Data Visualization for Biology: a practical workshop on design, techniques and tools
The application system requires cookies, and the limited processing of your personal data in order to function. By applying to this course you are agreeing to this as outlined in our Privacy Notice and Terms of Use
Date:
Monday 23 - Friday 27 November 2015Venue:
KU Leuven ESAT/STADIUS - KU Leuven ESAT/STADIUS, Kasteelpark Arenberg 10, 3001 Leuven, Leuven, BelgiumApplication opens:
Monday 01 June 2015Application deadline:
Thursday 03 September 2015Participation:
Open application with selectionContact:
Maria Bacadare GoitiaRegistration fee:
€350Registration closed
Course Overview
As biological datasets increase in size and complexity, we are moving more and more from an hypothesis-driven research paradigm to a data-driven one. As a result, exploration of that data has become even more crucial than in the past.
In this 5-day workshop, we will dive into the topic of biological data visualization and how it can be used to gain insight in and get a "feel" for a dataset, so that targeted analyses can be defined. We will start by covering theoretical questions like: What is data visualization? How do we perceive images? How can we visualise data in the best possible way? As the workshop continues, it will become more and more hands-on and interactive; a large part will be committed to creating visualisations using the D3 javascript library, including 2-day projects creating interactive visualisations based on data brought by the students themselves.
Details of the course (programme, trainers and the cost) are yet to be confirmed and are subject to funding arrangements. Please view here for details of this course.
Audience
This workshop is aimed at PhD students and researchers in the fields of genomics and transcriptomics, with previous experience of programming, and who wish to gain more experience of data visualization.
Learning outcomes
After this course you should be able to:
- Use programming approaches to visualize data
- Discuss best practices in data visualization