Diploma in Root Cause Analytics and Data-Based Diagnostics
Online postgraduate diploma of 800 hours in root cause analytics and data-driven diagnosis. 8 modules covering classical root cause analysis, diagnostic data preparation, multivariate statistics, explainable machine learning, causal inference and operationalisation of diagnosis, with an assigned technical tutor.
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What you will be able to do
Apply classical root cause analysis methodologies.
Build diagnostic datasets that integrate multiple sources.
Use multivariate statistics to locate sources of variation.
Interpret machine learning models with SHAP and related techniques.
Apply causal inference and discovery to process data.
Diagnose failures in time series and event sequences.
Integrate data-driven diagnosis into the quality system.
Who it's for
- Quality engineers handling recurring defects and customer complaints.
- Industrial data scientists who want their models to explain why things fail.
- Process engineers looking for the sources of variation in their lines.
- Continuous improvement analysts leading 8D and DMAIC projects.
- Reliability engineers investigating failures and their causes.
- Problem-solving leads who need evidence rather than opinions.
- Engineering or science graduates looking to move into the chemical industry.
What you'll work with
- IEC 62740:2015 (root cause analysis)
- IEC 61025:2006 (fault tree analysis)
- IATF 16949:2016 (automotive quality)
- ISO 9001:2015 (quality management systems)
- ISO 13053-1 (Six Sigma: DMAIC methodology)
- ISA-18.2 (alarm management in the process industries)
- Python with scikit-learn and SHAP
- JMP
- SIMCA (Sartorius)
- causal-learn
- Minitab
8 modules
1.1Defining the problem and the symptom1.2Data-oriented 8D methodology1.3Ishikawa diagrams1.45 Whys method1.5Fault tree analysis1.6Kepner-Tregoe problem analysis1.7Barrier analysis1.8Cognitive biases in diagnosis1.9Limits of qualitative analysis1.10Workshop: 8D for a customer complaint
2.1Joining process, quality and maintenance data2.2Time alignment and process delays2.3Batch genealogy2.4Aggregation by production unit2.5Handling censored variables2.6Selecting the analysis period2.7Normalisation by product and recipe2.8Data quality and bias2.9Documentation of the analysis dataset2.10Practical exercise: diagnostic dataset with batch genealogy
3.1Stratification and comparison of groups3.2t-tests, ANOVA and non-parametric tests3.3Correlation and partial correlation3.4Multi-vari charts of sources of variation3.5Components of variation analysis3.6Principal component analysis3.7PLS regression for processes3.8Contributions to T² and SPE3.9Interpretation with process knowledge3.10Lab: multivariate diagnosis with PCA
4.1Interpretable decision trees4.2Random forest and permutation importance4.3Gradient boosting for diagnosis4.4SHAP values and their interpretation4.5Partial dependence and ICE curves4.6Association rules and pattern mining4.7Subgroup discovery4.8Risks of spurious correlations4.9Validation of generated hypotheses4.10Practical exercise: SHAP explanation of a defect model
5.1Correlation versus causation5.2Causal graphs of the process5.3Causal discovery with PC and GES5.4Granger causality in time series5.5Transfer entropy between variables5.6Counterfactuals and causal attribution5.7Integrating expert knowledge5.8Confirmation experiments5.9Limitations and assumptions5.10Case study: causal graph of a continuous furnace
6.1Diagnosis of cascading alarms6.2Event sequence analysis6.3Change point detection6.4Dynamic time warping (DTW)6.5Comparison of good and bad batches6.6Failure signatures in time series6.7Failure knowledge graphs6.8Case-based diagnosis6.9Pattern search tools6.10Lab: batch comparison with DTW
7.1Guided diagnosis workflow7.2Integration with quality systems and CAPA7.3Diagnostic dashboards7.4Collaboration between analysts and engineers7.5Root cause knowledge base7.6Report automation7.7Measuring time to resolution7.8Change management in quality teams7.9Accountability for diagnostic conclusions7.10Workshop: guided diagnosis workflow for a plant
8.1Problem definition8.2Building the dataset8.3Exploratory analysis of the quality case8.4Explainable models for the quality case8.5Causal analysis of the quality case8.6Root cause hypotheses8.7Verification and actions8.8Lessons learned from the quality case8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with scikit-learn and SHAP, JMP, SIMCA (Sartorius), causal-learn and Minitab, in free, academic or evaluation versions. Exercises are based on real procedures: multivariate analysis of good and bad batches, causal discovery in process data and experimental verification of root cause hypotheses. Assessment is continuous by module, plus the final capstone project.
Standards covered: IEC 62740:2015, IEC 61025:2006, IATF 16949:2016, ISO 9001:2015, ISO 13053-1, ISA-18.2.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Python with scikit-learn and SHAP, JMP, SIMCA (Sartorius), causal-learn and Minitab, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector; no university degree is required.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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