Lifelong Learning for Professionals

Design and Analysis of Experiments, 2.5 credits

Data-Driven Process Improvement through Design of Experiments

Do you want to make more confident, data-driven decisions and systematically improve manufacturing processes? This course provides practical tools to plan, conduct, and analyse experiments in real industrial environments. By working with statistical thinking and modern approaches to Design of Experiments, you will learn how to identify the factors that influence quality, performance, and variation – and how to optimise them in a structured and efficient way.

The course is designed for professionals in production, quality, process development, and R&D who want to strengthen their ability to use data as a basis for improvement. The focus is on industrial applications, particularly within metal manufacturing, where sound analysis can support more stable processes, better use of resources, and well-grounded decisions.

After completing the course, you will have a clear methodology for testing ideas, evaluating results, and driving improvement initiatives based on evidence – skills that are directly applicable in your daily work and valuable for both you and your organisation.

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Foundations of Data Driven thinking, 2.5 credits

Unlock the value of your manufacturing data with the Foundations of Data-Driven Thinking.
This course targets a wide range of roles in industry and the public sector, such as decision-makers, engineering and technical specialists in design and production, and R&D personnel, who seek practical insight into how data-driven AI can be leveraged to create value in their organizations. Working hands-on with real industrial datasets, you’ll learn how to handle common manufacturing data types, such as sensor and time-series data, process and production logs, and quality and test measurements. You will apply methods to:

• assess data quality and readiness (noise, drift, missing values, inconsistencies)
• clean and prepare data for analysis
• perform exploratory data analysis to reveal trends, relationships, and root causes
• visualize and communicate insights in clear, decision-oriented ways.

You’ll also learn how to use the gained insights to plan the next step in an analytics or AI initiative, such as building solutions for predictive maintenance, automated quality inspection, anomaly detection, scrap and yield improvement, process monitoring, or throughput and cycle-time prediction.

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Machine Learning and AI in Manufacturing, 2.5 credits

Transform manufacturing performance with the power of AI and machine learning.
Machine Learning and AI in Manufacturing Analytics equips professionals with the tools and knowledge to harness predictive modeling, anomaly detection, and explainable AI to improve quality, reduce waste, and optimize industrial processes.

Through hands-on experience with real manufacturing data, participants gain practical expertise in modern machine learning techniques, robust validation methods, and responsible AI practices including transparency, fairness, and risk management.

This course emphasizes the deployment of interpretable, sustainable, and trustworthy AI solutions that enhance decision-making and support the transition toward more efficient, resilient, and data-driven manufacturing system.

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