Advanced Data Visualization with Python
Course Objective
Data visualization is an essential tool in academic research—it allows you to explore complex datasets, communicate results effectively, and support your arguments with clarity and precision. This course focuses on advanced data visualization techniques using the Python programming language, with a particular emphasis on methods that are relevant across disciplines in academia. In this course, you will learn how to use Python to create clear, informative, and publication-ready visualizations. You will explore how to visualize multivariate data, how to communicate statistical relationships, and how to represent uncertainty in your findings. By working through practical coding exercises using real datasets, you will gain a deeper understanding of how thoughtful visual design can enhance your research.
Course Description
This course aims at researchers with prior experience in Python. Hence, we will dive right into data visualization concepts that are frequently used in an academic setting.
After successfully completing this course, you will be able to:
- Create advanced plots to explore and present multivariate datasets
- Use Python libraries such as Seaborn and Matplotlib to build customized figures
- Visualize statistical models, confidence intervals, and distribution-based uncertainty
- Apply best practices for clarity, accuracy, and aesthetics in academic data visualization
Just following along fancy slides won’t magically transfer the skill of coding to you. But you actively engaging with the course content in your development environment will more likely do just that.
That’s why you need to prepare accordingly: Please ensure that you have access to Google Colab before the course. We will use Google Colab for the coding parts, such that we can use Python without (sometimes time-consuming) pre-configuration or installation on your machine. To use Google colab, you need a Google account (same account which is used for Gmail, YouTube, etc.)
Course Material
You can find the lecture slides here.
In case you want to look something up to apply it in the hands-on exercise sessions, you can find the lecture notebook here. But please be aware that this is just a quick overview and I will provide you with more examples during the lecture.
The Google Colab notebook with the exercises is here. The solutions are here.
Happy coding!