Prompt
You have a healthcare claims table and need to show you can analyze it for a data analyst interview. What tiny schema would you practice with, and what questions prove you understand the data grain?
Answer shape
- Say the table grain first: one row per submitted claim, or one row per claim line if the dataset is line-item level.
- Name the core identifiers: claim, patient, provider, service date, diagnosis, procedure, status, billed amount, and paid amount.
- Practice grouped metrics before complex modeling: rejection rate, paid amount, duplicate claims, and payment lag.
- Be truthful about experience. Practicing on sample claims data is not the same as handling production healthcare data.
Build the tiny dataset.
Create a small fake table with these fields:
claim_id,patient_id,provider_idservice_date,paid_date,diagnosis_code,procedure_codeclaim_status,billed_amount,paid_amount
Then create 20 to 50 rows with a few rejected claims, repeated patients, repeated providers, missing paid dates, and one duplicate-looking claim.
Six practice questions
- Claims per month by status.
- Rejection rate by provider.
- Average paid amount by procedure code.
- Duplicate claims for the same patient, date, provider, and procedure.
- Days between service date and paid date.
- Top diagnosis codes by paid amount.
What to say out loud
"I am treating this as one row per claim. Before trusting any rejection-rate metric, I would check whether the table is claim-level or claim-line-level, whether status can change over time, and whether paid date is missing because the claim is still open or because the data is incomplete."
Common miss
Do not say "I know healthcare datasets" if you only practiced SQL on fake claims rows. A stronger answer is: "I have not handled production claims data, but I understand the shape I would expect and the checks I would run before trusting the result."
Move from a claims query to interview judgment.
The Product Analytics packet adds SQL follow-ups, metric debugging, product cases, and recommendation practice so your answer does not stop at a query that merely runs.
Direct purchase note
This is the public $59 self-guided packet path. If a coaching or mock-interview session already gave you access, use that access instead of buying the same packet again.
Public checkout is for self-guided packet buyers.
If a coaching, mock-interview, or private session already gave you packet access, use that private access and do not buy the same packet again. The public $59 checkout is for candidates buying the self-guided Product Analytics or Leadership packet directly.
Use Product Analytics for SQL, metrics, experiments, and product cases. Use Leadership for conflict, failure, ambiguity, influence, and final-round stories.
Run one timed rep before checkout.
Pick the risk you can fix today. Do the rep, then buy the packet only if it matches the round in front of you.
- SQL or OA: say the row grain first, solve one baseline query, then name the edge case that could break it.
- Product case: start with the decision, then give the metric view, segment, risk, and recommendation.
- Leadership loop: choose one conflict, failure, or ambiguity story and name the operating change you owned.
Last-mile check: pick the packet for the round you could lose.
Use the rep on this page first. If the weak spot is SQL, metrics, experiments, or product cases, get Product Analytics. If the weak spot is conflict, ambiguity, or final-round stories, get Leadership. Public checkout is $59 and separate from any private session access.