Webinar: Interactive Visualization with Python
This intermediate level course is addressed to biologists, bioinformaticians, and other computational scientists which use python in their research and would like to enhance their data exploration and presentation capabilities with interactive plots.
Registration deadline: 22 October 2024
Date: 12 November 2024
General information
- Time: 12 November 2024
- Registration deadline: 22 October 2024
- Location: Online
- Fees:
- Academic: 100 CHF
- For-profit: 500 CHF
Description
If a picture is worth a thousand words, then one may say an interactive picture is worth a million.
In today’s data-driven research environment, interactive visualization is becoming an essential tool for effectively exploring and presenting complex datasets.
This one-day streamed course is designed to provide you with a solid grounding in this topic and help you get your bearings in this dynamic world.
You’ll learn to select the right visualization tools for your needs, create and customize interactive plots using Python Plotly, and develop web-based applications with Plotly-Dash.
These techniques will enable you to make your data exploration more intuitive and your findings easier to share. Enhance your research with clear, interactive visualizations and improve your ability to communicate scientific insights effectively.
Learning outcomes
At the end of this course, participants are expected to:
- Compare and choose the most relevant interactive visualization technical solution based on their needs and means, from simple interactive plots, complex static html pages using web assembly, to full-blown dash apps
- Create simple interactive plots and tune them to make them useful for scientific data exploration with python plotly
- Enrich visualizations with interactive elements while keeping them easy to share as simple html files with python plotly or web assembly
- Develop web server-based data visualization applications with plotly-dash
Prerequisites
Participants should be fluent in the python programming language, including a working knowledge of standard data-analysis libraries such as numpy and pandas.
A basic knowledge of standard python plotting libraries (matplotlib, seaborn) as well as some basic HTML elements would be a plus.