Diploma in Design and Operation of Industrial A/B Experiments
Online postgraduate diploma of 800 hours in the design and operation of industrial A/B experiments. 8 modules covering controlled experimentation, power and sample size, switchback and cluster designs, CUPED variance reduction, sequential and adaptive testing, experimentation platforms and programme scaling in line with ISO 3534-3, ISO 16269-6 and ISO 2602, with an assigned technical tutor.
Diploma in Design and Operation of Industrial A/B Experiments — studying from Canada
Canada is a recognised centre for artificial intelligence research, with hubs in Toronto, Montréal and Edmonton supported by institutes such as the Vector Institute, Mila and Amii. Engineering teams across manufacturing, energy, finance and transport apply simulation, machine learning and data analytics to real operational problems. The federal government's Pan-Canadian AI Strategy and privacy regulation shape how organisations handle data. Typical employers include technology firms, banks, aerospace and automotive companies, and public research bodies.
Fees for Canada: C$2,010 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Do Canadian bodies recognise data or AI qualifications from private institutes?
Data and AI roles in Canada are generally not licensed in the way engineering is, so recognition depends on employers and professional communities rather than a regulator. Engineering licensing through provincial associations applies only to engineering titles. SEIUM's qualifications are private, not official state degrees and not accredited by a national body, so employers decide how much weight to give them.
Do Canadian employers hire for data and AI skills, and would this help?
Canada has a large and active data and AI sector, with notable hubs in Toronto, Montreal and Vancouver, and employers hire widely for these skills. They usually look first at portfolios, technical interviews and proven projects. A SEIUM final project can demonstrate your ability, but a private qualification's value is decided by each employer, so present it alongside concrete work.
Is this manageable with a job, and what level of English is needed?
Because the programmes are fully online, you can study from home around your job, and no student visa is needed. Specialist courses are 300 hours, postgraduate diplomas 800 and master's 1,900, so plan your weekly time. Teaching is in English, so strong written and reading English is important. Instalment payments and possible employer sponsorship can help with funding.
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 controlled experiments in industrial environments.
Calculate power and sample size for real metrics.
Apply switchback, cluster and stepped wedge designs.
Reduce variance and analyse results rigorously.
Use sequential tests and adaptive designs safely.
Build an experimentation platform with quality checks.
Scale an experimentation programme with measurable impact.
Who it's for
- Experimentation data scientists.
- Process engineers who want to test changes on the line with statistical rigour.
- Quality engineers.
- Industrial product analysts.
- Continuous improvement managers.
- Industrial statisticians.
- Engineering or science graduates seeking to enter manufacturing.
What you'll work with
- ISO 3534-3 (Vocabulary: design of experiments)
- ISO 16269-6 (Statistical tolerance intervals)
- ISO 2602 (Estimation of the mean: confidence interval)
- ICH E9 (Statistical principles for clinical trials)
- ISO 5725-2 (Accuracy of measurement methods: repeatability and reproducibility)
- ISO 13053-1 (Six Sigma: DMAIC methodology)
- Python with statsmodels and SciPy
- R
- GrowthBook
- Minitab
- PyMC
8 modules
1.1A/B experiments versus classical DOE1.2Experimental units: machines, batches, shifts and plants1.3Randomisation and its mechanisms1.4Control group and operational placebo1.5Ethics and safety in plant experiments1.6Costs and risks of experimenting1.7Hypothesis and primary metric1.8Experiments in industrial software and in processes1.9Experimentation culture1.10Workshop: designing a process A/B experiment
2.1Hypothesis testing and type I and II errors2.2Minimum detectable effect size2.3Power and sample size calculation2.4Variance of industrial metrics2.5Parametric and non-parametric tests2.6Bootstrap for complex metrics2.7Ratio metrics and the delta method2.8Control of multiple comparisons2.9Pre-registration of the analysis2.10Practical exercise: power calculation with statsmodels
3.1Block randomisation3.2Switchback designs3.3Crossover designs between periods3.4Cluster randomisation3.5Interference between units3.6Carry-over and washout effects3.7Stepped wedge designs3.8Scarce units and paired designs3.9Production constraints3.10Case study: switchback in a continuous furnace
4.1CUPED and pre-experiment covariates4.2Post-experiment stratification4.3Regression with covariates4.4Mixed models for repeated measures4.5Intention-to-treat analysis4.6Heterogeneous effects by segment4.7Outliers and winsorisation4.8Bayesian analysis of experiments4.9Practical interpretation versus significance4.10Lab: CUPED on line performance data
5.1The problem of peeking at results repeatedly5.2Sequential tests and SPRT5.3Alpha spending functions5.4Always-valid confidence intervals5.5Multi-armed bandits for allocation5.6Allocation with Thompson sampling5.7Early stopping for safety5.8Adaptive experiments in processes5.9Risks of adaptive designs5.10Practical exercise: sequential test with alpha spending
6.1Treatment assignment and logging6.2Integration with MES and control systems6.3Feature flags in industrial software6.4Automatic metric collection6.5Quality checks: SRM and balance6.6Experiment dashboards6.7Catalogue of experiments and learnings6.8Analysis automation6.9Governance and approval of experiments6.10Lab: sample ratio mismatch (SRM) detection
7.1Hypothesis prioritisation7.2Experiment portfolio7.3Communicating experimental results7.4Rollout decisions7.5Long-term and novelty effects7.6Meta-analysis of experiments7.7Common mistakes in experimentation7.8Team training7.9Measuring programme value7.10Workshop: critical review of failed experiments
8.1Hypothesis and context8.2Metrics and power8.3Choice of experimental design8.4Randomisation plan8.5Pre-registered analysis plan8.6Simulated or real execution8.7Analysis and conclusions8.8Rollout recommendation8.9Report 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 statsmodels and SciPy, R, GrowthBook, Minitab and PyMC, in free, academic or evaluation versions. Practical work is based on real procedures for switchback experiments in continuous processes, analysis with CUPED variance reduction and sequential tests with early stopping. Assessment is continuous by module plus the final project.
Standards covered: ISO 3534-3, ISO 16269-6, ISO 2602, ICH E9, ISO 5725-2, ISO 13053-1.
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 statsmodels and SciPy, R, GrowthBook, Minitab and PyMC, 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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