Exploratory Analysis of Biological Data: Data Carpentry

Overview

Course 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

Speakers

Speaker and Trainer

Florian Huber

EMBL Heidelberg

Germany

Hugo Tavares

Sainsbury Laboratory, University of Cambridge

United Kingdom

Thea Van Rossum

EMBL Heidelberg

Germany

Georg Zeller

EMBL Heidelberg

Germany

Scientific Organisers

Toby Hodges

EMBL Heidelberg

Germany

Course Organisers

Maryann Heck

EMBL Heidelberg

Germany

Programme