What is a data dictionary and why is it useful?
PICTURE THIS: PYTHON TYPES
Simple meaning
A data dictionary documents each field's meaning, grain, allowed values, and null behavior.
WHY — EDA instead of guessing?
Why interviewers care about EDA:
who only read docs from people who shipped.
and tied to Data Science work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1A data dictionary documents
each field's meaning, grain, allowed values, and null behavior.
- 2It prevents mixing user-level
and session-level metrics or treating a flagged test user as production.
- 3When the warehouse lacks
one, building it is part of responsible EDA.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
A data dictionary documents each field's meaning, grain, allowed values, and null behavior. It prevents mixing user-level and session-level metrics or treating a flagged test user as production.