Postgraduate Diploma in Data Platforms and Intelligent Alerting
Online 800 h Postgraduate Diploma in Data Platforms and Intelligent Alerting. 8 modules covering ISO 19847 (onboard data servers), ISO 19848 (data standards for ship machinery and equipment) and Apache Kafka. Dedicated tutor and enrolment open.
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What you will be able to do
Identify the sources of data in maritime operations and their characteristics.
Design scalable ship-to-shore data platform architectures.
Build data ingestion, cleaning and processing pipelines.
Define alerting systems with thresholds, priorities and escalation.
Apply advanced analytics to reduce false alarms.
Design dashboards oriented towards operational decision-making.
Incorporate cybersecurity and reliability requirements into the platform.
Who it's for
- Maritime data platform architect who wants to specialise in Data Platforms and Intelligent Alerting.
- Data engineer who wants to specialise in Data Platforms and Intelligent Alerting.
- Alerting systems specialist who wants to specialise in Data Platforms and Intelligent Alerting.
- Fleet operations analyst who wants to specialise in Data Platforms and Intelligent Alerting.
- Maritime cybersecurity manager who wants to specialise in Data Platforms and Intelligent Alerting.
- Systems reliability engineer who wants to specialise in Data Platforms and Intelligent Alerting.
- Engineering or science graduates seeking to enter shipping companies.
What you'll work with
- ISO 19847 (onboard data servers)
- ISO 19848 (data standards for ship machinery and equipment)
- IACS UR E26 and E27 (ship and system cyber resilience)
- IMO MSC-FAL.1/Circ.3 guidelines (maritime cyber risk management)
- IEC 62443 (industrial cybersecurity)
- ISO/IEC 27001 (information security)
- Directive (EU) 2022/2555 (NIS2)
8 modules · 800 h
1.1Ship and fleet data ecosystem1.2Sensor time-series data1.3Event, alarm and maintenance data1.4AIS, meteorological and port data1.5ISO 19847 and ISO 19848 standards1.6Naval ontologies and data models1.7Data quality and governance1.8Latency and volume requirements1.9Intelligent alerting use cases1.10Case study: data map of a ro-ro ship
2.1Lambda and kappa architectures2.2Data lake, data warehouse and lakehouse2.3Batch and streaming ingestion2.4Apache Kafka and message queues2.5Time-series databases: InfluxDB and TimescaleDB2.6Cloud services: Azure, AWS and GCP2.7Ship-to-shore synchronisation with low connectivity2.8Scalability and costs2.9Security and access control2.10Laboratory: ingestion channel with Kafka and TimescaleDB
3.1Cleaning and handling missing values3.2Resampling and temporal alignment3.3Noise and outlier filtering3.4Aggregations and time windows3.5Labelling operational states3.6Pipelines with Apache Spark3.7Orchestration with Airflow3.8Data versioning and lineage3.9Automated quality testing3.10Practice: engine data cleaning pipeline
4.1Alarm philosophy and the ISA-18.2 standard4.2IMO Bridge Alert Management (BAM)4.3Priorities and alert classification4.4Alarm flooding and alert fatigue4.5Static versus dynamic thresholds4.6Rule-based alerts4.7Escalation and notification of people4.8Acknowledgement, suppression and closure of alerts4.9Alerting effectiveness metrics4.10Workshop: engine room alarm rationalisation
5.1Statistical anomaly detection5.2Isolation models and autoencoders5.3Regime change detection5.4Failure prediction models5.5Alarm correlation and root cause analysis5.6Complex event processing5.7Reducing false positives5.8Alert explainability5.9Model deployment in production (MLOps)5.10Practice: autoencoder anomaly detector
6.1Alert consoles for shore operators6.2Integration with the operations centre6.3Grafana dashboards6.4Mobile and email notifications6.5Investigation workflows6.6Integration with planned maintenance6.7User feedback to models6.8Ergonomics and human factors6.9Periodic alert reports6.10Case study: alert console for 30 ships
7.1Data platform cybersecurity7.2IACS UR E26 and E27 requirements7.3Protection of crew personal data7.4Availability and disaster recovery7.5Platform monitoring7.6Service level agreements7.7Auditing and traceability7.8Software validation for class7.9Technology supplier management7.10Practice: platform continuity plan
8.1Use case definition8.2Data source inventory8.3Architecture design8.4Data pipeline8.5Alerting rules and models8.6User interface and notifications8.7Validation plan8.8Deployment and operations plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM Institute campus, with downloadable materials, recorded classes and a dedicated technical tutor. There are no fixed timetables: students progress at their own pace within the intake period, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, InfluxDB, Grafana, Python, Elasticsearch, in free, academic or evaluation versions. Tests are studied using real procedures for designing a ship-to-shore data architecture, configuring alerting rules on real data, and simulating cybersecurity incidents. Assessment is continuous per module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the sources of data in maritime operations and their characteristics.
Design scalable ship-to-shore data platform architectures.
Build data ingestion, cleaning and processing pipelines.
Define alerting systems with thresholds, priorities and escalation.
Apply advanced analytics to reduce false alarms.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses Apache Kafka, InfluxDB, Grafana, Python and Elasticsearch. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM Institute own diploma upon passing continuous assessment and the integrating project. It 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. Please check the recognition policy when you enrol.
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