Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Data Science for Predictive Maintenance in Manufacturing takes a methods-first view of keeping machines running. You learn the data-science workflow applied to maintenance: acquiring and cleaning sensor and maintenance-log data, engineering features that signal wear, and building and validating models that predict failures and estimate remaining useful life. The course emphasises the analytical rigour — validation, drift, cost trade-offs — that reliable predictive maintenance demands. You finish able to run the data-science process behind a predictive-maintenance solution. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers data science for predictive maintenance in manufacturing — the analytics workflow, from sensor data and feature engineering to failure and remaining-life models.
📋 Course Objectives
1. Acquire and clean sensor and maintenance data.
2. Engineer features that reveal wear.
3. Build failure-prediction and remaining-life models.
4. Validate models and handle drift.
5. Weigh maintenance cost trade-offs.
👥 Who Should Enroll?
• Manufacturing data scientists and analysts
• Reliability and maintenance engineers
• Industrial analytics teams
• Students of data science
🚀 Key Learning Outcomes
• A data-science workflow for maintenance.
• A predictive-maintenance modelling project.
• A rigour-first analytical approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Maintenance Strategy as a Modelling Problem
• 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
Manufacturing Data Sources and Their Defects
• 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
Modelling Approaches
• 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
Process Quality and Yield
• Linking equipment condition to product quality and scrap
• Statistical process control alongside learned models
• Root-cause analysis across multi-stage production lines
Deployment and Business Case
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Seaborn |
| Covered Tool / Platform | Tableau |
| Covered Tool / Platform | SQL |
Frequently Asked Questions
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