What's Data Science Reporting
summary
Argues that data science reports must show the model’s business impact not just ML metrics, and prescribes one central metric (example: money per passerby) with a daily automated comparison before/after model revenue so stakeholders see how much the model adds. Lists concrete components: measurability, format, channel, alerts, audience. Recommends simple channels and tools like Slack and Google Sheets or Python/R dashboards, automatic alerts on metric drift, and highlights surprises such as conversion sometimes being higher without the model.
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chapters (17)
- 0:00Data science reporting intro
- 1:16Why reports matter daily
- 2:40Flawed vending machine report
- 3:50What makes a report data science
- 6:09Effective reports Venn diagram
- 7:33Critical role of one metric
- 11:59Key report components
- 13:26Measuring data source reliability
- 15:57Choosing report format
- 18:28Best delivery channel
- 20:18Alerts for model health
- 21:23Reports for your audience
- 23:10Effective reporting tools
- 24:13Reports benefit future self
- 25:40Reports without model baselines
- 27:04Keeping reports engaging
- 27:47Resources and closing
