📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Data Science for Predictive Maintenance in Manufacturing, from foundations to a certified capstone project.
Framing
• Reactive, preventive, condition-based and predictive strategies compared on cost
• Choosing the target: failure event, degradation state or remaining life
• Establishing the economic baseline the model must beat
Data
• MES, SCADA, historian and maintenance log integration
• Maintenance records as noisy labels: missing, late and miscoded entries
• Class imbalance when failures are rare by design
Methods
• Classification for imminent failure and regression for remaining life
• Survival analysis with right-censored maintenance histories
• Sequence models over sensor histories and their data requirements
• Validation that respects time order and asset identity
Quality Link
• Linking equipment condition to product quality and scrap
• Statistical process control alongside learned models
• Root-cause analysis across multi-stage production lines
Value
• Cost-sensitive thresholds using downtime, spares and labour costs
• Pilot design that produces a defensible before-and-after comparison
• Scaling from one line to a plant, and what breaks when you do
Earn government-registered certification in Data Science for Predictive Maintenance in Manufacturing
e-Certificate and e-Marksheet issued on successful completion.
