Exam-Journey Analytics
Anonymized community study data. Hours logged versus pass outcomes, sitting-order patterns, and where candidates actually lose their time.
Live Rggplot2Survey design
Why guess when the community has data?
Every exam forum runs on folklore: “you need 400 hours”, “never sit P and FM in the same window”. Folklore is untested data. So I started collecting the community’s actual journeys (anonymized study logs, sitting orders, and outcomes) to see which advice survives contact with evidence.
Early patterns worth knowing
Three findings keep recurring: consistency beats volume (candidates studying 10 steady hours weekly outperform equal-total-hours crammers), the final-month simulation phase is the most-skipped and most pass-correlated behavior, and failed first sittings most often trace to entering the exam without a single full timed practice run. None of this is causal proof: it’s the community’s honest telemetry, visualized.
How it’s built
A recurring anonymous survey feeds an R pipeline: cleaning and de-identification, then ggplot2 visual summaries that go back to the community in newsletter issues. The dataset grows every cycle, and so does the reliability of the patterns.
The takeaway
Want to strengthen the dataset? Contribute your journey anonymously via the contact page: every entry sharpens the picture for the next candidate.