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CourseDefense, Military Engineering & Land Systems Applications open

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.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Design an experimentation and measurement plan focused on data quality.

02

Estimate measurement uncertainty according to the GUM.

03

Apply statistical tools to validate experimental results.

04

Detect and treat outliers using standardised criteria.

05

Assess the repeatability and reproducibility of a method.

06

Organise interlaboratory comparisons and interpret their results.

07

Ensure the integrity and traceability of data throughout its lifecycle.

Profile

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.
Standards & tools

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)
Syllabus

6 modules · 300 h

  1. 1.1What it means to validate data
  2. 1.2Error, uncertainty and bias
  3. 1.3Metrological traceability
  4. 1.4Accuracy, precision and trueness
  5. 1.5Reference standards for measurements and tests
  6. 1.6Laboratory competence requirements
  7. 1.7Good laboratory practice
  8. 1.8Data integrity and its regulatory importance
  9. 1.9International metrological vocabulary
  10. 1.10Exercise: classify sources of error in a test
  1. 2.1Defining the measurement objective
  2. 2.2Method selection and its validation
  3. 2.3Experimental design and factors
  4. 2.4Number of replicates and statistical power
  5. 2.5Randomisation and control of variables
  6. 2.6Environmental conditions and their recording
  7. 2.7Standards and reference materials
  8. 2.8Blanks, controls and blind samples
  9. 2.9Documentation of the measurement plan
  10. 2.10Practice: design a method validation plan
  1. 3.1Measurement model and input quantities
  2. 3.2Standard and combined uncertainty
  3. 3.3Expanded uncertainty and coverage factor
  4. 3.4Propagation and simulation methods
  5. 3.5Outlier detection
  6. 3.6Normality and homogeneity tests
  7. 3.7Regression and calibration fitting
  8. 3.8Limits of detection and quantification
  9. 3.9Comparison of results and compatibility
  10. 3.10Practice: calculate the uncertainty of a result
  1. 4.1Control charts and alert criteria
  2. 4.2Control samples and their frequency
  3. 4.3Participation in interlaboratory comparisons
  4. 4.4Proficiency tests and scoring
  5. 4.5Intermediate equipment verification
  6. 4.6Calibration and periodicity
  7. 4.7Peer review of results
  8. 4.8Actions for an out-of-control result
  9. 4.9Recording corrective actions
  10. 4.10Practice: interpret a control chart
  1. 5.1Principles of data integrity
  2. 5.2Laboratory notebooks and electronic records
  3. 5.3Audit trails and change control
  4. 5.4Management and retention of raw data
  5. 5.5Laboratory information management systems
  6. 5.6Open data and principles of reuse
  7. 5.7Metadata documentation
  8. 5.8Reproducibility and publication of methods
  9. 5.9Misconduct and data manipulation
  10. 5.10Practice: audit the integrity of a data set
  1. 6.1Choice of test and its objective
  2. 6.2Measurement plan and method validation
  3. 6.3Calibration and standards programme
  4. 6.4Uncertainty calculation
  5. 6.5Internal quality control plan
  6. 6.6Participation in comparisons
  7. 6.7Data integrity and management procedure
  8. 6.8Documentation and publication plan
  9. 6.9Technical report
  10. 6.10Project defence
Methodology, software & labs

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.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operating limits.
Industry mentoring:faculty with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Choice of test and its objective

Design an experimentation and measurement plan focused on data quality.

Measurement plan and method validation

Estimate measurement uncertainty according to the GUM.

Calibration and standards programme

Apply statistical tools to validate experimental results.

Uncertainty calculation

Detect and treat outliers using standardised criteria.

Internal quality control plan

Assess the repeatability and reproducibility of a method.

Career opportunities

Where this takes you

Laboratory quality managerTest engineerMetrologistExperimental data analystAuditor of accredited laboratoriesExperimental R&D researcher
Admissions & scholarships

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.

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FAQ

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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