Postgraduate Diploma in Data Pipelines for EV Fleets and Predictive Maintenance
Online 800-hour Postgraduate Diploma in Data Pipelines for EV Fleets and Predictive Maintenance. 8 modules covering Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act) and Apache Kafka. Assigned tutor and open enrolment.
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Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the relevant data sources of an electric fleet.
Design scalable data pipelines for telematics data.
Ensure data quality and readiness.
Develop predictive maintenance models.
Deploy and monitor models using MLOps.
Integrate predictions into fleet operations.
Apply European regulation on data and artificial intelligence.
Who it's for
- Data engineer looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- Fleet data scientist looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- MLOps engineer looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- Fleet maintenance manager looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- Operations analyst looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- Data architect looking to specialise in Data Pipelines for EV Fleets and Predictive Maintenance.
- Engineering or science graduates seeking to enter fleet operators.
What you'll work with
- Reglamento (UE) 2016/679 (RGPD)
- Reglamento (UE) 2023/2854 (Data Act)
- Reglamento (UE) 2024/1689 (Ley de IA)
- Reglamento UNECE n.º 155
- ISO 13374
- OCPP 2.0.1
- ISO 55000
- ISO/IEC 27001
8 modules · 800 h
1.1Fleet types: delivery, bus, taxi and car sharing1.2Key vehicle, battery and charger signals1.3Telematics and charging data sources1.4OCPP and OCPI protocols in charging1.5Data volume, velocity and variety1.6Ownership and access to vehicle data1.7Privacy and anonymisation of trips1.8Operator business requirements1.9Predictive maintenance use cases1.10Workshop: inventory of a fleet's data sources
2.1Lambda and kappa architectures2.2Streaming ingestion with Apache Kafka2.3Batch processing with Apache Spark2.4Data lake, data warehouse and lakehouse2.5Columnar formats Parquet and Delta2.6Orchestration with Apache Airflow2.7Data schemas and contracts2.8Design for scalability and cost2.9Infrastructure as code2.10Practice: ingestion pipeline with Kafka and Spark
3.1Automated validation with Great Expectations3.2Handling missing values and outliers3.3Synchronising signals with different frequencies3.4Resampling and temporal aggregation3.5Feature engineering for batteries and motors3.6Labelling fault events3.7Data lineage and catalogue3.8Dataset versioning3.9Documentation and data dictionary3.10Laboratory: cleaning BMS data with pandas
4.1Corrective, preventive and predictive strategies4.2Fault classification with gradient boosting4.3Survival and risk models4.4Remaining useful life prediction of components4.5Anomaly detection in time series4.6Maintenance of tyres, brakes and battery4.7Class imbalance in fault data4.8Business-oriented evaluation metrics4.9Interpretability with SHAP values4.10Practice: fault prediction model with XGBoost
5.1Model lifecycle in production5.2Model registry with MLflow5.3Batch and real-time deployment5.4Data drift monitoring5.5Automatic model retraining5.6Model and data testing5.7Containers and Kubernetes5.8Cloud computing costs5.9Pipeline security5.10Workshop: model deployment with MLflow
6.1Integration with fleet management systems6.2Work order planning6.3Optimisation of vehicle availability6.4Spare parts management with prediction6.5Charging and energy optimisation6.6Dashboards for workshop managers6.7Alerts and notifications to drivers6.8Measuring savings achieved6.9Change management in workshops6.10Case study: fleet of 200 delivery vans
7.1General Data Protection Regulation7.2European Data Act regulation7.3Telematics cybersecurity under R1557.4Data access contracts with manufacturers7.5Ethics and bias in predictive models7.6European artificial intelligence regulation7.7Retention and deletion policies7.8Model auditing and traceability7.9Corporate data governance7.10Case study: data protection impact assessment
8.1Business case definition8.2Data pipeline architecture8.3Dataset preparation8.4Selected predictive model8.5MLOps plan8.6Integration with the workshop8.7Impact indicators8.8Data governance plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 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, Apache Spark, Apache Airflow, MLflow, Python with scikit-learn, in free, academic or evaluation versions. Tests are studied through real fault prediction procedures in electric delivery fleets, construction of telematics data pipelines and deployment of models with MLOps. Assessment is continuous by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the relevant data sources of an electric fleet.
Design scalable data pipelines for telematics data.
Ensure data quality and readiness.
Develop predictive maintenance models.
Deploy and monitor models using MLOps.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient English 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 is 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
You will work with Apache Kafka, Apache Spark, Apache Airflow, MLflow and Python with scikit-learn. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM upon passing the continuous assessment and the integrating project. It is institutional 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 credit transfer policy when you enrol.
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