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Master'sElectronics, Embedded Systems & Telecom Applications open

Master’s in Industrial Edge & Data Engineering

Online master's programme of 1,900 hours in industrial edge and data engineering, structured in 12 modules covering industrial data sources, edge computing, data acquisition and integration, stream processing, storage and modelling, data pipelines, process analytics, edge AI, data governance and hybrid cloud architectures, 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

Characterise the sources and nature of industrial data.

02

Design edge computing architectures.

03

Integrate plant data using OPC UA, MQTT and unified namespaces.

04

Build streaming and batch data pipelines.

05

Apply analytics and machine learning to process data.

06

Establish governance and security policies for industrial data.

07

Develop an edge-cloud data platform for a real case.

Profile

Who it's for

  • Industrial data engineers building pipelines from the plant floor to the cloud.
  • Edge-cloud architects designing hybrid industrial platforms.
  • Industrial MLOps engineers deploying models into production at the edge.
  • Process data analysts looking to move into data engineering.
  • IIoT platform engineers responsible for data acquisition and integration.
  • Digital transformation managers who need a solid technical foundation.
  • Engineering or science graduates aiming to work in manufacturing.
Standards & tools

What you'll work with

  • IEC 62264 (enterprise-control system integration, ISA-95)
  • IEC 62541 (OPC Unified Architecture)
  • IEC 62443-2-1 (security programme requirements for IACS asset owners)
  • Regulation (EU) 2023/2854 (European Data Act)
  • ISO/IEC 25012 (data quality model)
  • ISO 22400-2 (KPIs for manufacturing operations management)
  • Regulation (EU) 2016/679 (General Data Protection Regulation, GDPR)
  • Eclipse Sparkplug 3.0 (MQTT payload and topic specification for industrial data)
  • Apache Kafka
  • Apache Flink
  • InfluxDB
  • Grafana
  • Apache Airflow
Syllabus

12 modules · 1900 h

  1. 1.1OT data sources: PLC, SCADA and sensors
  2. 1.2Characteristics of process data
  3. 1.3Industrial process historians
  4. 1.4Data integration with MES and ERP
  5. 1.5Latency, volume and sampling frequency
  6. 1.6Asset context and ISA-95 hierarchy
  7. 1.7Data quality on the plant floor
  8. 1.8Industrial data use cases
  9. 1.9IT-OT organisational barriers
  10. 1.10Case study: data inventory of a plant
  1. 2.1Edge, fog and cloud architectures
  2. 2.2Industrial edge hardware
  3. 2.3Virtualisation and containers on the plant floor
  4. 2.4Lightweight Kubernetes at the edge
  5. 2.5Virtual PLCs and software-defined control
  6. 2.6Commercial edge platforms
  7. 2.7Determinism and real time at the edge
  8. 2.8Remote management of edge nodes
  9. 2.9Physical and logical security of the node
  10. 2.10Lab: K3s cluster on an industrial gateway
  1. 3.1OPC UA and Modbus connectors
  2. 3.2MQTT payloads with Sparkplug B
  3. 3.3Unified namespace architecture
  4. 3.4Tag normalisation and mapping
  5. 3.5Edge buffering and local storage
  6. 3.6Sampling, compression and deadband
  7. 3.7Time synchronisation in acquisition
  8. 3.8Protocol-to-protocol gateways
  9. 3.9Metadata management
  10. 3.10Practical: unified namespace with HiveMQ
  1. 4.1Lambda and Kappa architectures
  2. 4.2Apache Kafka in industry
  3. 4.3Processing with Flink and Kafka Streams
  4. 4.4Time windows and aggregations
  5. 4.5Complex event processing
  6. 4.6Handling late and out-of-order data
  7. 4.7Real-time threshold detection
  8. 4.8Streaming scalability
  9. 4.9Pipeline monitoring
  10. 4.10Workshop: streaming OEE calculation with Flink
  1. 5.1Time-series engines
  2. 5.2InfluxDB and TimescaleDB
  3. 5.3Data lakes and lakehouses
  4. 5.4Parquet and Delta Lake formats
  5. 5.5Asset and hierarchy modelling
  6. 5.6Plant data twin
  7. 5.7Data catalogue and lineage
  8. 5.8Retention and downsampling
  9. 5.9Analytical query performance
  10. 5.10Practical: industrial lakehouse with Delta Lake
  1. 6.1ETL and ELT process design
  2. 6.2Orchestration with Airflow and Dagster
  3. 6.3Transformations with dbt
  4. 6.4Data validation and testing
  5. 6.5Data and schema versioning
  6. 6.6Reproducible and deterministic pipelines
  7. 6.7Error handling in pipelines
  8. 6.8Continuous deployment of pipelines
  9. 6.9Data documentation
  10. 6.10Lab: batch pipeline with Airflow
  1. 7.1Statistical process control
  2. 7.2Data-driven root cause analysis
  3. 7.3Batch analytics in discontinuous processes
  4. 7.4Correlation of process variables
  5. 7.5Multivariate anomaly detection
  6. 7.6Visualisation with Grafana
  7. 7.7Energy efficiency indicators
  8. 7.8Product quality analysis
  9. 7.9Automated production reports
  10. 7.10Case study: analysis of rejects on a line
  1. 8.1Models for time series
  2. 8.2Predictive maintenance with machine learning
  3. 8.3Model optimisation and compression
  4. 8.4Inference with ONNX Runtime and TensorRT
  5. 8.5Computer vision at the edge
  6. 8.6MLOps for industrial environments
  7. 8.7Model drift and retraining
  8. 8.8Explainability in plant decisions
  9. 8.9Model validation in production
  10. 8.10Practical: deploying an ONNX model at the edge
  1. 9.1Ownership and quality of industrial data
  2. 9.2Access policies and roles
  3. 9.3Security at the IT-OT boundary
  4. 9.4Encryption and secrets management
  5. 9.5Cybersecurity according to IEC 62443
  6. 9.6Industrial data spaces
  7. 9.7Data sovereignty and Gaia-X
  8. 9.8European Data Act
  9. 9.9Auditing and traceability
  10. 9.10Workshop: data governance policy for a plant
  1. 10.1Industrial cloud services
  2. 10.2Secure plant-to-cloud connectivity
  3. 10.3Multi-cloud architectures for industrial data
  4. 10.4Transfer and storage costs
  5. 10.5Commercial industrial data platforms
  6. 10.6Infrastructure as code with Terraform
  7. 10.7Architecture observability
  8. 10.8Disaster recovery
  9. 10.9Architecture selection by use case
  10. 10.10Case study: hybrid migration of a historian
  1. 11.1DevOps for OT
  2. 11.2Node configuration management
  3. 11.3Deployment across multiple plants
  4. 11.4Change management with operations
  5. 11.5Scaling pilot use cases
  6. 11.6Data value indicators
  7. 11.7Training mixed teams
  8. 11.8Technology supplier management
  9. 11.9Transformation roadmap
  10. 11.10Workshop: scaling plan for a pilot
  1. 12.1Definition of the data use case
  2. 12.2Analysis of sources and requirements
  3. 12.3Edge-cloud architecture design
  4. 12.4Pipeline implementation
  5. 12.5Modelling and analytics
  6. 12.6Model deployment at the edge
  7. 12.7Evaluation of value and performance
  8. 12.8Conclusions on the data platform
  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. No fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, Apache Flink, InfluxDB, Grafana and Apache Airflow, in free, academic or evaluation versions. Testing is studied through real procedures: building data pipelines from PLC to cloud, analysing OEE indicators with process data, and deploying machine-learning models on edge nodes. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEC 62264, IEC 62541, IEC 62443-2-1, Regulation (EU) 2023/2854, ISO/IEC 25012, ISO 22400-2, Regulation (EU) 2016/679, Eclipse Sparkplug 3.0.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEC 62264, IEC 62541 and IEC 62443-2-1 applied to real cases, not as a theoretical annex.
Industry software:Apache Kafka, Apache Flink, InfluxDB, Grafana and Apache Airflow, in free, academic or evaluation versions.
Master's Thesis with defence:An edge-cloud data platform for a real industrial case, from pipelines and analytics to model deployment at the edge, 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.

Use case and sources
Define the data use case and analyse its sources and requirements.
Edge-cloud architecture
Design the edge-cloud architecture for acquisition, storage and processing.
Pipelines and analytics
Implement the streaming and batch pipelines and build the analytics.
Edge model deployment
Deploy a machine-learning model on an edge node and validate it.
Master's Thesis
Evaluate value and performance and present the data platform, defended before a panel.
Career opportunities

Where this takes you

Industrial Data EngineerEdge-Cloud ArchitectIndustrial MLOps EngineerProcess Data AnalystIIoT Platform EngineerDigital Transformation Manager
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 Apache Kafka, Apache Flink, InfluxDB, Grafana and Apache Airflow, 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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