Master’s in Industrial Edge and Data Engineering
Online master's programme of 1,900 hours in industrial edge and data engineering (time series, historians, pipelines and BI). 12 modules covering plant data, connectivity and acquisition, industrial edge platforms, stream processing, industrial data storage, data engineering and ETL, process analytics, industrial machine learning, MLOps, cybersecurity and data governance, and architecture and strategy, with an assigned technical tutor.
Master’s in Industrial Edge and Data Engineering — studying from Ireland
Ireland has a substantial electronics and telecommunications sector, with multinational semiconductor, networking and communications firms operating design, test and engineering centres across the country. Universities such as Trinity College Dublin, University College Cork and the Tyndall National Institute contribute research in microelectronics, photonics and communications. Engineers in this area design embedded systems, firmware, RF systems and telecommunications networks for industrial and consumer applications. Standards and spectrum management are overseen by national telecommunications and standards bodies.
Fees for Ireland: €7,920 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Ireland
Typical roles
Questions from Ireland
Which Irish bodies recognise electronics and telecom engineering qualifications?
Engineers Ireland is the main professional body for engineers and runs chartered registration. Telecoms and spectrum matters are regulated by ComReg, the Commission for Communications Regulation. SEIUM's qualifications are private and not state-awarded, and they are not accredited by any national body, so recognition depends on employers or the relevant body.
Do Irish electronics and telecom employers value private online study?
Ireland has a strong electronics, semiconductor and telecoms presence, and employers there usually emphasise hands-on competence, certifications where relevant and experience. A private online qualification may add to your profile, but its value is decided by each employer, and no outcome can be promised. Show practical embedded or RF work alongside it.
Is it feasible alongside work, and what should I expect from the tutoring?
The programme is 100% online and taught in English, so it suits working professionals who can manage their own time. No student visa is needed to study from Ireland. Master's programmes total 1,900 hours, postgraduate diplomas 800 and specialist courses 300. Each student is assigned a technical tutor, fees can be paid in instalments, and employer sponsorship may be possible.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Design edge-cloud industrial data architectures.
Integrate plant sources with OPC UA, MQTT and Unified Namespace.
Build streaming and batch processing pipelines.
Model and store industrial data in time-series databases.
Develop machine learning models for processes.
Operate models in production with MLOps practices.
Govern and protect industrial data in line with European regulations.
Who it's for
- Industrial data engineers.
- Edge architects.
- Process data scientists.
- MLOps engineers working with plant data.
- Digital transformation managers.
- IT/OT integration engineers.
- Engineering or science graduates aiming to enter discrete manufacturing.
What you'll work with
- IEC 62264 (ISA-95) (enterprise-control system integration)
- IEC 62541 (OPC UA) (unified architecture for industrial communication)
- IEC 62443-3-3 (system security requirements and security levels)
- DIN SPEC 91345 (RAMI 4.0) (Reference Architecture Model Industrie 4.0)
- Regulation (EU) 2023/2854 (Data Act) (fair access to and use of data)
- Regulation (EU) 2024/1689 (AI Act) (harmonised rules on artificial intelligence)
- Eclipse Sparkplug 3.0 (MQTT payload and state specification for industrial data)
- Apache Kafka
- InfluxDB
- Apache Airflow
- Node-RED
- MLflow
12 modules
1.1Data sources: PLC, SCADA, MES and historians1.2The ISA-95 automation pyramid1.3Data types: time series, events and batches1.4Volume, velocity and variety in the factory1.5IT and OT convergence1.6Industrial analytics use cases1.7RAMI 4.0 reference architectures1.8Digital maturity of the plant1.9Industrial data governance1.10Case study: data map of a packaging line
2.1OPC UA: information model and security2.2MQTT and Sparkplug B2.3Modbus, PROFINET and EtherNet/IP protocols2.4Connectors for PI and similar historians2.5Sampling, deadband and compression2.6Industrial protocol gateways2.7Unified Namespace as a pattern2.8Clock synchronisation2.9Tag management and naming conventions2.10Lab: Unified Namespace with OPC UA and MQTT
3.1Edge hardware for industrial environments3.2Docker containers in the plant3.3Orchestration with K3s and KubeEdge3.4Remote management of edge nodes3.5Siemens Industrial Edge and similar platforms3.6Real-time processing at the edge3.7Local storage and tolerance to disconnection3.8Edge node security3.9Application updates3.10Practical exercise: deploying an edge stack with Docker Compose
4.1Lambda and kappa architectures4.2Apache Kafka and partitioning4.3Processing with Kafka Streams and Flink4.4Time windows and aggregations4.5Complex event detection4.6State management in streaming4.7Delivery guarantees4.8Latency and throughput4.9Pipeline monitoring4.10Lab: streaming aggregation with Apache Flink
5.1Time-series databases: InfluxDB and TimescaleDB5.2Process historians5.3Data lakes and Parquet formats5.4Lakehouse with Delta Lake and Iceberg5.5Asset data modelling5.6Retention and downsampling5.7Data catalogues and lineage5.8Storage costs5.9Backup and recovery5.10Practical exercise: data model in TimescaleDB
6.1Design of ETL and ELT pipelines6.2Orchestration with Apache Airflow6.3Transformations with dbt6.4Data quality and validation6.5Contextualisation with MES and ERP data6.6Asset master data6.7Data versioning6.8Pipeline testing6.9Documentation and data contracts6.10Lab: Airflow pipeline for production data
7.1OEE indicators and their calculation7.2Statistical process control7.3Data-driven root cause analysis7.4Batch analytics7.5Energy consumption analysis7.6Correlation of process variables7.7Dashboards for operations and management7.8Automated report generation7.9Data culture in the plant7.10Case study: improving OEE with real data
8.1Preparation of sensor data8.2Time-based feature engineering8.3Regression and classification models8.4Unsupervised anomaly detection8.5Predictive maintenance models8.6Soft sensors for quality variables8.7In-line machine vision8.8Optimised inference at the edge8.9Model explainability8.10Practical exercise: soft sensor for product quality
9.1Model lifecycle9.2Experiment tracking with MLflow9.3Model registry and versioning9.4Deployment at the edge and in the cloud9.5Drift monitoring9.6Automatic model retraining9.7Model testing9.8Governance and the AI Act9.9Scaling to multiple plants9.10Lab: deploying a model with MLflow
10.1Segmentation according to IEC 6244310.2Secure data flow from OT to IT10.3Identity and access management10.4Encryption in transit and at rest10.5The European Data Act10.6Data sovereignty and data spaces10.7Information classification10.8Audit and traceability10.9Response to data incidents10.10Workshop: design of an industrial data DMZ
11.1Hybrid edge-cloud architectures11.2Industrial cloud platforms11.3Vendor selection and open standards11.4Total cost and return11.5Scaling pilots to production11.6Data teams and roles11.7Change management in the plant11.8Transformation roadmap11.9Success indicators11.10Case study: industrial data roadmap
12.1Selection of the industrial process12.2Analysis of sources and requirements12.3Edge and data architecture12.4Implementation of data acquisition12.5Processing and storage pipelines12.6Analytics or predictive model12.7Deployment and monitoring12.8Impact assessment12.9Master's Thesis report12.10Master's Thesis: defence before a panel
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: you progress at your own pace within the 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, InfluxDB, Apache Airflow, Node-RED and MLflow, in free, academic or evaluation versions. Real procedures are studied: deployment of analytics pilots in the plant, data pipeline validation and monitoring of models in production. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: IEC 62264 (ISA-95), IEC 62541 (OPC UA), IEC 62443-3-3, DIN SPEC 91345 (RAMI 4.0), Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2024/1689 (AI Act), Eclipse Sparkplug 3.0.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer able to run the programme 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.
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, InfluxDB, Apache Airflow, Node-RED and MLflow, 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 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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