Postgraduate Diploma in Exposure Data Analysis and Reporting
Online 800-hour Postgraduate Diploma in Exposure Data Analysis and Reporting. 8 modules with UNE-EN 689 (measurement of exposure by inhalation to chemical agents), Royal Decree 374/2001 (chemical agents) and R. Assigned tutor and open enrolment.
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
Characterise occupational exposure data and its sources.
Ensure the quality of sampling data and its traceability.
Apply lognormal statistics and percentiles to exposure assessment.
Analyse exposure time series to detect trends and peaks.
Build exposure models for situations without direct measurements.
Prepare clear reports and dashboards for decision-making.
Handle exposure data in line with data protection and professional ethics.
Who it's for
- Industrial hygienist who wants to specialise in Exposure Data Analysis and Reporting.
- Exposure data analyst who wants to specialise in Exposure Data Analysis and Reporting.
- Occupational risk prevention technician who wants to specialise in Exposure Data Analysis and Reporting.
- Occupational epidemiologist who wants to specialise in Exposure Data Analysis and Reporting.
- Chemical safety consultant who wants to specialise in Exposure Data Analysis and Reporting.
- Occupational health manager who wants to specialise in Exposure Data Analysis and Reporting.
- Engineering or science graduates seeking to enter the chemical industry.
What you'll work with
- UNE-EN 689 (measurement of exposure by inhalation to chemical agents)
- Royal Decree 374/2001 (chemical agents)
- UNE-EN 482 (requirements for measurement procedures)
- Directive 2004/37/EC (carcinogenic and mutagenic agents)
- Regulation (EC) 1907/2006 (REACH)
- UNE-EN ISO 9612 (noise exposure at work)
- Royal Decree 286/2006 (noise)
8 modules · 800 h
1.1Physical, chemical and ergonomic agents1.2Exposure to noise, vibration and heat1.3Exposure to chemical agents and dust1.4Limit values: VLA-ED and VLA-EC1.5Sampling strategies under UNE-EN 6891.6Similar exposure groups1.7Personal versus area measurements1.8Portable exposure sensors1.9Data sources in the company1.10Case study: exposure data inventory of a plant
2.1Noise dosimeters and their configuration2.2Real-time particle monitors2.3WBGT heat stress meters2.4Exposure and location wearables2.5Calibration and verification in the field2.6Missing data and values below the limit of detection2.7Sampling metadata2.8Integration into databases2.9Data governance and quality2.10Laboratory: downloading and cleaning dosimeter data
3.1Lognormal distribution of exposures3.2Geometric mean and geometric standard deviation3.395th percentile and confidence limits3.4Handling censored data3.5UNE-EN 689 conformity tests3.6Bayesian statistics with IHSTAT and Expostats3.7Within- and between-subject variance analysis3.8Temporal trends3.9Tools: R and Python3.10Practical: conformity assessment of a homogeneous group
4.1Exposure profiles over the working day4.2Detection of peaks and critical tasks4.3Correlation with activity and location4.4Moving windows and averages4.5Cumulative exposure and dose4.6Event and alarm analysis4.7Series visualisation4.8Shift and seasonal patterns4.9Predictive models4.10Workshop: analysis of dust exposure peaks
5.1Two-zone models5.2Occupational exposure models: ART and Stoffenmanager5.3ECETOC TRA tools5.4Cumulative exposure for epidemiological studies5.5Job-exposure matrices5.6Model uncertainty5.7Validation against measurements5.8Control scenarios5.9Regulatory use under REACH5.10Practical: estimation with a two-zone model
6.1Principles of data visualisation6.2Exposure dashboards6.3Tools: Power BI and Grafana6.4Key industrial hygiene indicators6.5Technical assessment reports6.6Communication to workers and committees6.7Reports for inspection6.8Report automation6.9Traceability of calculations6.10Workshop: noise exposure dashboard
7.1Health data and GDPR7.2Worker monitoring and legal limits7.3Worker information and participation7.4Anonymisation of exposure data7.5Record retention7.6Use of data for disciplinary decisions7.7Algorithm transparency7.8Information security7.9Ethics in workplace surveillance7.10Case study: data policy for a wearables programme
8.1Definition of the problem and agents8.2Sampling plan8.3Data capture and cleaning8.4Statistical analysis8.5Scenario modelling8.6Dashboard8.7Technical report and recommendations8.8Data protection plan8.9Dissertation and presentation8.10Final review with the tutor
How you'll learn
Training 100 % online on the SEIUM Institute 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 the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, Python (pandas), IHSTAT (AIHA), Expostats, Power BI, in free, academic or evaluation versions. Assessments are based on real procedures from personal sampling campaigns, analysis of exposure databases and limit-value compliance reports. Assessment is continuous per 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.
Characterise occupational exposure data and its sources.
Ensure the quality of sampling data and its traceability.
Apply lognormal statistics and percentiles to exposure assessment.
Analyse exposure time series to detect trends and peaks.
Build exposure models for situations without direct measurements.
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 Spanish to follow technical regulatory reading.
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 totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with R, Python (pandas), IHSTAT (AIHA), Expostats and Power BI. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM Institute upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.
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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