Predictive Maintenance with Data Science Tools and Big Data Analytics

This advanced course provides hands-on training in using data science tools and big data analytics to drive predictive maintenance strategies.

Participants will learn how to collect, process, and analyze large-scale equipment and sensor data, build machine learning models to forecast equipment failures, and optimize maintenance planning. Designed for data scientists, maintenance engineers, and analytics professionals, the course equips attendees with the knowledge and technical skills to shift from reactive to proactive maintenance using modern data-driven methodologies. 

• Understand the principles of predictive and proactive maintenance.
• Work with large-scale equipment and sensor datasets for analysis.
• Apply data science workflows to real-world maintenance problems.
• Use tools such as Python, Jupyter, Pandas, and Scikit-learn for model development.
• Build, validate, and deploy machine learning models for failure prediction.
• Apply big data concepts (e.g., Spark, time-series databases) in scalable environments.
• Translate predictive insights into actionable maintenance strategies.