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Data Scientists estimate that they spend 80% of their time finding and cleaning data, according to a recent report.
“Tidy” data means that the “shape” of the data matches the assumptions required by the network traffic analysis algorithms. Unfortunately, few algorithms are designed to work with irregular time series that can occur in raw network traffic.
Dig into this white paper to uncover:
- Common actions for tidying data
- How you can reduce "tidying" complexity
- Algorithms and tasks for the 3 most common data types
- And more