Data Science for Manufacturing: Modeling (Pilot)

SUMMARY

How do manufacturers optimize processes, reduce costs, and improve efficiency using data? It all starts with effective modeling. This lesson offers a deep dive into the Modeling phase of the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, focusing on building, training, and evaluating models for manufacturing challenges.

Learn how to select the right algorithms for problems like predictive maintenance, quality control, and supply chain optimization. Understand the iterative process of modeling to ensure reliable and accurate outcomes.

Through practical examples, such as predicting equipment failures and detecting defects, this lesson provides hands-on insights into real-world applications.

By the end, you will be equipped with the knowledge to build and deploy data models that drive tangible improvements in your manufacturing operations.

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.