RESEARCH & EDUCATION

Data visualization

    
3x a yearNovember, February, June
Onsite25
RESEARCH & EDUCATION

Data visualization

3x a year
November, February, June
Onsite
25

 

 

 

 

 

 

 

 

 

 

 

 

 

Data visualization

In our data-driven world, the ability to visualize information clearly and compellingly is crucial. Modern data visualization techniques transform complex datasets into visually appealing insights. Yet, crafting high-quality visualizations that truly resonate with your audience can be a daunting task, fraught with challenges such as:

  • Setting up the right environments
  • Selecting the appropriate visual elements
  • Mastering various tools
  • Writing effective code
  • Creating publication-ready visuals

Content and learning goals of the course:

This course is designed to immerse you in the world of data visualization, equipping you with the skills to turn data into stunning, insightful visuals. What You'll Learn:

  • Data Handling for Visualizations: Learn the essentials of managing and preparing data for visualization.
  • Visualization Techniques: Explore and implement a range of popular visualization methods commonly used in scientific research.
  • Perception in Visualization: Understand key perception principles to ensure your visualizations are intuitive and impactful.
  • Design Principles: Dive into the fundamentals of effective visualization design.

Throughout the course, you'll engage with real-world use cases, designing compelling visualizations to address specific problems and scenarios.

Tools and Technologies:

  • Tableau: Master this powerful drag-and-drop tool to create interactive charts, including bar and scatter plots, maps, heat maps, and tree maps.
  • Python and R Notebooks: For other visualization techniques like violin plots, parallel coordinates and projections, you'll learn to leverage the capabilities of Python and R to build insightful  visualizations.
  • JupyterLab: Utilize this versatile development environment to create, share, and experiment with code and visualizations seamlessly.

Prerequisites:

Basic Python or R skills are helpful but not strictly necessary.

Dates:

The course consists of six 2-hour sessions (9:00 to 11:00), which are all within 2 weeks:

  • November 2025: 3th, 4th, 6th, 10th, 11th, 13th Nov
  • February 2026: 9th, 10th, 12th, 16th, 17th, 19th Feb
  • June 2026: 1st, 2nd, 4th, 8th, 9th, 11th Jun
Recommended credits
1 ECTS
 
Course coordinator
Venustiano Soancatl Aguilar
 
Location:
Onsite
   
 
Target audience
 
   
 
PhD candidates (any stage)

 

 
   

 

 
   
 
Contact person
 
   
 
Melina Aarnikoivu
phd-academy.fse@rug.nl

 

 
   

 

GREAT THAT YOU WANT TO ENROLL

Explore the options below:
COURSE FULL?
Still enroll and join the waiting list!