• Data science in manufacturing

Data science is transforming the manufacturing industry by turning vast amounts of production data into actionable insights. Through data analysis, companies can optimize processes, enhance quality control, and improve maintenance, all of which contribute to greater efficiency and competitiveness. Artificial intelligence (AI) also plays a critical role in this transformation, offering tools to analyze data, predict outcomes, and support decision-making.

Guided by European standards like the Ethics Guidelines for Trustworthy AI, these AI systems are designed to be transparent and privacy-respecting, enabling workers to understand and manage the algorithms they work with. By selecting and analyzing the right industrial data, employees can ensure that AI systems operate ethically and responsibly, keeping humans in the loop for critical decisions.

The project is funded by:

Data science is transforming the manufacturing industry by turning vast amounts of production data into actionable insights. Through data analysis, companies can optimize processes, enhance quality control, and improve maintenance, all of which contribute to greater efficiency and competitiveness. Artificial intelligence (AI) also plays a critical role in this transformation, offering tools to analyze data, predict outcomes, and support decision-making.

Guided by European standards like the Ethics Guidelines for Trustworthy AI, these AI systems are designed to be transparent and privacy-respecting, enabling workers to understand and manage the algorithms they work with. By selecting and analyzing the right industrial data, employees can ensure that AI systems operate ethically and responsibly, keeping humans in the loop for critical decisions.

The project is funded by:

5 Data science courses

DATA SCIENCE JOURNEY

The course “Data science (DS) journey” will facilitate managers and engineers rethink their business activities by focusing on higher-added functions. It covers the structured steps of a DS project: Business understanding, Data types in manufacturing, Data collection, Data preparation, Modeling, Model testing and deployment. The focus is to demonstrate how to catch the whole potential of DS techniques in manufacturing through various use cases. So, the complete DS Journey will be presented, explaining step by step how to implement a data science project on the manufacturing business.

QUALITY CONTROL

The “Quality Control in Manufacturing” course is designed to help managers and engineers enhance product consistency and minimize defects using data science. This course will guide participants through real-world applications of quality control, covering essential stages such as understanding manufacturing quality challenges, collecting and preparing relevant data, applying DS models to detect and address quality issues, and validating the outcomes. Through hands-on examples, learners will discover how data science can improve quality management processes and enhance product reliability.

PREDICTIVE MAINTENANCE

The “Predictive Maintenance” course provides a comprehensive approach to preventing equipment failures and extending machine life using data-driven strategies. Participants will learn to analyze maintenance use cases, apply DS techniques to predict when maintenance is needed, and validate results in real-life scenarios. With a focus on minimizing unplanned downtime, the course includes step-by-step guidance on implementing a predictive maintenance project, offering practical insights into using data science to increase operational efficiency and equipment longevity.

COST ESTIMATION

This course equips manufacturing professionals with the skills to accurately estimate costs across production processes. Through real industrial cases, participants will learn to analyze cost drivers, implement data science techniques for cost forecasting, and develop models to enhance commercial decision-making. The course covers key methodologies for tracking operational expenses and maintain efficiency. By the end, managers and engineers will be able to leverage data-driven approaches to improve cost control, enhance resource allocation, and drive profitability.

SUPPLY CHAIN MANAGEMENT

The “Supply Chain Management” course explores the use of data science to enhance the flow of materials and information across the supply chain. This learning path guides participants through real-world supply chain challenges, showcasing how to apply DS models for demand forecasting, inventory optimization, and logistics planning. With a structured approach, participants will gain insights into using data science to drive efficiency, reduce costs, and enhance responsiveness within the supply chain. This course provides a solid foundation for data-driven decision-making in supply chain management.

5 Data science courses

DATA SCIENCE JOURNEY

The course “Data science (DS) journey” will facilitate managers and engineers rethink their business activities by focusing on higher-added functions. It covers the structured steps of a DS project: Business understanding, Data types in manufacturing, Data collection, Data preparation, Modeling, Model testing and deployment. The focus is to demonstrate how to catch the whole potential of DS techniques in manufacturing through various use cases. So, the complete DS Journey will be presented, explaining step by step how to implement a data science project on the manufacturing business.

QUALITY CONTROL

The “Quality Control in Manufacturing” course is designed to help managers and engineers enhance product consistency and minimize defects using data science. This course will guide participants through real-world applications of quality control, covering essential stages such as understanding manufacturing quality challenges, collecting and preparing relevant data, applying DS models to detect and address quality issues, and validating the outcomes. Through hands-on examples, learners will discover how data science can improve quality management processes and enhance product reliability.

PREDICTIVE MAINTENANCE

The “Predictive Maintenance” course provides a comprehensive approach to preventing equipment failures and extending machine life using data-driven strategies. Participants will learn to analyze maintenance use cases, apply DS techniques to predict when maintenance is needed, and validate results in real-life scenarios. With a focus on minimizing unplanned downtime, the course includes step-by-step guidance on implementing a predictive maintenance project, offering practical insights into using data science to increase operational efficiency and equipment longevity.

COST ESTIMATION

This course equips manufacturing professionals with the skills to accurately estimate costs across production processes. Through real industrial cases, participants will learn to analyze cost drivers, implement data science techniques for cost forecasting, and develop models to enhance commercial decision-making. The course covers key methodologies for tracking operational expenses and maintain efficiency. By the end, managers and engineers will be able to leverage data-driven approaches to improve cost control, enhance resource allocation, and drive profitability.

SUPPLY CHAIN MANAGEMENT

The “Supply Chain Management” course explores the use of data science to enhance the flow of materials and information across the supply chain. This learning path guides participants through real-world supply chain challenges, showcasing how to apply DS models for demand forecasting, inventory optimization, and logistics planning. With a structured approach, participants will gain insights into using data science to drive efficiency, reduce costs, and enhance responsiveness within the supply chain. This course provides a solid foundation for data-driven decision-making in supply chain management.

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