Course in Predictive Models in Fleet Telematics
The Course in Digital Twins for Turbines explores the creation and application of precise virtual models of turbines, using real-time data to simulate, predict and optimise engine performance. It focuses on the integration of sensors, data analysis, artificial intelligence (AI) and machine learning (ML) to improve efficiency, reduce maintenance costs and extend the service life of turbines. It includes the use of CFD and FEM simulation tools to analyse the behaviour of components under diverse operating conditions, facilitating predictive monitoring and design optimisation.
The course provides practical knowledge of digital twin platforms, the processing of large volumes of data (Big Data) and the implementation of predictive maintenance strategies. Participants will learn to develop and use digital twins to diagnose faults, predict component wear and optimise turbine performance in real time, improving energy efficiency and operational safety. This training prepares professionals such as maintenance engineers, data analysts and simulation specialists to apply the latest technologies in the aviation industry.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Describe the fleet as a statistical population to model.
Prepare and clean telematics data from commercial vehicles.
Build claim prediction models.
Design driver scores and preventive interventions.
Measure the effect of interventions using causal designs.
Use the model in negotiations with the insurer.
Industrialise the model in compliance with data regulations.
Who it's for
- Telematics data scientist wishing to specialise in Predictive Models in Fleet Telematics.
- Fleet risk manager wishing to specialise in Predictive Models in Fleet Telematics.
- Fleet insurance analyst wishing to specialise in Predictive Models in Fleet Telematics.
- Corporate road safety manager wishing to specialise in Predictive Models in Fleet Telematics.
- Data engineer wishing to specialise in Predictive Models in Fleet Telematics.
- Telematics consultant wishing to specialise in Predictive Models in Fleet Telematics.
- Engineering or science graduates looking to enter fleet management.
What you'll work with
- ISO 39001 (road safety management)
- Regulation (EC) 561/2006 (driving and rest times)
- Regulation (EU) 165/2014 (tachographs)
- ISO 15638 (telematics for regulated commercial vehicles, TARV)
- Regulation (EU) 2023/2854 (Data Act)
- Regulation (EU) 2016/679 (GDPR)
6 modules · 300 h
1.1Fleet types: commercial, delivery, services and passenger transport1.2Assigned driver, rotation and shared vehicle1.3Fleet exposure units: kilometres, shifts and routes1.4Data sources: telematics, workshop, fuel and reports1.5Link between driver, vehicle and claim1.6Non-third-party accidents and manoeuvre damage1.7Fixed routes versus variable routes1.8Hidden costs: immobilisation, replacement and service delay1.9Which concrete decision the model must improve1.10Workshop: defining the predictive objective for a fleet
2.1Cleaning the telematics signal and detecting gaps2.2Driver identification in shared vehicles2.3Aggregation by shift, by route and by period2.4Driving variables normalised by exposure2.5Route variables: road type, traffic and environment2.6Context variables: weather and time of day2.7Management variables: seniority, training and shift2.8Information leakage and variables not available in production2.9Temporal partition for training and validation2.10Practice: building the modelling table
3.1Claim prediction by driver and by period3.2Count models with exposure3.3High-risk classification and probability calibration3.4Gradient boosting and regularised linear models3.5Class imbalance in small fleets3.6Hierarchical models: driver within fleet3.7Prediction of expected cost3.8Variable importance and operational interpretation3.9Temporal validation and stability across periods3.10Practice: driver risk model
4.1Designing the driving score for internal use4.2Intervention thresholds and team capacity4.3Intervention types: warning, coaching, training and route change4.4Prioritisation by risk and by avoided cost4.5Designing a controlled trial of the intervention4.6Measuring the real effect and regression to the mean4.7Effect of perceived monitoring on behaviour4.8Communication with the driver and programme acceptance4.9Fleet safety programme indicators4.10Practice: designing and evaluating an intervention
5.1Presenting fleet risk with own data5.2Negotiating deductibles and conditions5.3Programmes with profit sharing5.4Partial self-insurance and claims fund5.5Quantifying the savings attributable to the programme5.6Claim reconstruction and defence of claims5.7Detection of unreported claims5.8Claims report for renewal5.9Relationship with the broker and the insurer5.10Practice: renewal dossier based on the model
6.1Putting the model into production and automation6.2Drift monitoring and retraining6.3Data protection for drivers6.4Employment rights and limits of monitoring6.5Model governance and responsibilities6.6Documentation and handover to the fleet team6.7Scaling to several fleets and countries6.8Common errors in fleet predictive models6.9Analytics maturity roadmap6.10Final project: complete predictive system for a fleet
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: the student progresses at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Geotab, Samsara, Python (scikit-learn), Databricks, Power BI, in free, academic or evaluation versions. Tests are studied using real driver scoring procedures with telematics data, intervention trials and effect measurement, and analysis of driving events. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Describe the fleet as a statistical population to model.
Prepare and clean telematics data from commercial vehicles.
Build claim prediction models.
Design driver scores and preventive interventions.
Measure the effect of interventions using causal designs.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Spanish language level sufficient 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.
Frequently asked questions
The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Geotab, Samsara, Python (scikit-learn), Databricks, Power BI. Free, academic or evaluation versions are used, and the tutor indicates the free alternative when one exists.
An own SEIUM diploma upon passing the continuous assessment and the integrative project. It 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 in the higher programme. Check the recognition policy when you enrol.
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