Bioinformatics 2020
(Thu 11 Apr 2019 - Fri 11 Dec 2020)
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November 2020
| Wed 25 |
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. |
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Core Statistics
Finished
PLEASE NOTE that this course will be taught live online, with demonstrators available to help you throughout if have any questions. All lecture components will be recorded and uploaded to the course Moodle page so that you will be able to access that information even if technical or time zone restrictions means that you aren't able to join us for the live sessions. This virtually delivered course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. 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:
Both R and Python are free software environments that are suitable for statistical and data analysis. 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 or Python and moreover know when, and when not, to apply these techniques. |
December 2020
| Tue 1 |
Managing your Research Data (Online)
Finished
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. How much data would you lose if your laptop was stolen? Have you ever emailed your colleague a file named 'final_final_versionEDITED'? Have you ever struggled to import your spreadsheets into R? Would you be able to write a Data Management Plan as part of a grant proposal? As a researcher, you will encounter research data in many forms, ranging from measurements, numbers and images to documents and publications. Whether you create, receive or collect data, you will certainly need to organise it at some stage of your project. This workshop will provide an overview of some basic principles on how we can work with data more effectively. We will discuss the best practices for research data management and organisation so that our research is auditable and reproducible by ourselves, and others, in the future. Course materials are available here This event is part of a series of training courses organized in collaboration with the Bioinformatics Core Facility at CRUK Cambridge Institute. 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 |
This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data. The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools. Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility. More information about the course can be found here. 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 |
This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data. The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools. Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility. More information about the course can be found here. 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 |
This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data. The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools. Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility. More information about the course can be found here. 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 |
This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data. The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools. Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility. More information about the course can be found here. 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 |
This 1-week course provides an introduction to data exploration of biological data. It provides a learning journey starting with learning about how we can automate processes that can be reproduced to analyse our biological data. The course will begin with discussing what opportunities and challenges are associated with aspects of bioinformatics analyses. We will address a subset of them in greater detail in the central part of the course and provide time for participants to practise using some of the associated bioinformatics tools. Focusing on solutions around handling biological data, we will cover programming in R, version control, statistical analyses, and data exploration. The R component of the course will cover from the foundations of programming in R to how to use some of the most popular R packages (dplyr and ggplot2) for data manipulation and visualisation. No prior R experience or previous knowledge of programming is required. At the end of the course we will address issues relating to reusability and reproducibility. More information about the course can be found here. 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. |
