BACKGROUND
Data visualization and communication enable scientists to interpret complex datasets, identify patterns, and convey research findings to diverse audiences.
Python has become a powerful tool in data science due to its extensive libraries and community support. Importantly, programming languages like Python offer the ability to seamlessly integrate and manage the entire data cycle, from data processing, analysis, and modeling to visualization and communication. Jupyter notebooks play a key role by promoting reproducibility and transparency, allowing scientists to document and share their analysis and visualization workflows.
WORKSHOP GOAL
This one-day course equips scientists with essential skills and innovative techniques to visualize and communicate data using Python and Jupyter. Participants will learn how to create basic and advanced static plots, interactive maps, and animations to analyze and communicate research results.
The course covers many popular Python libraries for plotting, with hands-on sessions using Python via Jupyter. The goal is to enhance visualization techniques for effectively communicating complex scientific data.
WORKSHOP CONTENT
Key topics covered include:
- The impact of data visualization in science
- Quickstart with Python using Jupyter notebooks
- Basic and advanced Python libraries for static plots
- Building interactive visualizations and animations
- Communication and storytelling with visual data
TARGET AUDIENCE & PRIOR KNOWLEDGE
This course is suitable for researchers of all Python skill levels, although basic knowledge in Python or another programming language is an advantage. Researchers from all disciplines are welcome, though researchers from the Earth Sciences will benefit most from data use cases. The workshop contains hands-on sessions and thus limited to max. 15 participants.
TECHNICAL REQUIREMENTS
ABOUT THE TRAINER
Annika Nolte is a data scientist for training and consulting at the DSC. She holds a master’s degree in Environmental Sciences from the Technical University of Braunschweig (2019) and is a PhD candidate at the Universität Hamburg. Annika has worked with Python for more than 4 years on database development and analysis of quantitative environmental data. Her skills lie in scientific programming, hydroinformatics, GIS and geospatial analysis and AI in environmental research.