Type of course:
Digital learning, Lesson
Language:
EN
Duration:
15 minutes
Workload:
2 hours
Proficiency:
Intermediate
Target:
Workers, Professionals, Manager
SUMMARY
Exploratory data analysis (EDA) of images is about uncovering the hidden patterns, surprises, and stories in the pixels. Before any preprocessing or model training begins, EDA is where curiosity meets discovery, guiding us toward smarter decisions and deeper insights.
This lesson gives learners the ability to critically assess image datasets, using visualization techniques to reveal imbalances, biases, and subtle data characteristics that could impact the effectiveness of their models. Learners will apply these techniques on a given real dataset.

About The Author
Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.
Learning outcomes
- By the end of this lesson, students will be able to explain what is exploratory data analysis for image data.
- By the end of this lesson, students will be able to list various issues in image data and map them to appropriate exploratory data analysis techniques to identify them.
- By the end of this lesson, students will be able to execute exploratory data analysis Python code to analyze a given image dataset.
Course Content
Topics
Digital Transformation, Artificial Intelligence (AI), Computer Vision
Tags
Industry 5.0
