What are missing values in a dataset?
PICTURE THIS: DATABASE INDEX
Simple meaning
Missing values are observations that were not recorded, not applicable, or lost in a pipeline, often shown as NaN, None, or SQL NULL.
WHY — Missing Data instead of guessing?
Why interviewers care about Missing Data:
people 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:
- 1Missing values are observations
that were not recorded, not applicable, or lost in a pipeline, often shown as NaN, None, or SQL NULL.
- 2They are not the
same as zero unless the business defines them that way.
- 3Treating missing as zero
can invent fake revenue or fake engagement.
- 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
Missing values are observations that were not recorded, not applicable, or lost in a pipeline, often shown as NaN, None, or SQL NULL. They are not the same as zero unless the business defines them that way.