AI workflows for data science.
Using statistics and models on the company's data to forecast, segment or explain.
Who does this work
- Data scientist
- Statistician
- Forecasting analyst
Updated
Examples of the workflows identified
- Show the distribution behind an averageIllustrative
- Test the relationship behind an explanationIllustrative
- Check a predictive model before its scores are usedIllustrative
- Forecast a business measure from its historyIllustrative
- Segment customers from their purchase and usage historyIllustrative
The workflows identified
Marked illustrative: an example of the work, identified, with no workflow template yet.
Show the distribution behind an averageA percentile view with extreme cases and segment differences for a service or process review.Illustrative · not built yet
Test the relationship behind an explanationA correlation analysis with possible confounders and alternative explanations for the person attributing a result to another measure.Illustrative · not built yet
Check a predictive model before its scores are usedTests a scoring or forecasting model against held-out records, showing accuracy by segment, where it fails and what it was never trained on, for the person deciding whether to rely on it.Illustrative · not built yet
Forecast a business measure from its historyGives the analyst a forecast of a measure such as orders, calls or sign-ups with the method, a range, a back-test against past periods and the assumptions to accept or change.Illustrative · not built yet
Segment customers from their purchase and usage historyGroups customers by how they actually buy and use, describing each group with its size, value and defining traits, for the owner to name and decide what to do with.Illustrative · not built yet
Workflow templates
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