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CourseNaval, Offshore & Sports Nautical Applications open

Big Data in Maritime Operations Course

Online 300 h Big Data in maritime operations course. 6 modules covering Regulation (EU) 2019/1239 establishing a European maritime single window environment, Regulation (EU) 2015/757 on the monitoring, reporting and verification of CO₂ emissions from maritime transport, and Apache Spark. Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Identify the data sources and standards of the maritime sector.

02

Design Big Data architectures for operational data.

03

Process large data volumes with distributed tools.

04

Build fleet and port indicators and dashboards.

05

Develop predictive models for maritime operations.

06

Apply data governance principles and data protection.

07

Develop a Big Data project focused on a maritime problem.

Profile

Who it's for

  • Maritime data scientist seeking to specialise in Big Data in maritime operations.
  • Data engineer seeking to specialise in Big Data in maritime operations.
  • Shipping operations analyst seeking to specialise in Big Data in maritime operations.
  • Data governance manager seeking to specialise in Big Data in maritime operations.
  • Digital transformation consultant seeking to specialise in Big Data in maritime operations.
  • Port intelligence analyst seeking to specialise in Big Data in maritime operations.
  • Engineering or science graduates seeking to enter shipping companies.
Standards & tools

What you'll work with

  • Regulation (EU) 2019/1239 establishing a European maritime single window environment
  • Regulation (EU) 2015/757 on the monitoring, reporting and verification of CO₂ emissions from maritime transport
  • MARPOL Convention, Annex VI
  • Regulation (EU) 2016/679 General Data Protection Regulation
  • ISO/IEC 20546 Information technology. Big data. Overview and vocabulary
  • ISO 19848 Ships and marine technology. Standards for machinery and equipment data
Syllabus

6 modules · 300 h

  1. 1.1Data sources: AIS, sensors, meteorology and trade
  2. 1.2Data on vessels, ports and logistics chains
  3. 1.3Volume, velocity and variety of data
  4. 1.4Data quality and governance
  5. 1.5Maritime data standards
  6. 1.6European maritime single window
  7. 1.7Open and commercial data
  8. 1.8Data ownership and sharing
  9. 1.9Success stories in shipping companies and ports
  10. 1.10Practical case: data inventory for a shipping company
  1. 2.1Data lakes and data warehouses
  2. 2.2Lakehouse architecture for maritime data
  3. 2.3Batch and streaming processing
  4. 2.4Apache Spark and distributed processing
  5. 2.5Apache Kafka for events
  6. 2.6Columnar formats: Parquet and Delta
  7. 2.7Cloud platforms
  8. 2.8Data pipeline orchestration
  9. 2.9Security and access control
  10. 2.10Laboratory: AIS data pipeline with Spark
  1. 3.1Fleet performance indicators
  2. 3.2Consumption and emissions analysis
  3. 3.3Terminal productivity
  4. 3.4Time in port and anchorage waiting times
  5. 3.5Route and speed analysis
  6. 3.6Executive dashboards
  7. 3.7MRV and DCS emissions reporting
  8. 3.8Maritime supply chain analysis
  9. 3.9Visualisation of large data volumes
  10. 3.10Workshop: fleet dashboard with Power BI
  1. 4.1Demand and freight rate forecasting
  2. 4.2Arrival time prediction
  3. 4.3Fuel consumption models
  4. 4.4Port congestion prediction
  5. 4.5Predictive maintenance at fleet scale
  6. 4.6Fraud and anomaly detection
  7. 4.7Distributed machine learning
  8. 4.8Model evaluation and deployment
  9. 4.9Model explainability
  10. 4.10Practice: port waiting time prediction model
  1. 5.1Data governance in the organisation
  2. 5.2Protection of personal data of crew members and passengers
  3. 5.3Cybersecurity of data platforms
  4. 5.4Data sharing agreements
  5. 5.5Monetisation of maritime data
  6. 5.6Ethics in data use
  7. 5.7Data strategy for a shipping company
  8. 5.8Roles within the data team
  9. 5.9Measuring the value of data
  10. 5.10Practical case: data strategy for a port
  1. 6.1Business question
  2. 6.2Sources and acquisition
  3. 6.3Platform architecture
  4. 6.4Data processing
  5. 6.5Development of the analytical model
  6. 6.6Visualisation of results
  7. 6.7Governance and privacy
  8. 6.8Production deployment plan
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

Training is 100 % online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the course with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Spark, Apache Kafka, Databricks, Python, Microsoft Power BI, in free, academic or evaluation versions. The practicals are based on real procedures: building an AIS data pipeline, analysing a fleet's consumption and emissions, and predicting port waiting times. Assessment is continuous per module, plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with careers in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

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

Business question

Identify the data sources and standards of the maritime sector.

Sources and acquisition

Design Big Data architectures for operational data.

Platform architecture

Process large data volumes with distributed tools.

Data processing

Build fleet and port indicators and dashboards.

Development of the analytical model

Develop predictive models for maritime operations.

Career opportunities

Where this takes you

Maritime data scientistData engineerShipping operations analystData governance managerDigital transformation consultantPort intelligence analyst
Admissions & scholarships

Requirements

  • No prior qualification required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical regulatory texts.

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 300 hours, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

You will work with Apache Spark, Apache Kafka, Databricks, Python, Microsoft Power BI. Free, academic or evaluation versions are used, and the tutor will indicate the free alternative where one exists.

A SEIUM diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official Ministry qualification.

Yes. Modules passed in a Course or Postgraduate Diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.

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