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Master'sAutonomous, ADAS & Software-Defined Vehicle Applications open

Master’s in Diagnostics, Telemetry and Edge Analytics Engineering

Online master's programme of 1,900 hours in diagnostics, telemetry and edge analytics engineering, structured in 12 modules covering on-board diagnostics, vehicle telemetry, edge analytics, cloud data platforms, diagnostic analytics, anomaly detection, prognostics, the vehicle digital twin and data regulation (ISO 14229-1, ISO 13400-2, ISO 15031), 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

Apply on-board diagnostic protocols.

02

Design efficient telemetry systems.

03

Implement analytics at the vehicle edge.

04

Build cloud data platforms.

05

Develop anomaly detection and prognostic models.

06

Comply with privacy and data access requirements.

07

Develop a diagnostics, telemetry and edge analytics project.

Profile

Who it's for

  • Diagnostics engineers expanding into fleet-level data analysis.
  • Telematics engineers designing vehicle data collection.
  • Vehicle data scientists building anomaly and prognostic models.
  • Edge computing engineers deploying analytics on board.
  • Field quality analysts working with DTC and warranty data.
  • Data platform architects designing cloud pipelines for vehicle data.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 14229-1 (unified diagnostic services (UDS))
  • ISO 13400-2 (diagnostic communication over IP (DoIP))
  • ISO 15031 (OBD emissions-related diagnostic communication)
  • ISO 22900-2 (modular vehicle communication interface (D-PDU API))
  • Regulation (EU) 2023/2854 (Data Act) (EU rules on fair access to and use of data)
  • Regulation (EU) 2016/679 (GDPR) (EU data protection regulation)
  • Regulation (EU) 2019/631 (OBFCM) (on-board fuel and energy consumption monitoring)
  • ISO 20078 (extended vehicle web services)
  • Vector CANoe
  • AWS IoT FleetWise
  • Apache Kafka
  • Grafana
  • Python
Syllabus

12 modules · 1900 h

  1. 1.1Data sources in the vehicle
  2. 1.2CAN and Ethernet bus signals
  3. 1.3Diagnostic and event data
  4. 1.4Vehicle data use cases
  5. 1.5Value for engineering, quality and services
  6. 1.6Collection architectures
  7. 1.7Data transmission costs
  8. 1.8Regulation of data access
  9. 1.9The data provider ecosystem
  10. 1.10Case study: map of data use cases
  1. 2.1The UDS protocol and services
  2. 2.2DTC management and freeze frames
  3. 2.3Diagnostics over IP
  4. 2.4OBD and emissions diagnostics
  5. 2.5Diagnostics of high-voltage systems
  6. 2.6ODX and OTX models
  7. 2.7Distributed diagnostics in zonal architectures
  8. 2.8Diagnostic authentication
  9. 2.9Diagnostics of software and services
  10. 2.10Lab: reading DTCs with UDS
  1. 3.1TCU telematics units
  2. 3.2MQTT and AMQP protocols
  3. 3.3Data compression and prioritisation
  4. 3.4Campaign-based collection
  5. 3.5VSS signal models
  6. 3.6Time synchronisation of data
  7. 3.74G, 5G and satellite connectivity
  8. 3.8Handling connection losses
  9. 3.9Connectivity costs per vehicle
  10. 3.10Practical: telemetry with AWS IoT FleetWise
  1. 4.1Data processing at the edge
  2. 4.2In-vehicle aggregation and filtering
  3. 4.3On-board event detection
  4. 4.4Lightweight machine learning models
  5. 4.5TinyML on microcontrollers
  6. 4.6Execution in on-board containers
  7. 4.7Remote rule updates
  8. 4.8On-board resource consumption
  9. 4.9Balance between edge and cloud
  10. 4.10Workshop: on-board anomaly detector
  1. 5.1Streaming ingestion with Kafka
  2. 5.2Time-series databases
  3. 5.3Lakehouse for vehicle data
  4. 5.4Processing with Spark
  5. 5.5Data catalogues and governance
  6. 5.6Queries at scale
  7. 5.7Visualisation with Grafana
  8. 5.8Platform costs
  9. 5.9Data security
  10. 5.10Lab: Kafka pipeline to a time-series database
  1. 6.1DTC analysis at fleet scale
  2. 6.2Correlation of failures and conditions
  3. 6.3Early defect detection
  4. 6.4Root cause analysis
  5. 6.5Prioritisation of quality problems
  6. 6.6Warranty analysis
  7. 6.7Guided diagnostics for workshops
  8. 6.8Feedback to engineering
  9. 6.9Field quality indicators
  10. 6.10Practical: DTC analysis of a fleet
  1. 7.1Statistical anomaly methods
  2. 7.2Reconstruction-based models
  3. 7.3Isolation Forest and variants
  4. 7.4Recurrent networks for time series
  5. 7.5Multivariate anomaly detection
  6. 7.6Labelling and validation of anomalies
  7. 7.7Reducing false alarms
  8. 7.8Explainability of anomalies
  9. 7.9Deployment in production
  10. 7.10Workshop: autoencoder for engine signals
  1. 8.1Remaining useful life models
  2. 8.2Battery state of health
  3. 8.3Brake and tyre degradation
  4. 8.4Physics-based versus data-driven models
  5. 8.5Hybrid prognostic models
  6. 8.6Prognostic uncertainty
  7. 8.7Integration with workshop planning
  8. 8.8Economic assessment of maintenance
  9. 8.9Commercial fleet cases
  10. 8.10Lab: battery SOH prognosis
  1. 9.1Concept of the vehicle digital twin
  2. 9.2State synchronisation
  3. 9.3Physical and data models
  4. 9.4Simulation of usage scenarios
  5. 9.5Battery twin
  6. 9.6Twin for maintenance
  7. 9.7Twin platforms
  8. 9.8Scaling to the fleet
  9. 9.9Limitations of the twin
  10. 9.10Practical: battery twin with fleet data
  1. 10.1GDPR in telemetry
  2. 10.2The Data Act and user access
  3. 10.3Consent and legal bases
  4. 10.4Anonymisation of location data
  5. 10.5Data for independent repair
  6. 10.6OBFCM real-world consumption requirements
  7. 10.7EDR event data recorders
  8. 10.8Information security
  9. 10.9International data transfers
  10. 10.10Case study: telemetry impact assessment
  1. 11.1Data-driven fleet services
  2. 11.2Usage-based insurance
  3. 11.3Proactive maintenance services
  4. 11.4Vehicle data marketplaces
  5. 11.5Data for cities and traffic
  6. 11.6Monetisation and pricing
  7. 11.7Third-party partnerships
  8. 11.8Value metrics
  9. 11.9Business risks
  10. 11.10Workshop: proposal for a data-driven service
  1. 12.1Definition of use cases
  2. 12.2Design of on-board collection
  3. 12.3Edge analytics strategy
  4. 12.4Cloud platform architecture
  5. 12.5Anomaly detection models
  6. 12.6Prognostic model
  7. 12.7Privacy analysis
  8. 12.8Conclusions and roadmap
  9. 12.9Thesis 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. There are no fixed timetables: you progress at your own pace within each intake, making it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Vector CANoe, AWS IoT FleetWise, Apache Kafka, Grafana and Python, in free, academic or evaluation versions. Testing is studied through real procedures: DTC analysis at fleet scale, anomaly detection in vehicle signals and battery state-of-health prognosis. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ISO 14229-1, ISO 13400-2, ISO 15031, ISO 22900-2, Regulation (EU) 2023/2854 (Data Act), Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2019/631 (OBFCM), ISO 20078.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 14229-1, ISO 13400-2 and ISO 15031 applied to real cases, not as a theoretical annex.
Industry software:Vector CANoe, AWS IoT FleetWise, Apache Kafka, Grafana and Python, in free, academic or evaluation versions.
Master's Thesis with defence:a complete diagnostics, telemetry and edge analytics project, from on-board collection and edge strategy to cloud platform, anomaly detection, prognostics and privacy analysis, 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.

Diagnostic data capture
Read DTCs with UDS and design campaign-based telemetry with AWS IoT FleetWise.
Edge analytics
Build an on-board anomaly detector with lightweight models.
Cloud data pipeline
Stream data with Kafka into a time-series database and visualise it in Grafana.
Anomaly and prognostic models
Train an autoencoder for engine signals and a battery SOH prognosis model.
Master's Thesis
A complete diagnostics, telemetry and edge analytics project, from on-board collection and edge strategy to cloud platform, anomaly detection, prognostics and privacy analysis, defended before a panel.
Career opportunities

Where this takes you

Diagnostics engineerTelematics engineerVehicle data scientistEdge computing engineerField quality analystData platform architect
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 Vector CANoe, AWS IoT FleetWise, Apache Kafka, Grafana and Python, 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 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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