Data Science Foundations with Python & SQL
Join tables without multiplying the story
Use keys, grouped queries and reconciliation checks to make SQL results trustworthy.
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- Ask a question the data can answer — Free preview
Define a decision, an observation and a useful denominator before writing code.
- Make invalid records visible — Free preview
Parse a small dataset into validated records without hiding rejected observations.
- Turn an analysis into a reusable function — Sign-in access
Separate a calculation from input/output and check its boundaries.
- Join tables without multiplying the story — Sign-in access
Use keys, grouped queries and reconciliation checks to make SQL results trustworthy.
- Describe variation before choosing a headline — Free preview
Distinguish typical values, unusual observations and uncertainty about the future.
- Design a chart that survives questions — Sign-in access
Choose clear units, honest scales and a comparison that answers the decision.
- Build a pipeline that fails informatively — Sign-in access
Connect validation, aggregation and a deterministic output with explicit failure behavior.
- Deliver the Community Meter decision pack — Sign-in access
Combine code, checks and a careful recommendation in a complete local project.