Master’s Program
Industrial Data Analytics
Applied analytics for factories, supply chains, and operations
Overview
Industrial Data Analytics focuses on real operational constraints: messy data, complex processes, and decision-making under uncertainty. You’ll learn analytics patterns used in industry: KPI frameworks, root-cause analysis, forecasting, anomaly detection, and optimization-oriented reporting.
Key outcomes
- Model industrial processes and define actionable KPIs
- Build analytics workflows from raw data to decision dashboards
- Forecast demand/throughput and detect anomalies
- Explain insights to operations stakeholders with clear storytelling
- Deliver production-grade reporting assets and data products
Format
- Project-driven learning with realistic case studies
- Deliverables: dashboards, reports, notebooks, and documented analyses
- Remote-friendly (worldwide cohorts)
Tools
- SQL, Python (Pandas)
- Power BI / Tableau fundamentals (optional)
- Time-series basics (forecasting)
- Data quality checks & monitoring mindset
- Git fundamentals for analytics projects
Detailed program
A job-aligned curriculum for operations & industry: KPI systems, root-cause analysis, forecasting, anomaly detection and stakeholder-ready reporting.
Module 1 — Industrial Data Foundations (ERP, Sensors, Operations)
Understand industrial data sources and build reliable datasets from messy, real-world inputs.
2–3 weeks
Module 1 — Industrial Data Foundations (ERP, Sensors, Operations)
Understand industrial data sources and build reliable datasets from messy, real-world inputs.
What you’ll learn
- Industrial data landscape: ERP, MES, CMMS, IoT sensors, logs
- Data cleaning for operations: missingness, duplicates, inconsistent units
- Validation checks: schema, ranges, business rules, reconciliation
- Data modeling basics: facts/dimensions for operational reporting
- Documenting assumptions and building reproducible analytics notebooks
Skills you’ll gain
Module 2 — KPI Systems (OEE-style thinking & operational metrics)
Design KPI frameworks that actually drive decisions in operations.
2 weeks
Module 2 — KPI Systems (OEE-style thinking & operational metrics)
Design KPI frameworks that actually drive decisions in operations.
What you’ll learn
- KPI design: leading vs lagging, actionable vs vanity metrics
- Throughput, yield, scrap, downtime, cycle time, lead time
- OEE-style thinking (availability, performance, quality) — practical usage
- Metric definitions, data contracts, and consistent calculations
- Building KPI scorecards and operational review dashboards
Skills you’ll gain
Module 3 — Diagnostic Analytics (Root-cause & segmentation)
Move from ‘what happened’ to ‘why it happened’ with rigorous analysis.
2–3 weeks
Module 3 — Diagnostic Analytics (Root-cause & segmentation)
Move from ‘what happened’ to ‘why it happened’ with rigorous analysis.
What you’ll learn
- Segmentation: by line, shift, product, supplier, machine, operator
- Pareto & contribution analysis (80/20) for losses & defects
- Root-cause workflow: hypotheses, tests, evidence tracking
- Practical statistics for ops: distributions, confidence, variability
- Communicating findings: narratives that drive action plans
Skills you’ll gain
Module 4 — Forecasting & Capacity Planning
Forecast demand/throughput and evaluate models with business constraints.
2–3 weeks
Module 4 — Forecasting & Capacity Planning
Forecast demand/throughput and evaluate models with business constraints.
What you’ll learn
- Time-series fundamentals: trend/seasonality/events
- Baselines first: moving average, seasonal naive
- Backtesting and evaluation: MAPE/SMAPE + cost of errors
- Forecasting for planning: capacity, inventory, staffing signals
- Communicating uncertainty: intervals and planning recommendations
Skills you’ll gain
Module 5 — Anomaly Detection & Operational Alerts
Detect abnormal behavior and design alerting that avoids noise.
2 weeks
Module 5 — Anomaly Detection & Operational Alerts
Detect abnormal behavior and design alerting that avoids noise.
What you’ll learn
- Rule-based detection vs statistical baselines
- Thresholding strategies: dynamic thresholds, seasonality-aware rules
- False positives/negatives trade-offs + alert fatigue prevention
- Incident context: what to log, what to escalate, what to ignore
- Monitoring mindset: drift, changes, and process shifts
Skills you’ll gain
Module 6 — Reporting & Storytelling for Operations
Deliver dashboards and reports that support daily/weekly operational decisions.
2 weeks
Module 6 — Reporting & Storytelling for Operations
Deliver dashboards and reports that support daily/weekly operational decisions.
What you’ll learn
- Dashboard structure: executive view vs shop-floor view
- Design for actions: targets, exceptions, drill-down, root-cause paths
- Narrative reporting: what changed, why, what to do next
- Data governance basics: metric definitions, ownership, refresh cadence
- Packaging deliverables: dashboard + analysis memo + recommendations
Skills you’ll gain
Capstone — Industrial Analytics Case (End-to-End)
Solve a realistic industrial problem and deliver a complete analytics package.
3–4 weeks
Capstone — Industrial Analytics Case (End-to-End)
Solve a realistic industrial problem and deliver a complete analytics package.
What you’ll learn
- Problem framing + operational KPI selection
- Data cleaning + validation + model/report building
- Forecasting or anomaly detection applied to the case
- Dashboard + executive summary + recommended action plan
Skills you’ll gain