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Descriptive vs Diagnostic vs Predictive Analytics
Descriptive, diagnostic and predictive analytics compared in practice: one real bike-sharing dataset taken through all three, from what happened, to why the worst day happened, to forecasting the next six months and beating a simple baseline, with the limits of each type and where prescriptive analytics takes over.
Product Analytics 101: Events, Funnels and Retention
Product analytics built on real data: how event data is structured, how to write a tracking plan, measuring return rates and time to a second order, finding early behaviour linked to retention, and how funnels, cohorts, segmentation and experiments fit together, plus the pitfalls that corrupt event data.
Time Intelligence in Analytics: Trends and Seasonality
Time intelligence for analysts, worked through two years of real daily bike-share data: why a 23% monthly fall was good news, smoothing daily noise with moving averages, year-to-date comparisons, seasonal indexes, weekday effects, and the partial-period trap that turned a 4.5% rise into a 48% fall.