Describe key components for EDA on three years of daily user sign-ups
This tests time-series decomposition intuition. A strong answer covers trend, seasonality, and noise via plots, autocorrelation, and calendar effects, plus checks for missing days and outliers. Red flag: jumping to forecast models before validating structure.
This tests whether you can systematically dissect a daily time series before building models. A strong answer walks through four layers in order: data quality checks for missing dates and outliers; visual inspection for overall trend and level shifts; seasonal decomposition into weekly, monthly, and holiday patterns; and autocorrelation or lag analysis to detect cyclic behavior. Red flag: skipping structural exploration and immediately naming forecasting libraries like Prophet or ARIMA without explaining what patterns you expect to find.
Read the original → Wikipedia: Time series
- #time series
- #eda
- #analytics
- #data quality
- #seasonality
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