Data Analysis / Intermediate: Relationships and Uncertainty

Data Analysis β€” Intermediate: Relationships and Uncertainty

Combine tables, inspect patterns, and relate sampling design to uncertain estimates.

6 lessons Β· No account required

What you will learn

  • Audit SQL selection and join cardinality.
  • Build clear charts and exploratory comparisons.
  • Explain sampling limits and interval assumptions.

  1. SQL Query Logic and Honest Denominators

    Translate an analytical definition into row filters, grouped summaries, missing-value rules, and a reproducible result order.

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  2. Joins, Cardinality, and Changing Grain

    Combine tables without silently multiplying measures or dropping unmatched entities, and distinguish SQL null matching from documented pandas merge behavior.

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  3. Visualization Design, Bins, and Uncertainty

    Choose a graphic that exposes the comparison, preserves measurement meaning, and states uncertainty without turning visual styling into evidence.

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  4. Exploratory Relationships and Stratification

    Inspect paired patterns, subgroup mixtures, and unusual observations without confusing a descriptive association with a causal explanation.

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  5. Sampling Frames, Selection, and Weights

    Connect the population of interest to the records that can actually be selected, and distinguish sampling variability from coverage and response bias.

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  6. Standard Errors, Confidence Intervals, and Assumptions

    Explain what varies across samples, calculate a mean standard error under an explicit model, and communicate interval coverage without overstating what was learned.

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