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Master'sNaval, Offshore & Sports Nautical Applications open

Master’s in Digital, Data & Operations Engineering

Online master's programme of 1,900 hours in maritime digital, data and operations engineering. 12 modules covering maritime digital transformation, onboard data architecture, cloud platforms, maritime data analytics, energy efficiency and emissions, fleet operations, data-driven maintenance, digital ports and logistics, digital twins, artificial intelligence and data governance, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design digital transformation strategies.

02

Implement onboard data architectures.

03

Apply maritime data analytics.

04

Calculate efficiency indicators.

05

Optimise fleet operations.

06

Develop data-driven maintenance.

07

Govern data and cybersecurity.

Profile

Who it's for

  • Maritime data analysts.
  • Naval digitalisation engineers.
  • Fleet efficiency managers.
  • Data architects.
  • Fleet operations managers.
  • Digital transformation consultants.
  • Engineering and science graduates seeking to join shipping companies.
Standards & tools

What you'll work with

  • ISO 19847 (shipboard data servers to share field data at sea)
  • ISO 19848 (standard data for shipboard machinery and equipment)
  • Regulation (EU) 2015/757 MRV (monitoring, reporting and verification of CO2 emissions from maritime transport)
  • Regulation (EU) 2023/1805 FuelEU Maritime (use of renewable and low-carbon fuels in maritime transport)
  • MARPOL Annex VI (prevention of air pollution from ships)
  • IACS UR E26 (cyber resilience of ships)
  • ISO/IEC 27001 (information security management systems)
  • Python
  • Power BI
  • Azure IoT
  • Node-RED
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1Digitalisation of maritime transport
  2. 1.2Industry 4.0 in the naval sector
  3. 1.3The maritime data ecosystem
  4. 1.4Players in the digital chain
  5. 1.5Shipping company transformation strategies
  6. 1.6Organisational and cultural barriers
  7. 1.7Success stories in shipping companies
  8. 1.8Return on digital investment
  9. 1.9Digitalisation roadmap
  10. 1.10Case study: digital diagnosis of a shipping company
  1. 2.1Ship data sources
  2. 2.2Automation systems
  3. 2.3NMEA and Modbus marine protocols
  4. 2.4Data gateways
  5. 2.5Edge computing on board
  6. 2.6Satellite connectivity
  7. 2.7Data storage
  8. 2.8Data quality
  9. 2.9ISO 19847 and ISO 19848 standards
  10. 2.10Laboratory: data gateway with Node-RED
  1. 3.1Industrial IoT platforms
  2. 3.2AWS and Azure cloud services
  3. 3.3Maritime data lakes
  4. 3.4APIs and services
  5. 3.5Systems integration
  6. 3.6Cloud security
  7. 3.7Data governance
  8. 3.8Platform costs
  9. 3.9Platform selection
  10. 3.10Practical exercise: data ingestion into a cloud platform
  1. 4.1Descriptive statistics of operational data
  2. 4.2Data visualisation
  3. 4.3Time series analysis
  4. 4.4Anomaly detection
  5. 4.5Supervised machine learning
  6. 4.6Predictive fuel consumption models
  7. 4.7Consumption analytics
  8. 4.8Hull performance analytics
  9. 4.9Dashboards
  10. 4.10Workshop: fuel consumption analysis with Python
  1. 5.1EEXI and CII indicators
  2. 5.2Fuel consumption monitoring
  3. 5.3EU MRV Regulation
  4. 5.4Maritime emissions trading system
  5. 5.5FuelEU Maritime regulation requirements
  6. 5.6Speed optimisation
  7. 5.7Trim optimisation
  8. 5.8Hull performance
  9. 5.9Emissions reporting
  10. 5.10Case study: calculating a ship's CII
  1. 6.1Fleet management
  2. 6.2Voyage planning
  3. 6.3Route optimisation
  4. 6.4Crew management
  5. 6.5Maintenance management
  6. 6.6Procurement management
  7. 6.7Operations centres
  8. 6.8Ship-to-shore communication
  9. 6.9Fleet operational indicators
  10. 6.10Practical exercise: fleet dashboard
  1. 7.1Fundamentals of predictive maintenance
  2. 7.2Condition monitoring
  3. 7.3Vibration analysis
  4. 7.4Oil analysis
  5. 7.5Remaining useful life models
  6. 7.6Integration with maintenance systems
  7. 7.7Dry-dock planning
  8. 7.8Classification societies and data
  9. 7.9Use cases in main engines
  10. 7.10Laboratory: remaining useful life model
  1. 8.1Port call optimisation
  2. 8.2Just-in-time arrival
  3. 8.3Port community systems
  4. 8.4European Maritime Single Window
  5. 8.5Container traceability
  6. 8.6Blockchain in logistics
  7. 8.7Exploiting AIS data
  8. 8.8Port-ship data integration
  9. 8.9Port efficiency indicators
  10. 8.10Workshop: port call analysis with AIS data
  1. 9.1The digital twin concept
  2. 9.2Physics-based and data-driven models
  3. 9.3Simulation of operations
  4. 9.4System twins
  5. 9.5Fleet twins
  6. 9.6Validation of digital twins
  7. 9.7Commercial digital twin platforms
  8. 9.8Digital twin use cases in shipping companies
  9. 9.9Implementation challenges
  10. 9.10Practical exercise: simple digital twin of fuel consumption
  1. 10.1AI in navigation
  2. 10.2Computer vision for surveillance
  3. 10.3Natural language processing
  4. 10.4AI in maintenance
  5. 10.5AI in commercial operations
  6. 10.6AI ethics
  7. 10.7AI regulation
  8. 10.8Project implementation
  9. 10.9Model management
  10. 10.10Case study: AI project in a shipping company
  1. 11.1Cyber risks of digitalisation
  2. 11.2IACS cyber resilience requirements
  3. 11.3Data security
  4. 11.4Data privacy
  5. 11.5Data governance
  6. 11.6Change management
  7. 11.7Digital training for crews
  8. 11.8Maturity indicators
  9. 11.9Audits of digital systems
  10. 11.10Workshop: digital maturity assessment
  1. 12.1Definition of the case
  2. 12.2Inventory of available data
  3. 12.3Proposed data architecture
  4. 12.4Development of analytical models
  5. 12.5Results and validation
  6. 12.6Implementation plan
  7. 12.7Economic analysis of the project
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme can be combined with full-time work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python, Power BI, Azure IoT, Node-RED, Grafana, in free, academic or evaluation versions. Practical work is studied on real procedures for analysis of ship operational data, emissions indicator calculation and dashboard development. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ISO 19847, ISO 19848, Regulation (EU) 2015/757 MRV, Regulation (EU) 2023/1805 FuelEU Maritime, MARPOL Annex VI, IACS UR E26, ISO/IEC 27001.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 19847, ISO 19848 and Regulation (EU) 2015/757 MRV applied to real cases, not as a theoretical annex.
Industry software:Python, Power BI, Azure IoT, Node-RED, Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A complete maritime data project, from data inventory and architecture to analytical models, implementation plan and economic analysis, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Onboard data gateway
Build an onboard data gateway with Node-RED.
Fuel consumption analysis
Analyse a ship's fuel consumption with Python.
CII calculation
Calculate a ship's Carbon Intensity Indicator.
Fleet dashboard
Develop an operational dashboard for a fleet.
Master's Thesis
A complete maritime data project, from data inventory and architecture to analytical models, implementation plan and economic analysis, defended before a panel.
Career opportunities

Where this takes you

Maritime data analystNaval digitalisation engineerFleet efficiency managerData architectFleet operations managerDigital transformation consultant
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python, Power BI, Azure IoT, Node-RED, Grafana, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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