Postgraduate Diploma in Driver Telemetry and Risk Analytics
Online 800-hour Postgraduate Diploma in Driver Telemetry and Risk Analytics. 8 modules with ISO 15638 (telematics for commercial vehicles), Regulation (EU) 165/2014 (tachographs) and Python (pandas, scikit-learn). Assigned tutor and open enrolment.
Postgraduate Diploma in Driver Telemetry and Risk Analytics — studying from the United Kingdom
The United Kingdom has a strong medical technology and healthcare engineering base, with research in biomechanics, medical devices and wearables across universities and companies in London, Oxford, Leeds and Glasgow. The NHS, medical device manufacturers and digital health firms require engineers who understand clinical requirements and regulatory compliance. Operational safety in workplaces, transport and sport drives work on human factors and ergonomics. The Medicines and Healthcare products Regulatory Agency regulates medical devices, and the Health and Safety Executive oversees workplace safety.
Fees for the United Kingdom: £1,490 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Will this qualification be recognised by UK professional bodies such as the Institution of Engineering and Technology or the Institution of Mechanical Engineers?
SEIUM's qualifications are private qualifications, not official state degrees, and they are not accredited by any national body. Professional bodies such as the IET or IMechE decide whether to accept training for membership, usually through their own assessment of your experience and learning. Chartered Engineer status is awarded by the Engineering Council via licensed professional engineering institutions. Check directly with the relevant body before you enrol.
Will UK employers in healthcare technology, wearables or occupational safety value this training on a CV?
Employers decide for themselves how much weight to give a private qualification. Many value evidence of specialist skills, such as a completed final project and demonstrable work on biomechanics or wearable sensor systems, more than the name of the provider. Recognition is not guaranteed, so review relevant job adverts and ask prospective employers how they view online private training before committing.
Can I complete this programme alongside a full-time job in the UK, and does my employer need to fund it?
The programmes are 100% online and taught in English, so you can study at your own pace around work and across UK time zones. Master's programmes total 1,900 hours, postgraduate diplomas 800 hours and specialist courses 300 hours. Fees can be paid in instalments, and employer sponsorship is possible, though you should agree arrangements with your employer in advance.
Take the controls.
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
Describe telematics data sources: GPS, accelerometer, CAN and tachograph.
Pre-process driving signals through filtering, synchronisation and cleaning.
Detect risk events such as harsh braking, acceleration, sharp cornering and speeding.
Build indicators and a robust, interpretable driver score.
Train predictive collision models using driving data.
Apply the results to usage-based insurance and proactive fleet risk management.
Develop a driving analytics project using real data.
Who it's for
- Telematics analyst wishing to specialise in Driver Telemetry and Risk Analytics.
- Driving data scientist wishing to specialise in Driver Telemetry and Risk Analytics.
- Usage-based insurance actuary wishing to specialise in Driver Telemetry and Risk Analytics.
- Fleet safety manager wishing to specialise in Driver Telemetry and Risk Analytics.
- Telematics product engineer wishing to specialise in Driver Telemetry and Risk Analytics.
- Risk analyst at insurance companies wishing to specialise in Driver Telemetry and Risk Analytics.
- Engineering or science graduates seeking to enter motor insurance.
What you'll work with
- ISO 15638 (telematics for commercial vehicles)
- Regulation (EU) 165/2014 (tachographs)
- Regulation (EU) 2019/2144 (general vehicle safety)
- SAE J1939 (communications in heavy vehicles)
- ISO 39001 (road traffic safety management)
- Regulation (EU) 2016/679 (GDPR)
- UN Regulation No. 155 (cybersecurity)
8 modules · 800 h
1.1OBD-II devices, dongles and black boxes1.2CAN bus and PID reading according to SAE J19791.3Smartphone sensors: accelerometer, gyroscope and GNSS1.4OEM telematics units and the connected vehicle1.5Sampling frequency and GNSS signal quality1.6Transmission over MQTT and LTE-M networks1.7Data normalisation across different manufacturers1.8Consent and legal bases under the GDPR1.9Driving data lifecycle1.10Workshop: capture and decoding of OBD-II frames
2.1Time synchronisation of heterogeneous sensors2.2Reorientation of the smartphone to the vehicle axis2.3Kalman filter for GNSS-IMU fusion2.4Map matching on OpenStreetMap2.5Automatic trip segmentation2.6Driver versus passenger identification2.7Handling gaps, jumps and outliers2.8Calculation of speed, acceleration and jerk2.9Enrichment with speed limits and weather data2.10Practice: trip cleaning pipeline in Python
3.1Harsh braking: thresholds and validation3.2Aggressive acceleration and cornering through lateral force3.3Speeding relative to the legal limit3.4Mobile phone use while driving and distraction3.5Night driving and time-of-day exposure3.6Fatigue and continuous driving time3.7Collision detection through acceleration peaks3.8Road context: urban, interurban and motorway3.9False positives and detector calibration3.10Laboratory: event labelling on real journeys
4.1Exposure: kilometres, hours and road type4.2Event rates per 100 km4.3Design of an interpretable score4.4Factor weighting according to accident rates4.5Normalisation by context and percentiles4.6Temporal stability of the score4.7Per-trip score versus cumulative score4.8Communicating the score to the driver4.9Bias and fairness across profiles4.10Case study: building a 0–100 score
5.1Frequency and severity as target variables5.2Poisson and negative binomial models5.3GLM with exposure offset5.4Gradient boosting with LightGBM and XGBoost5.5Survival models until the claim5.6Class imbalance and rare events5.7Temporal and out-of-time validation5.8Metrics: Gini, lift curve and calibration5.9Interpretability with SHAP5.10Practice: frequency model with telematics data
6.1Pay-as-you-drive and pay-how-you-drive pricing6.2Fleet management and the digital tachograph6.3Driver coaching and gamification6.4Automatic first notification of loss (FNOL)6.5Accident reconstruction using telematics6.6eCall and Regulation (EU) 2015/7586.7Integration with ADAS data6.8Economic return of telematics programmes6.9Cases from insurers and logistics operators6.10Case study: fleet accident reduction programme
7.1Data protection impact assessment (DPIA) in telematics7.2Pseudonymisation of trajectories7.3Re-identification risk through location data7.4EDPB Guidelines 01/2020 on connected vehicles7.5Applicable requirements of the AI Act7.6Streaming with Kafka and Spark7.7MLOps: drift and retraining7.8Risk dashboards for managers7.9Model auditing and documentation7.10Workshop: real-time scoring architecture
8.1Definition of the use case and objectives8.2Selection and description of the dataset8.3Ingestion and pre-processing pipeline8.4Event detection and indicator calculation8.5Predictive risk model8.6Validation and bias analysis8.7Deployment proposal and data governance8.8Driver communication 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. There are 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 Python (pandas, scikit-learn), MATLAB, Geotab, SQL, Power BI, in free, academic or evaluation versions. Tests are studied on real signal-processing procedures for GPS and accelerometer data from real fleets, building a driver score, and validating collision models. Assessment is continuous across each 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 telematics data sources: GPS, accelerometer, CAN and tachograph.
Pre-process driving signals through filtering, synchronisation and cleaning.
Detect risk events such as harsh braking, acceleration, sharp cornering and speeding.
Build indicators and a robust, interpretable driver score.
Train predictive collision models using driving data.
Where this takes you
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 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 comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You will work with Python (pandas, scikit-learn), MATLAB, Geotab, SQL and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM diploma upon passing the continuous assessment and the integrative final 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 towards the advanced programme. Check the recognition policy when you enrol.
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