Preprocessing for Tabular Data (Pilot)

SUMMARY

Have you ever encountered messy, inconsistent data and wondered how to make it analysis-ready? This lesson focuses on the crucial steps that come after Exploratory Data Analysis (EDA): Data Preprocessing. Proper preprocessing is the foundation for accurate and reliable insights, making it an essential skill for supply chain professionals and data enthusiasts alike.

In this lesson, you’ll learn practical techniques to address real-world data challenges in manufacturing and supply chain contexts. From handling missing values to scaling and transforming variables, every step is designed to ensure your data is clean, consistent, and ready for modeling or decision-making.

With interactive examples and exercises tailored to supply chain scenarios, you’ll leave this course confident in your ability to prepare data for deeper analysis and advanced machine learning applications.

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.