Define a measurement, check its collection and explain the reading limits.
These sheets describe activities and teaching exercises. They do not constitute job offers, certifying courses or recruitment promises.
All job descriptions
Turn website measurements into decision-supporting observations, starting with the question, metric definitions and collection limits.
A metric does not automatically establish causation. Explain collection losses, scope changes and insufficient data. Tracking implementation requires appropriate review.
Present a question, tested measurement, proportionate finding and an action whose effect can be observed.
This is an editorial description of common activities. Job titles and scope vary across teams; it is not a certification framework.
W3C: organising accessibility responsibilities across a team
Distinguish consultation of form, attempt to send and request received.
Work on a copy or demo environment. Define the data used and the expected result before you begin. Keep your decisions and the difficulties encountered in order to be able to explain the process.
A measurement plan, collection checks and a table explained.
Indicators have a stable definition and collection anomalies are identifiable.
For each check, note the context, the action, the expected result and what has been observed. Distinguish a successful verification from a remaining hypothesis. Another person must be able to understand the deliverable without attending all its manufacture.
Use the Functional Validation BookAn increase may be due to a change in collection or public. The measure must specify what it does not allow to conclude.
Collaboration to explore: SEO and visibility.
Specify the sharing of responsibilities: who provides the information, who decides, who checks and maintains the result. A good handover retains the constraints and decisions, not just the final file.
Employment, trades and trainingIn-depth exercise: Analyze a small set of synthetic data on a research path. Define events and units before calculating an indicator. Introduce missing lines, duplicate and period variation. Compare the results before and after correction. Separate an event from a visitor and a volume of a rate. A numerical evolution does not prove its cause: formulate the possible explanations and indicate the verification that would make it possible to decide between them.
These criteria are a working basis to adapt to the context. For each, note the action taken, the expected result, the observed result and the information remaining to be confirmed.
Deliver the test game, transformations and formulas with the result. Explain the exclusions and their effect on totals. Another person must be able to redo the calculation and find the same number. Charts should keep units and periods visible. If a production data has not been checked, do not replace this absence with artificial precision.
Analysis starts with a possible decision. Compare ongoing indicators with a one-off study according to definition stability and the depth of the question.
An apparently precise dashboard can rest on ambiguous definitions. Include denominators, periods and limitations with important conclusions.
Present a conclusion, supporting data and checks that could change it. Link it to an action or further investigation.
It allows you to show an approach and its limits. It is neither a diploma, nor a certification, nor proof of a production experience.
Specify that this is an exercise, your role, constraints, decisions and checks. Use test data and media whose rights are established.
Indicators have a stable definition and collection anomalies are identifiable. Add the difficulties encountered and what remains to be verified rather than presenting absolute validation.
Identify comparable events and populations. Flag the discontinuity and avoid presenting the overall difference as a usage effect. If reliable comparison is impossible, explain the limit and rebuild a baseline with the new setup.
