Diploma in Product KPIs, Pricing and A/B Testing at ADAS
Online postgraduate diploma of 800 hours in product KPIs, pricing and A/B testing in ADAS. 8 modules covering ADAS product metrics, fleet instrumentation, product analytics, feature pricing, experiment design and analysis, and data governance, with Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act) and UN R156, with an assigned technical tutor.
Diploma in Product KPIs, Pricing and A/B Testing at ADAS — studying from Canada
Canada hosts growing activity in autonomous driving and advanced driver assistance, with research centres and companies in Ontario, Québec and British Columbia. Toronto and Waterloo have significant artificial intelligence and robotics ecosystems, and the Vector Institute and university labs contribute to perception and control research. Automakers and suppliers are developing software-defined vehicle architectures, requiring expertise in embedded software, sensor fusion and cybersecurity. Regulation is coordinated through provincial authorities and federal guidance from Transport Canada.
Fees for Canada: C$2,330 (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
Does this qualification count toward a professional engineering licence in Canada?
No, not on its own. In Canada, the Professional Engineer (P.Eng.) licence is issued by provincial associations such as Professional Engineers Ontario, which require accredited education and experience. SEIUM's qualifications are private and not accredited by any national body, so they are not recognised as state degrees. Employers or relevant bodies decide whether they value the training, so confirm this in advance.
Are there Canadian employers hiring for autonomous and software-defined vehicle skills?
Canada has active work in autonomous systems, advanced driver assistance and vehicle software, with activity in Ontario, Quebec and British Columbia. Employers generally assess candidates on technical ability, project evidence and experience. A SEIUM final project can showcase your skills, but how a private qualification is regarded depends entirely on the employer, so ask prospective employers what they expect.
Can I manage this training with a job, and do I need English proficiency?
Yes, the courses are 100% online, so you can study from home, and no student visa is needed to study from outside your country. Specialist courses take 300 hours, postgraduate diplomas 800 and master's programmes 1,900. Instruction is in English, so you should be comfortable reading and writing technical English. Instalment payments and employer sponsorship may ease costs.
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
Define a product metric tree with safety counter-metrics.
Design event instrumentation for a connected ADAS function.
Analyse cohorts, funnels and subscriber retention.
Apply pricing research methods to feature bundles.
Design A/B experiments with adequate statistical power.
Analyse experiments with frequentist and Bayesian techniques.
Make launch decisions that respect ethics and the GDPR.
Who it's for
- ADAS product analysts tracking feature adoption and usage.
- Automotive product data scientists running experiments.
- Digital pricing managers setting subscription and bundle prices.
- Data-driven product managers working on connected features.
- Experimentation specialists moving into the automotive sector.
- Connected fleet analysts working with telemetry data.
- Engineering or science graduates looking to enter vehicle manufacturing.
What you'll work with
- Regulation (EU) 2016/679 (General Data Protection Regulation)
- Regulation (EU) 2023/2854 (Data Act, access to connected product data)
- UN R156 (software update management system)
- ISO 26262 (road vehicle functional safety)
- ISO 21448 (safety of the intended functionality, SOTIF)
- UN R155 (vehicle cybersecurity management system)
- ISO/IEC 27001 (information security management)
- Python (pandas, statsmodels)
- SQL (BigQuery)
- Amplitude
- Optimizely
- Tableau
8 modules
1.1Metric trees and the North Star Metric1.2Activation rate and effective use of the function1.3Time in use within the ODD versus available time1.4Voluntary and involuntary disengagements1.5Driver satisfaction and trust1.6Subscriber retention metrics1.7Non-negotiable safety counter-metrics1.8In-vehicle event instrumentation1.9Perceived quality metrics for the function1.10Case study: metric tree for a motorway assistant
2.1Onboard telemetry and event sampling2.2Event schemas and versioning2.3Cloud ingestion and fleet data lakes2.4Data quality and deduplication2.5Privacy and data minimisation2.6Attributing usage to software version2.7Vehicle identification and cohorts2.8Connectivity costs and data prioritisation2.9Reproducible metrics pipeline2.10Laboratory: event schema for a parking function
3.1Cohort analysis and retention curves3.2Activation and conversion funnels3.3Segmentation by market, model and version3.4Survival analysis of subscriptions3.5Anomaly detection in daily metrics3.6Tracking dashboards with SQL3.7Root cause analysis of usage drops3.8From data to roadmap decisions3.9Usage by traffic and weather conditions3.10Practical exercise: cohort analysis with synthetic fleet data
4.1Willingness to pay and Van Westendorp studies4.2Conjoint analysis for feature bundles4.3Pricing by market and purchasing power parity4.4Subscription price versus one-off payment4.5Discounts, free trials and anchoring4.6Price elasticity of demand4.7Cannibalisation between bundles4.8Perceived price fairness4.9Seasonal promotions and dynamic pricing4.10Workshop: Van Westendorp study for an ADAS bundle
5.1Hypothesis, primary metric and guardrails5.2Randomisation unit: vehicle, account or driver5.3Sample size and statistical power5.4Experiments with small fleets and network effects5.5Staggered tests by region and version5.6Safety and type-approval constraints on experimentation5.7Price experiments and ethical considerations5.8Pre-registration of the analysis plan5.9Interleaving and multi-armed bandits5.10Practical exercise: sample size calculation for a price test
6.1t-tests, chi-squared and Mann-Whitney tests6.2Variance reduction with CUPED6.3Multiple comparisons and Holm correction6.4Bayesian approach and probability of superiority6.5Heterogeneous effects by segment6.6Sample ratio mismatch and implementation errors6.7Novelty effects and stabilisation6.8Causal inference without randomisation6.9Bootstrap intervals for ratio metrics6.10Laboratory: analysing an A/B test in Python
7.1Experimentation platforms and feature flags7.2Decision committee and launch criteria7.3Documenting learnings7.4GDPR compliance in experimentation7.5Consent and transparency with the driver7.6Ethics of experimenting with assistance functions7.7Communicating results to management7.8Data-driven product culture7.9Experiment catalogue and meta-analysis7.10Case study: launch decision with ambiguous results
8.1Selecting the function and the hypothesis8.2Metric tree and guardrails8.3Instrumentation schema8.4Pricing strategy to be tested8.5Experimental design and sample size8.6Statistical analysis on simulated data8.7Decision recommendation8.8Staggered rollout plan after the test8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
The programme is delivered 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 each intake, so it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. You work with Python (pandas, statsmodels), SQL (BigQuery), Amplitude, Optimizely and Tableau, in free, academic or evaluation versions. Testing is studied through real procedures: designing an A/B experiment on a connected fleet, a willingness-to-pay study for an ADAS bundle, and cohort-based subscriber retention analysis. Assessment is continuous by module, plus the final project.
Standards covered: Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2023/2854 (Data Act), UN R156 (software updates), ISO 26262, ISO 21448 (SOTIF), UN R155 (cybersecurity), ISO/IEC 27001.
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. A university degree is not 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 (pandas, statsmodels), SQL (BigQuery), Amplitude, Optimizely and Tableau, 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 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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