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Postgraduate DiplomaAutomotive & Motorsport Applications open

Postgraduate Diploma in Racing Pipelines and Dashboards

800-hour online Postgraduate Diploma in Racing Pipelines and Dashboards. 8 modules covering ASAM MDF 4.2, ISO/IEC 25012 and Apache Kafka. Assigned tutor and open enrolment.

Duration800 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 data architectures for racing teams.

02

Build real-time and batch ingestion pipelines.

03

Calculate performance metrics in a scalable way.

04

Design clear dashboards for engineers and strategists.

05

Implement robust real-time dashboards.

06

Automate reproducible reports.

07

Operate and govern data platforms at events.

Profile

Who it's for

  • Data engineer who wants to specialise in Racing Pipelines and Dashboards.
  • Dashboard developer who wants to specialise in Racing Pipelines and Dashboards.
  • Telemetry engineer who wants to specialise in Racing Pipelines and Dashboards.
  • Performance analyst who wants to specialise in Racing Pipelines and Dashboards.
  • Data architect who wants to specialise in Racing Pipelines and Dashboards.
  • Competition software engineer who wants to specialise in Racing Pipelines and Dashboards.
  • Graduates in engineering or science looking to enter motorsport.
Standards & tools

What you'll work with

  • ASAM MDF 4.2
  • ISO/IEC 25012
  • ISO/IEC 27001:2022
  • ISO 8601-1
  • ISO/IEC 25010
  • ISO/IEC 20547-3
Syllabus

8 modules · 800 h

  1. 1.1Data sources: telemetry, data logger, timing
  2. 1.2Data volume and velocity
  3. 1.3Lambda and kappa architectures
  4. 1.4Storage in the garage and at the factory
  5. 1.5Time-series databases
  6. 1.6MDF4, Parquet and HDF5 formats
  7. 1.7Data model per session and lap
  8. 1.8Latency requirements
  9. 1.9Security and access control
  10. 1.10Case study: data architecture of a GT team
  1. 2.1Real-time ingestion with Kafka
  2. 2.2Batch ingestion after a session
  3. 2.3Connectors with telemetry tools
  4. 2.4Data transformation and cleansing
  5. 2.5Orchestration with Airflow
  6. 2.6Error handling and retries
  7. 2.7Scalability and partitioning
  8. 2.8Pipeline monitoring
  9. 2.9Infrastructure as code
  10. 2.10Laboratory: ingestion pipeline with Kafka and Python
  1. 3.1Calculated channels and mathematics
  2. 3.2Segmentation by lap and sector
  3. 3.3Driver performance metrics
  4. 3.4Tyre and energy metrics
  5. 3.5Distributed processing with Spark
  6. 3.6Stream processing
  7. 3.7Caches and materialised views
  8. 3.8Validation of results
  9. 3.9Metric documentation
  10. 3.10Practice: sector metric calculation with pandas
  1. 4.1Visualisation principles for engineers
  2. 4.2Information hierarchy and priorities
  3. 4.3Choosing charts by data type
  4. 4.4Dashboards for the pit wall and garage
  5. 4.5Dashboards for the driver and strategist
  6. 4.6Use of colour and visual alerts
  7. 4.7Interactivity and filters
  8. 4.8Performance and loading times
  9. 4.9Accessibility and legibility
  10. 4.10Workshop: stint dashboard in Grafana
  1. 5.1Streaming data to the dashboard
  2. 5.2End-to-end latency
  3. 5.3Live chart updates
  4. 5.4Official timing data
  5. 5.5Live comparisons with rivals
  6. 5.6Track and position maps
  7. 5.7Live visual alerts
  8. 5.8Displays on the pit wall
  9. 5.9Robustness against link loss
  10. 5.10Case study: live dashboard for the strategist
  1. 6.1Automatic post-session reports
  2. 6.2Comparison between sessions and events
  3. 6.3Reports for technical direction
  4. 6.4Reproducible notebooks with Jupyter
  5. 6.5PDF and HTML generation
  6. 6.6Secure distribution of reports
  7. 6.7Template management
  8. 6.8Report calendar per event
  9. 6.9User feedback
  10. 6.10Practice: automatic post-session report
  1. 7.1Deployment in a portable garage
  2. 7.2Synchronisation with the factory
  3. 7.3Backup and retention
  4. 7.4Data ownership and confidentiality
  5. 7.5User and permission management
  6. 7.6Data governance and catalogue
  7. 7.7Infrastructure costs
  8. 7.8Support during events
  9. 7.9Continuous improvement of the platform
  10. 7.10Case study: deployment at an intercontinental event
  1. 8.1Definition of user requirements
  2. 8.2Data architecture
  3. 8.3Ingestion pipeline
  4. 8.4Metric calculation
  5. 8.5Dashboard design
  6. 8.6Real-time dashboard
  7. 8.7Automatic report generation
  8. 8.8User evaluation
  9. 8.9Dissertation and presentation
  10. 8.10Final review with the tutor
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: students progress at their own pace within the intake, which makes it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Apache Kafka, InfluxDB, Grafana, Apache Airflow, Python (pandas), in free, academic or evaluation versions. Tests are studied using real pipeline implementation procedures during a race weekend, dashboard evaluation with track engineers, and real-time latency tests. Assessment is continuous per module plus the final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-based Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Definition of user requirements

Design data architectures for racing teams.

Data architecture

Build real-time and batch ingestion pipelines.

Ingestion pipeline

Calculate performance metrics in a scalable way.

Metric calculation

Design clear dashboards for engineers and strategists.

Dashboard design

Implement robust real-time dashboards.

Career opportunities

Where this takes you

Data engineerDashboard developerTelemetry engineerPerformance analystData architectCompetition software engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish level to follow technical regulatory texts.

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 spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.

You will work with Apache Kafka, InfluxDB, Grafana, Apache Airflow, Python (pandas). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

SEIUM's own diploma upon passing the continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.

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