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Master'sOff-Road, Special Vehicles & Machinery Applications open

Master’s in Digital, Telematics & Data Engineering

Online master's programme of 1,900 hours in digital, telematics and data engineering for off-highway machinery. 12 modules covering digital transformation, machine electronic architecture, telematics, data engineering, analytics and machine learning, fleet management, digital twins, digital agriculture and construction, cybersecurity, digital services and project management, 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 off-highway digital transformation strategies.

02

Understand the electronic and telematics architecture of the machine.

03

Build telemetry data pipelines.

04

Apply analytics and machine learning to fleet data.

05

Develop digital twins and dashboards.

06

Manage the cybersecurity of connected machines.

07

Design digital services and business models.

Profile

Who it's for

  • Machinery data engineers
  • Digital product managers
  • Telematics analysts
  • Industrial data scientists
  • IoT solutions architects
  • Connected fleet managers
  • Engineering or science graduates looking to enter the construction machinery sector
Standards & tools

What you'll work with

  • SAE J1939 (heavy-vehicle communication network)
  • ISO 11783 (ISOBUS)
  • ISO 15143-3 (telematics data exchange for machinery)
  • IEC 62443 (industrial cybersecurity)
  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2024/2847 (Cyber Resilience Act)
  • Regulation (EU) 2016/679 (General Data Protection Regulation)
  • Python with pandas
  • Apache Kafka
  • Microsoft Power BI
  • InfluxDB
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1Connected machines and digital services
  2. 1.2The data value chain
  3. 1.3High-impact use cases
  4. 1.4Data-driven business models
  5. 1.5Data ownership and access
  6. 1.6The European Data Act
  7. 1.7Digital maturity of manufacturers and users
  8. 1.8Transformation roadmaps
  9. 1.9Common mistakes in digital projects
  10. 1.10Case study: digital roadmap for a manufacturer
  1. 2.1Control units and on-board networks
  2. 2.2CAN and SAE J1939 protocols
  3. 2.3ISOBUS in agricultural machinery
  4. 2.4In-vehicle Ethernet
  5. 2.5On-board telematics gateways
  6. 2.6Connected sensors and actuators
  7. 2.7On-board diagnostics
  8. 2.8Over-the-air software updates
  9. 2.9Centralised architecture trends
  10. 2.10Lab: reading J1939 messages
  1. 3.1Factory-fitted telematics units
  2. 3.24G and 5G mobile networks
  3. 3.3Satellite connectivity
  4. 3.4Data frequency and volume
  5. 3.5ISO 15143-3 machine data interface
  6. 3.6Mixed fleets from several manufacturers
  7. 3.7Connectivity costs
  8. 3.8Data quality
  9. 3.9Communications security
  10. 3.10Practical exercise: integrating the ISO 15143-3 API
  1. 4.1Real-time data ingestion
  2. 4.2Time-series databases
  3. 4.3Data lakes and data warehouses
  4. 4.4Batch and stream processing
  5. 4.5Machine data modelling
  6. 4.6Data quality and cleansing
  7. 4.7Data governance
  8. 4.8Cloud platforms
  9. 4.9Storage and compute costs
  10. 4.10Lab: telemetry data pipeline
  1. 5.1Descriptive statistics of fleets
  2. 5.2Data visualisation
  3. 5.3Anomaly detection
  4. 5.4Predictive failure models
  5. 5.5Fuel consumption optimisation
  6. 5.6Classification of duty cycles
  7. 5.7Machine learning in production
  8. 5.8Model interpretability
  9. 5.9Ethics and bias
  10. 5.10Practical exercise: fuel consumption model in Python
  1. 6.1Location and geofencing
  2. 6.2Hours of use and utilisation
  3. 6.3Fuel consumption
  4. 6.4Maintenance alerts
  5. 6.5Productivity per machine
  6. 6.6Operator behaviour
  7. 6.7Rental management
  8. 6.8Integration with CMMS and ERP
  9. 6.9Fleet indicators
  10. 6.10Case study: dashboard for a mixed fleet
  1. 7.1The digital twin concept
  2. 7.2Component, machine and site twins
  3. 7.3Physics-based and data-driven models
  4. 7.4Synchronisation with real data
  5. 7.5Scenario simulation
  6. 7.6Twins for maintenance
  7. 7.7Twins for operations
  8. 7.8Digital twin platforms
  9. 7.9Return on investment
  10. 7.10Lab: simplified digital twin of a machine
  1. 8.1Data-driven precision agriculture
  2. 8.2Yield and prescription maps
  3. 8.3Farm management information systems
  4. 8.4Machine control in construction
  5. 8.5Integration with BIM
  6. 8.6Drone surveying
  7. 8.7Tracking construction progress
  8. 8.8Format interoperability
  9. 8.9Success stories
  10. 8.10Practical exercise: variable-rate prescription map
  1. 9.1Threats to connected machines
  2. 9.2IEC 62443 industrial cybersecurity
  3. 9.3UNECE Regulation No. 155
  4. 9.4The European Cyber Resilience Act
  5. 9.5Cybersecurity in the Machinery Regulation
  6. 9.6Threat analysis and risk assessment
  7. 9.7Vulnerability management
  8. 9.8Incident response
  9. 9.9Cloud security
  10. 9.10Case study: TARA analysis of a telematics unit
  1. 10.1Designing digital services
  2. 10.2User experience in applications
  3. 10.3Subscription models
  4. 10.4Maintenance as a service
  5. 10.5Pay-per-use
  6. 10.6Data marketplaces
  7. 10.7Technology partnerships with third parties
  8. 10.8Digital product metrics
  9. 10.9Scaling services
  10. 10.10Practical exercise: subscription service proposal
  1. 11.1Agile development methodologies
  2. 11.2Multidisciplinary data teams
  3. 11.3Managing data requirements
  4. 11.4Prototypes and proofs of concept
  5. 11.5Scaling pilots
  6. 11.6Organisational change management
  7. 11.7Selecting technology suppliers
  8. 11.8Budgets for digital projects
  9. 11.9Assessing the value generated
  10. 11.10Case study: scaling a telematics pilot
  1. 12.1Definition of the use case
  2. 12.2Data architecture
  3. 12.3Telematics integration
  4. 12.4Development of the analytical model
  5. 12.5Dashboard
  6. 12.6Cybersecurity analysis
  7. 12.7Business model
  8. 12.8Implementation plan
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
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: you progress at your own pace within the intake, so the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with pandas, Apache Kafka, Microsoft Power BI, InfluxDB and Grafana, in free, academic or evaluation versions. Testing is studied through real procedures for telematics data integration, fleet dashboard development and predictive models using machine data. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: SAE J1939, ISO 11783 (ISOBUS), ISO 15143-3, IEC 62443, Regulation (EU) 2023/2854, Regulation (EU) 2024/2847, Regulation (EU) 2016/679.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of SAE J1939, ISO 11783 (ISOBUS) and ISO 15143-3 applied to real cases, not as a theoretical annex.
Industry software:Python with pandas, Apache Kafka, Microsoft Power BI, InfluxDB and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A complete connected-machinery data project, from use case to implementation plan, 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 architecture
Define the use case and design the data architecture.
Telematics integration
Integrate machine telematics data into the pipeline.
Analytical model and dashboard
Develop the analytical model and the fleet dashboard.
Cybersecurity and business model
Analyse cybersecurity and define the business model and implementation plan.
Master's Thesis
Present the complete connected-machinery project, defended before a panel.
Career opportunities

Where this takes you

Machinery data engineerDigital product managerTelematics analystIndustrial data scientistIoT solutions architectConnected fleet 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 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 with pandas, Apache Kafka, Microsoft Power BI, InfluxDB and 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 degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme 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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