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Postgraduate DiplomaHealth, Biomechanics, Wearables & Operational Safety Applications open

Diploma in In-Vehicle Digital Product Metrics and Usage Telemetry

Online postgraduate diploma of 800 hours in in-vehicle digital product metrics and usage telemetry. 8 modules covering in-vehicle product metrics, data protection and vehicle data regulation, product analytics methods, telemetry design, data pipelines, driver experience and distraction metrics, product governance and an integrative case study, with an assigned technical tutor.

Duration800 hours · 6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand how digital products in vehicles are measured and improved.

02

Apply the GDPR, the ePrivacy Directive and the Data Act to vehicle telemetry.

03

Analyse adoption, engagement and retention with Amplitude and Python.

04

Design telemetry specifications and tracking plans based on COVESA VSS.

05

Build streaming data pipelines and dashboards with Kafka and Grafana.

06

Measure driver experience and distraction alongside usage data.

07

Lead data-driven product decisions for in-vehicle digital services.

Profile

Who it's for

  • Product managers of digital cockpit and connected services.
  • Data analysts in the automotive sector.
  • Software engineers working on connected vehicles.
  • UX and HMI designers.
  • Data protection professionals in automotive companies.
  • Telematics engineers.
  • Engineering or data science graduates seeking to enter automotive digital products.
Standards & tools

What you'll work with

  • GDPR (Regulation (EU) 2016/679)
  • ePrivacy Directive 2002/58/EC
  • EDPB Guidelines 01/2020 (connected vehicles)
  • Data Act (EU) 2023/2854
  • ISO 20078 (extended vehicle web services)
  • ISO/SAE 21434 (road vehicle cybersecurity)
  • UN R155 and UN R156
  • ISO/IEC 27001 (information security)
  • ISO 15007 (driver visual behaviour)
  • COVESA Vehicle Signal Specification
  • Python (pandas)
  • Apache Kafka
  • Grafana
  • Amplitude
  • Power BI
Syllabus

8 modules

  1. 1.1Digital products in vehicles: infotainment, apps and connected services
  2. 1.2Product metrics versus vehicle diagnostics
  3. 1.3Telemetry architecture: ECU, TCU and cloud
  4. 1.4Event-based and signal-based data
  5. 1.5North star metrics and KPIs: adoption, engagement and retention
  6. 1.6Usage telemetry and over-the-air updates
  7. 1.7Stakeholders: product, engineering and legal
  8. 1.8Terminology
  9. 1.9Common pitfalls
  10. 1.10Exercise: metrics tree for an in-vehicle app
  1. 2.1GDPR principles applied to vehicles
  2. 2.2Article 5(3) of the ePrivacy Directive
  3. 2.3EDPB guidelines on connected vehicles
  4. 2.4Access to vehicle data under the Data Act
  5. 2.5Cybersecurity requirements: UN R155, UN R156 and ISO/SAE 21434
  6. 2.6Extended vehicle concept (ISO 20078)
  7. 2.7Consent management in the vehicle
  8. 2.8Data minimisation and anonymisation
  9. 2.9International data transfers
  10. 2.10Exercise: outline of a DPIA
  1. 3.1Event taxonomy and tracking plan
  2. 3.2Funnel and cohort analysis
  3. 3.3Retention and engagement metrics
  4. 3.4A/B testing and feature flags via OTA
  5. 3.5Statistical significance
  6. 3.6Session analysis in a driving context
  7. 3.7Analysis with Amplitude and Python
  8. 3.8Data quality checks
  9. 3.9Causal inference
  10. 3.10Exercise: cohort analysis
  1. 4.1Signal catalogue with COVESA VSS
  2. 4.2Edge sampling and aggregation
  3. 4.3Bandwidth and data cost constraints
  4. 4.4Data schemas and versioning
  5. 4.5Instrumenting the HMI
  6. 4.6Timestamps and clock synchronisation
  7. 4.7Edge filtering
  8. 4.8Privacy by design
  9. 4.9Telemetry specification review
  10. 4.10Exercise: telemetry tracking plan
  1. 5.1Ingestion from TCU to cloud with MQTT
  2. 5.2Streaming with Apache Kafka
  3. 5.3Data lakes and data warehouses
  4. 5.4Batch and real-time processing
  5. 5.5Dashboards in Grafana and Power BI
  6. 5.6Fleet-scale processing
  7. 5.7Data governance and catalogues
  8. 5.8Cost management
  9. 5.9Information security (ISO/IEC 27001)
  10. 5.10Exercise: prototype data pipeline
  1. 6.1HMI usability metrics
  2. 6.2Measuring driver visual behaviour (ISO 15007)
  3. 6.3Visual-manual distraction guidelines
  4. 6.4Task completion time
  5. 6.5Voice assistant metrics
  6. 6.6Balancing feature usage and safety
  7. 6.7Human factors studies
  8. 6.8Combining telemetry with user research
  9. 6.9Accessibility of in-vehicle interfaces
  10. 6.10Exercise: distraction-aware KPI set
  1. 7.1Validating telemetry on the test bench
  2. 7.2Data accuracy and completeness
  3. 7.3Metric reviews and OKRs
  4. 7.4Prioritising the roadmap with data
  5. 7.5Monetisation of digital services
  6. 7.6Reporting to management
  7. 7.7Data ethics
  8. 7.8Continuous improvement
  9. 7.9Data retention and deletion
  10. 7.10Exercise: product metrics review
  1. 8.1Definition of the case and success criteria
  2. 8.2Collection of input data and requirements
  3. 8.3Work planning and deliverables
  4. 8.4Technical development guided by the tutor
  5. 8.5Application of the regulatory framework to the case
  6. 8.6Verification of results and critical analysis
  7. 8.7Documentation of the deliverable
  8. 8.8Technical defence and peer review
  9. 8.9Lessons learned and continuous improvement
  10. 8.10Final project: final assessment and programme close-out
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, so you can combine the programme with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas), Apache Kafka, Grafana, Amplitude and Power BI, in free, academic or evaluation versions. Practical work is based on real procedures from an infotainment telemetry test bench, a connected vehicle data pipeline environment and a product analytics lab. Assessment is continuous by module, plus the final integrative project.

Standards covered: GDPR (Regulation (EU) 2016/679), ePrivacy Directive 2002/58/EC, EDPB Guidelines 01/2020, Data Act (EU) 2023/2854, ISO 20078, ISO/SAE 21434, UN R155 and UN R156, ISO/IEC 27001, ISO 15007, COVESA Vehicle Signal Specification.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of GDPR (Regulation (EU) 2016/679), ePrivacy Directive 2002/58/EC and EDPB Guidelines 01/2020 applied to real cases, not as a theoretical annex.
Industry software:Python (pandas), Apache Kafka, Grafana, Amplitude and Power BI, in free, academic or evaluation versions.
Final project:A complete metrics and usage telemetry plan for an in-vehicle digital product, reviewed by your tutor.
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.

Metrics tree
Define a metrics tree and KPIs for an in-vehicle digital product.
Tracking plan
Design a telemetry tracking plan with privacy by design.
Data pipeline
Build a prototype streaming pipeline and dashboard.
Cohort analysis
Analyse feature adoption and retention with cohorts and an A/B test.
Final project
Deliver a complete metrics and usage telemetry plan for an in-vehicle digital product, reviewed by your tutor.
Career opportunities

Where this takes you

Automotive product analystConnected vehicle data engineerDigital cockpit product managerTelematics engineerHMI research analystVehicle data governance manager
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An 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.

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FAQ

Frequently asked questions

The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python (pandas), Apache Kafka, Grafana, Amplitude and Power BI, 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree is required), plus 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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