ISO Course in Experimental Data Validation
Online course of 300 h in ISO for experimental data validation. 6 modules covering JCGM 100:2008 (GUM, Evaluation of measurement data – Guide to the expression of uncertainty in measurement), ISO/IEC 17025 (competence of testing and calibration laboratories) and Python (pandas, SciPy). 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
Design an experimentation and measurement plan focused on data quality.
Estimate measurement uncertainty according to the GUM.
Apply statistical tools to validate experimental results.
Detect and treat outliers using standardised criteria.
Assess the repeatability and reproducibility of a method.
Organise interlaboratory comparisons and interpret their results.
Ensure the integrity and traceability of data throughout its lifecycle.
Who it's for
- Laboratory quality manager who wants to specialise in ISO for experimental data validation.
- Test engineer who wants to specialise in ISO for experimental data validation.
- Metrologist who wants to specialise in ISO for experimental data validation.
- Experimental data analyst who wants to specialise in ISO for experimental data validation.
- Auditor of accredited laboratories who wants to specialise in ISO for experimental data validation.
- Experimental R&D researcher who wants to specialise in ISO for experimental data validation.
- Engineering or science graduates seeking to join testing and calibration laboratories.
What you'll work with
- JCGM 100:2008 (GUM, Evaluation of measurement data – Guide to the expression of uncertainty in measurement)
- ISO/IEC 17025 (competence of testing and calibration laboratories)
- ISO 5725-2 (repeatability and reproducibility)
- ISO 13528 (statistical methods for proficiency testing)
- JCGM 200:2012 (VIM, International vocabulary of metrology)
- ISO 16269-4 (detection and treatment of outliers)
- ISO 8000-8 (data quality)
6 modules · 300 h
1.1What it means to validate data1.2Error, uncertainty and bias1.3Metrological traceability1.4Accuracy, precision and trueness1.5Reference standards for measurements and tests1.6Laboratory competence requirements1.7Good laboratory practice1.8Data integrity and its regulatory importance1.9International metrological vocabulary1.10Exercise: classify sources of error in a test
2.1Defining the measurement objective2.2Method selection and its validation2.3Experimental design and factors2.4Number of replicates and statistical power2.5Randomisation and control of variables2.6Environmental conditions and their recording2.7Standards and reference materials2.8Blanks, controls and blind samples2.9Documentation of the measurement plan2.10Practice: design a method validation plan
3.1Measurement model and input quantities3.2Standard and combined uncertainty3.3Expanded uncertainty and coverage factor3.4Propagation and simulation methods3.5Outlier detection3.6Normality and homogeneity tests3.7Regression and calibration fitting3.8Limits of detection and quantification3.9Comparison of results and compatibility3.10Practice: calculate the uncertainty of a result
4.1Control charts and alert criteria4.2Control samples and their frequency4.3Participation in interlaboratory comparisons4.4Proficiency tests and scoring4.5Intermediate equipment verification4.6Calibration and periodicity4.7Peer review of results4.8Actions for an out-of-control result4.9Recording corrective actions4.10Practice: interpret a control chart
5.1Principles of data integrity5.2Laboratory notebooks and electronic records5.3Audit trails and change control5.4Management and retention of raw data5.5Laboratory information management systems5.6Open data and principles of reuse5.7Metadata documentation5.8Reproducibility and publication of methods5.9Misconduct and data manipulation5.10Practice: audit the integrity of a data set
6.1Choice of test and its objective6.2Measurement plan and method validation6.3Calibration and standards programme6.4Uncertainty calculation6.5Internal quality control plan6.6Participation in comparisons6.7Data integrity and management procedure6.8Documentation and publication plan6.9Technical report6.10Project defence
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, making it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, SciPy), R, Minitab, MATLAB, LabVIEW, in free, academic or evaluation versions. Tests are studied using real procedures from laboratory measurement campaigns, interlaboratory comparison exercises and data integrity audits. 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.
Design an experimentation and measurement plan focused on data quality.
Estimate measurement uncertainty according to the GUM.
Apply statistical tools to validate experimental results.
Detect and treat outliers using standardised criteria.
Assess the repeatability and reproducibility of a method.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow the reading of technical standards.
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 300 hours spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with full-time work.
You work with Python (pandas, SciPy), R, Minitab, MATLAB, LabVIEW. 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 integrative project. It is own training, not an official degree from the Ministry.
Yes. Modules passed in a course or Postgraduate Diploma in the same area are credited towards the higher-level programme. Check the credit transfer policy when you enrol.
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