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Exploratory Analysis of Biological Data: Data Carpentry – Course and Conference Office

EMBL Course

Exploratory Analysis of Biological Data: Data Carpentry

Overview
Speakers, Trainers and Organisers
Programme

EMBL is committed to sharing research advances and sustaining scientific interaction throughout the coronavirus pandemic. We are delighted to announce that the conference is going virtual and invite you to join us online. The virtual conference includes talks from invited speakers, short talk presenters, digital poster sessions, online group discussions and networking opportunities.

Conference Overview

The missing step between data collection and research progress is a lack of training for researchers in crucial skills for effectively managing and analysing large amounts of data. This hands on workshop teaches researchers the fundamental data skills they need to conduct their work, and provides high-quality, domain-specific training covering the full lifecycle of data-driven research. The domain-specific approach allows participants to tailor the data, content, and tools to reflect the specific data and analysis needs of different areas.

Audience

The course is aimed at graduate students and other researchers in the life sciences who would like to learn good practices in data management and analysis. The course is ideally suited to students without prior experience in computational research, but some familiarity with working with tabular data on a computer is welcome.

Modules/Resources

Data organisation and cleaning
Introduction to R
Data analysis and visualisation in R
Interacting with databases from R
Data analysis with applications to genomics

Learning Outcomes

How to organise data and some practices for more effective data handling
To effectively clean and format data and automatically track any changes
To use R to handle, filter and summarise tabular data
To use the split-apply-combine concept for data analysis
To visualise the results of these analyses in R using the ggplot2 library
To apply all of the concepts in an exploratory analysis of a typical biological dataset