Networks.

The webinar will be presented in the form of a lecture and tutorial with screenshots that enable listeners to emulate the protocols in R. Note that this is a webinar and not a coding exercise. Links to further reading and practice will be shared.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

September 2020

Tue 1
Using the Ensembl Genome Browser (ONLINE TRAINING) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

The Ensembl Project provides a comprehensive and integrated source of annotation of, mainly vertebrate, genome sequences. This workshop offers a comprehensive practical introduction to the use of the Ensembl genome browser as well as essential background information.

This course will focus on the vertebrate genomes in Ensembl, however much of what will be covered is also applicable to the non-vertebrates (plants, bacteria, fungi, metazoa and protists) in Ensembl Genomes.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Wed 2
Ensembl REST API workshop (ONLINE TRAINING) Finished 09:30 - 15:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

The Ensembl project provides a comprehensive and integrated source of annotation of mainly vertebrate genome sequences.

This workshop is aimed at researchers and developers interested in exploring Ensembl beyond the website. The workshop covers how to use the Ensembl REST APIs, including understanding the major endpoints and how to write scripts to call them.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Fri 4
Statistics bootcamp using R (Online) (1 of 6) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Mon 7
Statistics bootcamp using R (Online) (2 of 6) Finished 09:30 - 14:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Tue 8
Statistics bootcamp using R (Online) (3 of 6) Finished 09:30 - 14:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Wed 9
Statistics bootcamp using R (Online) (4 of 6) Finished 09:30 - 14:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Thu 10
Statistics bootcamp using R (Online) (5 of 6) Finished 09:30 - 14:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Fri 11
Statistics bootcamp using R (Online) (6 of 6) Finished 09:30 - 14:00 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This bootcamp provides an in depth look at statistical analyses using R.

Day 1 aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. As well as introducing core R language concepts, this course also provides the basics of using the Tidyverse for data manipulation, and ggplot for plotting. It will focus on entering and manipulating data in R and producing simple graphs.

PLEASE NOTE: If you are already comfortable working in R and using the tidyverse package, you might find that you can skip Friday’s training session. Please review the pre-requisites section below for further information.

Day 2-6 (half days) will focus on the statistical possibilities of R, covering from experimental design to analysis of quantitative and qualitative data. Ample time will be given to participants to practise different type of analysis and interact with the trainers to discuss their statistical problems.

This event is organized in collaboration with the Babraham Institutes's Bioinformatics Group and it is supported by the BBSRC Strategic Training Awards for Research Skills (STARS) grant (BB/P022766/1).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Thu 17
Data Science School: Machine learning applications for life sciences (Online) charged (1 of 4) Finished 10:00 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

THIS EVENT IS NOW FULLY BOOKED!

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Centre for Data-Driven Discovery (C2D3).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Fri 18
Data Science School: Machine learning applications for life sciences (Online) charged (2 of 4) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

THIS EVENT IS NOW FULLY BOOKED!

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Centre for Data-Driven Discovery (C2D3).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Mon 21
Data Science School: Machine learning applications for life sciences (Online) charged (3 of 4) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

THIS EVENT IS NOW FULLY BOOKED!

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Centre for Data-Driven Discovery (C2D3).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Tue 22
Data Science School: Machine learning applications for life sciences (Online) charged (4 of 4) Finished 09:30 - 13:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

THIS EVENT IS NOW FULLY BOOKED!

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.

Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.

This event is sponsored by Cambridge Centre for Data-Driven Discovery (C2D3).

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Wed 23
An Introduction to Solving Biological Problems with Python (ONLINE LIVE TRAINING) (1 of 2) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customize more complex code to fit their needs.

Course materials are available here.

Please note that the content of this course has recently been updated. This course now mostly focuses on core concepts including Python syntax, data structures and reading/writing files. Concepts and strategies for working more effectively with Python are now the focus of a new 2-days course, Data Science in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Thu 24
An Introduction to Solving Biological Problems with Python (ONLINE LIVE TRAINING) (2 of 2) Finished 09:30 - 17:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are lead through the core aspects of Python illustrated by a series of example programs. Upon completion of the course, attentive participants will be able to write simple Python programs and customize more complex code to fit their needs.

Course materials are available here.

Please note that the content of this course has recently been updated. This course now mostly focuses on core concepts including Python syntax, data structures and reading/writing files. Concepts and strategies for working more effectively with Python are now the focus of a new 2-days course, Data Science in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Mon 28
Data Science in Python (ONLINE LIVE TRAINING) (1 of 2) Finished 09:30 - 16:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code, using function and libraries. Participants will acquire a working knowledge of key concepts which are prerequisites for advanced programming in Python e.g. writing modules and classes.

Note: this course is the continuation of the Introduction to Solving Biological Problems with Python; participants are expected to have attended the introductory Python course and/or have acquired some working knowledge of Python. This course is also open to Python beginners who are already fluent in other programming languages as this will help them to quickly get started in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Tue 29
Data Science in Python (ONLINE LIVE TRAINING) (2 of 2) Finished 09:30 - 16:30 Bioinformatics Training Facility - Online LIVE Training (Time Zone = BST)

PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course covers concepts and strategies for working more effectively with Python with the aim of writing reusable code, using function and libraries. Participants will acquire a working knowledge of key concepts which are prerequisites for advanced programming in Python e.g. writing modules and classes.

Note: this course is the continuation of the Introduction to Solving Biological Problems with Python; participants are expected to have attended the introductory Python course and/or have acquired some working knowledge of Python. This course is also open to Python beginners who are already fluent in other programming languages as this will help them to quickly get started in Python.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.