Diploma in Scenarios, Trade-offs and Sensitivity Analysis
Online postgraduate diploma of 800 hours in scenarios, trade-offs and sensitivity analysis. 8 modules covering multi-criteria decision structuring, scenario planning, AHP, TOPSIS and PROMETHEE, multi-objective optimisation, local and global sensitivity analysis and robust decision making in line with ISO 31000:2018, IEC 31010:2019 and ISO 15288:2023, with an assigned technical tutor.
Diploma in Scenarios, Trade-offs and Sensitivity Analysis — studying from the United States
Data science, artificial intelligence and simulation are central to American industry, with major concentrations in the San Francisco Bay Area, Seattle, New York and Boston. Engineering organizations increasingly rely on machine learning, digital twins and high-performance computing to accelerate design, testing and operations. Employers range from large technology companies to aerospace, automotive and energy firms that apply analytics to complex systems. Demand is strong for engineers who combine domain knowledge with modeling, statistics and software skills.
Fees for the United States: US$1,440 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
Which US bodies relate to data science, AI and simulation engineering?
There is no single licensing body for data science or AI in the US. Relevant professional organizations include the IEEE Computer Society, the Association for Computing Machinery (ACM), and the Society for Industrial and Applied Mathematics (SIAM). Federal guidance such as the NIST AI Risk Management Framework shapes practice. SEIUM's qualifications are private and not accredited by a national body, so recognition is decided by employers.
How do US employers in data and AI regard private online postgraduate qualifications?
US tech firms, financial institutions and research labs generally hire on the basis of portfolios, technical interviews, relevant experience and sometimes advanced degrees. A private online qualification can show structured study, but it is not a standard hiring requirement and each employer weighs it differently. Public projects, code repositories and measurable results tend to matter most.
Is it realistic to study data and AI from the US around a full-time job?
Yes, the programmes are fully online and taught in English, so you can study from home without a student visa. The workload is large: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, and 300 hours for a specialist course. An assigned technical tutor supports you. Fees may be paid in instalments, and employer sponsorship is sometimes available, which you should confirm with your employer.
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
Structure technical decision problems with conflicting criteria.
Build consistent exploratory scenarios.
Apply multi-criteria methods such as AHP, TOPSIS and PROMETHEE.
Obtain and interpret Pareto fronts in multi-objective optimisation.
Perform local sensitivity analysis to identify thresholds.
Apply global sensitivity analysis with Sobol indices.
Communicate robust decisions to management and stakeholders.
Who it's for
- Decision analysts.
- Systems engineers balancing cost, performance and risk in design choices.
- Technical strategy consultants.
- Energy planners.
- Project engineers.
- Risk analysts.
- Engineering or science graduates seeking to enter the energy sector.
What you'll work with
- ISO 31000:2018 (Risk management)
- IEC 31010:2019 (Risk assessment techniques)
- ISO 15288:2023 (System life cycle processes)
- ISO 14040:2006 (Life cycle assessment: principles)
- ISO 50001:2018 (Energy management systems)
- JCGM 101:2008 (Propagation of distributions using a Monte Carlo method)
- Python with SALib
- pymoo
- EMA Workbench
- Expert Choice
- Microsoft Excel with @RISK
8 modules
1.1Structure of a decision problem1.2Alternatives, criteria and attributes1.3Conflicting objectives1.4Dominance and the Pareto frontier1.5Decision-maker preferences1.6Individual and group decisions1.7Cognitive biases in decision-making1.8Documenting the decision logic1.9Cases in engineering design1.10Workshop: structuring an investment decision
2.1Exploratory and normative scenarios2.2Identifying drivers and uncertainties2.3Shell axes method2.4Morphological matrix analysis2.5Scenario consistency2.6Narratives and quantification2.7Scenarios in energy and climate2.8Early signals and indicators2.9Adaptive planning through decision pathways2.10Practical exercise: four scenarios for an industrial plant
3.1Weighted sum and its limits3.2Analytic hierarchy process (AHP)3.3TOPSIS distance-to-ideal method3.4Outranking methods: ELECTRE and PROMETHEE3.5Multi-attribute utility theory (MAUT)3.6Criteria normalisation3.7Eliciting weights and swing weighting3.8Consistency of judgements3.9Decision support software3.10Lab: AHP and TOPSIS in Python
4.1Weighted sum and epsilon-constraint methods4.2NSGA-II and NSGA-III evolutionary algorithms4.3Quality indicators: hypervolume4.4Visualising Pareto fronts4.5Parallel coordinates and scatter plots4.6Selecting solutions on the front4.7Marginal rates of substitution4.8Trade-offs in product design4.9Trade-offs in system operation4.10Practical exercise: cost-emissions Pareto front with pymoo
5.1Derivatives and elasticities5.2One-at-a-time (OAT) analysis5.3Tornado diagrams5.4Break-even analysis5.5Weight sensitivity in multi-criteria analysis5.6Ranking stability5.7Decision threshold analysis5.8Limitations of the local approach5.9Communicating sensitivity results5.10Lab: tornado diagram for a cost model
6.1Morris method6.2First-order and total Sobol indices6.3Saltelli sampling6.4FAST and eFAST6.5Moment-based and density-based methods6.6Metamodels for sensitivity6.7Factor prioritisation and factor fixing6.8Computing indices with SALib6.9Interpreting interactions6.10Practical exercise: Sobol indices for an energy model
7.1Robust decision making (RDM)7.2Scenario discovery with PRIM7.3Regret analysis7.4Decision matrices under uncertainty7.5Visualisation for management7.6Participatory workshops with stakeholders7.7Traceability of assumptions7.8Independent review of the analysis7.9Lessons from real cases7.10Case study: robust capacity expansion decision
8.1Problem and stakeholders8.2Building the case scenarios8.3Criteria and alternatives8.4Evaluation model8.5Trade-off analysis8.6Decision sensitivity analysis8.7Robust decision recommendation8.8Communication plan8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training 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 each intake, so the programme is compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python with SALib, pymoo, EMA Workbench, Expert Choice and Microsoft Excel with @RISK, in free, academic or evaluation versions. Practical work is based on real procedures for building scenarios through expert workshops, global sensitivity analysis of technical models and exploring Pareto fronts for design decisions. Assessment is continuous by module plus the final project.
Standards covered: ISO 31000:2018, IEC 31010:2019, ISO 15288:2023, ISO 14040:2006, ISO 50001:2018, JCGM 101:2008.
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.
- 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 SALib, pymoo, EMA Workbench, Expert Choice and Microsoft Excel with @RISK, 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 required), a computer able to run the programme software and English sufficient to read technical standards.
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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