Data Science Foundations with Python & SQL

Join tables without multiplying the story

Use keys, grouped queries and reconciliation checks to make SQL results trustworthy.

Sign in to read this chapter

Use your existing SmartInterviewPro account to continue.

Log in to continue

The curriculum

  1. Ask a question the data can answer — Free preview

    Define a decision, an observation and a useful denominator before writing code.

  2. Make invalid records visible — Free preview

    Parse a small dataset into validated records without hiding rejected observations.

  3. Turn an analysis into a reusable function — Sign-in access

    Separate a calculation from input/output and check its boundaries.

  4. Join tables without multiplying the story — Sign-in access

    Use keys, grouped queries and reconciliation checks to make SQL results trustworthy.

  5. Describe variation before choosing a headline — Free preview

    Distinguish typical values, unusual observations and uncertainty about the future.

  6. Design a chart that survives questions — Sign-in access

    Choose clear units, honest scales and a comparison that answers the decision.

  7. Build a pipeline that fails informatively — Sign-in access

    Connect validation, aggregation and a deterministic output with explicit failure behavior.

  8. Deliver the Community Meter decision pack — Sign-in access

    Combine code, checks and a careful recommendation in a complete local project.