The goal of this course this to provide students and established scientists with an introduction to the purpose, principals and methods of data visualization. Data visualization is fundamental to the presentation and publication of data in every branch of the sciences and is increasingly important in a world of increasingly large and complex datasets. However, few scientists receive any formal training on how to produce clear, powerful images of their data. This course is designed to help fill this void, and includes the option for participants to submit work from their own research project for assement. By the end of the course, participants will have a sound understanding of:
• The power and purpose of graphical representation in scientific discovery and communication
• The main tools that are available for making and optimizing visualizations
• How to choose the right visualization for the data and message
• The qualities of good and bad visualizations
• How to maximize the functionality of a visualization
• The importance and theory of color and aesthetics
• The process of going from raw data to images that are optimized for publication or presentation
Target group: Students and postdocs that are interested in learning and improving data visualization skills
Prerequisites: Basic data handling/programming skills; basic data visualization skills
Evaluation: Regular assignments
Teaching format: Lectures / group discussions / project work
ECTS: 3 Year: 2020
Track segment(s):
DSSC-ANA Data Science and Scientific Computing - Data Analysis
Teacher(s):
Daria Shipilina Sean Stankowski
Teaching assistant(s):
If you want to enroll to this course, please click: REGISTER
- Teacher: Daria SHIPILINA
- Teacher: Sean Stankowski