Data Visualisation with Python and AI

About

Data has become one of the most powerful ways we understand the world, from how societies and cultures evolve to how individuals make daily decisions. Yet data on its own rarely speaks clearly. It needs to be organized, interpreted, and communicated effectively. Visualization plays a central role in this process as it transforms numbers into stories, makes complex patterns visible, allows us to see relationships that are otherwise hidden, and thus to make the right data-informed decisions.

This course offers students a comprehensive introduction to the theory and practice of data visualization, grounded in the principles of clear, ethical, and impactful visual communication. Over two intensive weeks, participants will learn to read, critique, and design effective visualizations, and to apply theoretical frameworks (such as perceptual psychology and design principles) to practical examples using the Python programming language, modern plotting libraries, AI tools, Agents, and Skills. Students will also engage with questions of ethics, misrepresentation, and persuasion in data visualization, as they develop a critical and creative approach.

Course leader

Yan Asadchy, MSc, Junior Research Fellow at Tallinn University, is an interdisciplinary researcher and a Ph.D. student at CUDAN Open Lab, Tallinn University. He works at the intersection of computer science and humanities and uses computational methods to examine digital culture and self-representation on social media. He is a member of MIT’s Connection Science group and the DTx Research Group at Oulu University. Yan founded Affinity OÜ, a design agency that provides ethical design consultancy and solutions to startups and research groups in digital health, wellbeing, and AI. Yan has a background in UX/UI design and earned his MSc in Human-Computer Interaction from Tallinn University

Target group

The course is designed for Bachelor’s and Master’s students from any discipline who are interested in improving their ability to analyze and communicate with data. No prior experience in programming or statistics is required, though familiarity with basic data handling will be helpful. The mix of lectures, group discussions, hands-on labs, and project work makes the course accessible to beginners while also offering depth for more advanced students

Course aim

Understand the theoretical foundations of data visualization, including perception, cognition, and design principles.

Be able to critically evaluate visualizations in media, research, and professional contexts.

Gain practical experience in creating clear, ethical, and engaging visualizations using accessible tools

Fee info

Accommodation and meals are not included in the price