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CourseCivil, Rail & Smart Cities Enrolment open

Big Data Course Applied to Off-Road Fleets

Online 300-hour course in Big Data applied to Off-Road fleets. 6 modules covering ISO 15143-3 (telematics for earthmoving machinery), ISO 11783 (ISOBUS) and Apache Spark. Dedicated tutor and open enrolment.

Duration300 h
ModeHybrid
LanguageEnglish / Spanish
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Characterise the data generated by off-road fleets.

02

Design large-scale data ingestion and storage.

03

Process and model data with distributed tools.

04

Visualise utilisation and performance indicators.

05

Apply data governance, security and data protection requirements.

06

Estimate the costs of a data platform.

07

Develop an end-to-end analytical use case.

Profile

Who it's for

  • Data engineer who wants to specialise in Big Data applied to off-road fleets.
  • Fleet data scientist who wants to specialise in Big Data applied to off-road fleets.
  • Data platform architect who wants to specialise in Big Data applied to off-road fleets.
  • Telematics analyst who wants to specialise in Big Data applied to off-road fleets.
  • Data governance manager who wants to specialise in Big Data applied to off-road fleets.
  • Head of digital fleet who wants to specialise in Big Data applied to off-road fleets.
  • Engineering or science graduates seeking to enter mining.
Standards & tools

What you'll work with

  • ISO 15143-3 (telematics for earthmoving machinery)
  • ISO 11783 (ISOBUS)
  • Regulation (EU) 2023/2854 (Data Act)
  • Regulation (EU) 2016/679 (GDPR)
  • ISO/IEC 27001 (information security)
  • ISO/IEC 20547 (big data reference architecture)
  • ISO 8000 (data quality)
Syllabus

6 modules · 300 h

  1. 1.1Volume, velocity and variety in a connected fleet
  2. 1.2Sources: telemetry, workshop, operation, fuel and weather
  3. 1.3Decisions that depend on aggregated data
  4. 1.4Why a spreadsheet stops being enough
  5. 1.5Possible architectures and their cost
  6. 1.6Latency requirements by use case
  7. 1.7Ownership and access to manufacturer data
  8. 1.8Team and required profiles
  9. 1.9Organisational data maturity
  10. 1.10Workshop: inventory of sources and volumes
  1. 2.1Batch and streaming ingestion
  2. 2.2Transmission protocols from the machine
  3. 2.3Time-series storage
  4. 2.4Data lake and zones by processing level
  5. 2.5Columnar formats and compression
  6. 2.6Partitioning and query-oriented design
  7. 2.7Data catalogue and metadata
  8. 2.8Retention, archiving and deletion
  9. 2.9Storage costs and optimisation
  10. 2.10Practice: designing a fleet's storage
  1. 3.1Distributed processing and parallelism
  2. 3.2Data transformations and pipelines
  3. 3.3Dimensional model for fleet analysis
  4. 3.4Canonical metrics: hours, consumption and availability
  5. 3.5Reconciling conflicting sources
  6. 3.6Data quality and automated controls
  7. 3.7Orchestration and process dependencies
  8. 3.8Pipeline observability
  9. 3.9Reproducibility and data versioning
  10. 3.10Practice: building a complete pipeline
  1. 4.1Dashboards by audience
  2. 4.2Fleet indicators: availability, utilisation and cost
  3. 4.3Analysis by machine, site and operator
  4. 4.4Comparison across periods and sites
  5. 4.5Self-service analytics and its limits
  6. 4.6Semantic layer and certified metrics
  7. 4.7Query performance and pre-aggregations
  8. 4.8Alerts and operational thresholds
  9. 4.9The risk of misinterpreting an aggregated figure
  10. 4.10Practice: a fleet dashboard
  1. 5.1Data governance and owners by domain
  2. 5.2Access control and sensitive data
  3. 5.3Protection of operator data
  4. 5.4Platform security and encryption
  5. 5.5Vendor lock-in and exit plan
  6. 5.6Platform cost control
  7. 5.7Agreements with manufacturers on data access
  8. 5.8Documentation and handover to operations
  9. 5.9Platform audit
  10. 5.10Practice: fleet data governance policy
  1. 6.1Reducing consumption with aggregated data
  2. 6.2Improving fleet utilisation
  3. 6.3Supporting machine renewal
  4. 6.4Objective comparison between suppliers
  5. 6.5Basis for predictive models
  6. 6.6Integration with maintenance and procurement
  7. 6.7Measuring the platform's return
  8. 6.8Common mistakes in data projects
  9. 6.9Maturity roadmap
  10. 6.10Final project: data platform for an off-road fleet
Methodology, software & labs

How you'll learn

Fully 100% online training on the SEIUM campus, with downloadable material, recorded classes and a dedicated 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 Spark, Apache Kafka, PostgreSQL/TimescaleDB, Python (pandas), Power BI, in free, academic or evaluation versions. Case work is based on real procedures for ingesting telematics data from mixed fleets, building data pipelines and creating utilisation dashboards. Assessment is continuous per module, plus the final integrating project.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL and vibration/acoustics.
Evidence-oriented final project: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.

Reducing consumption with aggregated data

Characterise the data generated by off-road fleets.

Improving fleet utilisation

Design large-scale data ingestion and storage.

Supporting machine renewal

Process and model data with distributed tools.

Objective comparison between suppliers

Visualise utilisation and performance indicators.

Basis for predictive models

Apply data governance, security and data protection requirements.

Career opportunities

Where this takes you

Data engineerFleet data scientistData platform architectTelematics analystData governance managerDigital fleet manager
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient level of Spanish 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 300 hours spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

You will work with Apache Spark, Apache Kafka, PostgreSQL/TimescaleDB, Python (pandas) and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.

An SEIUM own diploma upon passing the continuous assessment and the integrating project. This is in-house training, not an official Ministry qualification.

Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Check the credit transfer policy when you enrol.

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