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Product Analyst
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Current availability
The subject-specific curriculum is authored but not yet published for completion. Learners may inspect its scope and learning design now. Start, grading, and completion remain unavailable until every listed gate passes.
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- reviewer identity, qualification, evidence, and approval date are required
What this course establishes
- Translate a user problem and product decision into a metric tree, explicit metric contracts, event specifications, and falsifiable learning questions
- Validate event data and calculate activation, funnel, cohort retention, reliability, and conversion measures with reproducible eligibility and denominators
- Evaluate observational and experimental evidence with uncertainty, guardrails, privacy, accessibility, user-research context, and an evidence-bounded recommendation
Entry knowledge
- Spreadsheet formulas or introductory SQL
- Percentages, proportions, averages, medians, and basic probability
- Basic familiarity with digital products, user journeys, events, sessions, and releases
Scope boundaries
- Uses fictional data only; it does not authorize production tracking, identity resolution, sensitive inference, surveillance, manipulative engagement, re-identification, or collection without a valid purpose and legal basis
- Does not replace user research, statistics/causal-inference expertise, privacy/security/legal review, accessibility research with disabled people, research ethics, engineering validation, or product decision authority
Fastest competency route
Competency evidence determines completion; no fastest-hour, job-placement, product-success, or certification guarantee applies. Prior experience accelerates the route only when the learner demonstrates every calculation, boundary, validation, decision artifact, and substantive revision.
- Pass diagnostics for user problems, metric trees, event contracts, eligible populations, activation, funnels, cohorts, experiments, guardrails, privacy, and accessibility
- Complete the supplied synthetic workspace-product analysis and decision portfolio
- Correct every instrumentation, duplicate, consent, missingness, denominator, causal, accessibility, ethical, and recommendation defect before final assessment
Full self-paced route
- Product questions, metric trees, and instrumentation contracts
- Data quality, activation, funnels, cohorts, and mixed-method diagnosis
- Experiments, uncertainty, guardrails, ethics, and product decisions
- Applied portfolio and final assessment
Lesson and assessment map
From user problem to metric tree and event contract
Design a minimal measurement system that connects user value and product decisions to governed events and metric definitions.
4 questions: recall, application, judgment, and error correction.
Data quality, activation, funnels, cohorts, and diagnosis
Produce auditable product measures while distinguishing descriptive signals from explanations and causal claims.
4 questions: recall, application, judgment, and error correction.
Experiments, guardrails, ethics, and product decisions
Evaluate a product change without causal overstatement, selective metrics, dark patterns, privacy harm, or inaccessible reporting.
4 questions: recall, application, judgment, and error correction.
Required applied assignment
Product measurement and evidence decision portfolio
Using only the supplied synthetic workspace-product events, move from user problem and decision through instrumentation, quality controls, activation/funnel/cohort analysis, experiment planning, accessibility, ethics, and an evidence-bounded recommendation. Do not use real user data or imply a production result.
- User-problem and decision brief, evidence inventory, metric tree, metric contracts, and explicit learning questions
- Versioned event/parameter specification with triggers, purpose-to-field justification, identity, consent, retention/deletion, retries, and QA matrix
- Immutable dataset copy/checksum, data dictionary, quality report, cleaning log, consent exclusion, deduplication, missingness, and user-level reconciliation
- Activation, funnel, Day-7 retention, latency, error, plan/channel/device, and control/treatment tables with explicit eligible IDs and denominators
- Accessible charts and data tables with labels, alt text, color-independent encoding, privacy thresholds, definitions, and data-through date
- Future experiment pre-registration, power rationale, assignment checks, outcomes, guardrails, stopping, analysis, qualitative research, privacy/consumer review, decision memo, monitoring/rollback, peer feedback, and substantive revision
Passing standard: 85 of 100 points, with substantive revision required.
Continue after this course
- SQL window functions, warehouses, semantic layers, event pipelines, experimentation platforms, and data contracts
- Probability, confidence intervals, power, sequential testing, multiple comparisons, variance reduction, quasi-experiments, and causal inference
- Survey design, qualitative research, accessibility research, behavioral science ethics, service design, and product strategy
- Privacy engineering, consent management, algorithmic accountability, and consumer-protection requirements by jurisdiction