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data-quality-basics

Data quality basics

Last reviewed Jun 1, 2026 Content v20260601
Track mode
server_script
Means
Server runner
Reading
~1 min
Level
beginner

This lesson

This lesson teaches Data quality basics: the data science mindset, methods, and communication habits behind evidence-based decisions.

Teams apply Data quality basics in every serious Data Science project—skipping it leaves blind spots in analysis and reviews.

You will apply Data quality basics in contexts like: Analytics teams, product experimentation, research labs, and ML-adjacent engineering in every data-driven company.

Read the narrative, run Python in the playground (stdlib snippets now; install Jupyter, pandas, and scikit-learn locally for full notebooks), and complete MCQs to lock in vocabulary.

When you can explain the previous lesson's ideas in your own words.

Garbage in, garbage out: track completeness, validity, consistency, timeliness, and uniqueness before modeling.

Quality dimensions

DimensionQuestion
CompletenessAre fields missing?
ValidityAre values allowed (age > 0)?
ConsistencyDo two tables agree on counts?
TimelinessIs data fresh enough?
UniquenessDuplicate keys?

Data dictionary

Maintain column definitions, units, and known bugs—onboarding analysts depend on it.

Important interview questions and answers

  1. Q: Completeness?
    A: Fraction of non-missing values per field.
  2. Q: Duplicate user rows?
    A: Uniqueness failure—inflates counts.

Self-check

  1. Name three quality dimensions.
  2. Why a data dictionary?

Tip: Start every project with a data dictionary row count check.

Interview prep

Completeness?

Fraction of non-missing values.

Duplicates?

Uniqueness failure—inflates counts.

Interview tip Lesson completion confidence

Can you explain this lesson in 30 seconds without reading notes?

Not saved yet.

Playground

Runs on the configured server runner (dev: npm run runner with LEARNING_RUNNER_ENABLED=true). Output appears below the editor.

Check yourself

Multiple choice — immediate feedback.

Discussion

Past discussion is visible to everyone. Only logged-in users can post comments and replies.

Starter discussion topics

  • Completeness check?
  • Data dictionary?

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