Data Analytics for Marine Engineers: From Sensors to Engineering Decisions

Introduction
Data is the “New Oil” of the maritime industry, but it is useless without refinement. This course develops the capabilities for “Evidence-Based Management” (EBM), in which engineers use advanced analytics to move beyond “gut-feel” and into data-driven decision-making. These regions utilise the DIKW Pyramid (Data, Information, Knowledge, Wisdom) to transform raw sensor telemetry into actionable wisdom for the fleet.

In emerging economies, data analytics is a tool for “Resource Optimisation.” In environments where budgets are tight, AI-driven analytics help managers decide where to allocate their limited maintenance or fuel budgets to maximise return. This course teaches how to manage “Data Quality” in regions where sensor infrastructure may be inconsistent or prone to environmental interference.
This course concludes by exploring how to re-engineer your firm’s culture to be data-first, ensuring you are ready for the competitive maritime landscape of 2026 and beyond.

Learning Outcomes
1. Apply data analytics to create an evidence-based engineering decision environment.
2. Develop data-driven strategies that optimise fleet performance and fuel spend.
3. Implement “Decision-Support Systems” that reduce human error in critical engineering repairs.
4. Formulate data-governance policies that ensure “Data Quality” from marine sensors.
5. Evaluate the impact of data analytics on the firm’s overall global competitive advantage.

Learning Objectives
1. Interpret marine sensor data using advanced statistical and visualisation tools.
2. Apply analytics to engineering decision models using Evidence-Based Management (EBM).
3. Develop data-driven engineering strategies that align with Digital Workplace Trends.
4. Map the “DIKW” (Data, Information, Knowledge, Wisdom) hierarchy to ship telemetry.
5. Construct a fleet-wide performance dashboard using real-time operational data.