Analytics Challenges
Most practice sites test whether you can write SQL. These test whether you can find the real story in messy data and convince someone to act on it — which is the part interviews actually fail people on.
Take a real business problem
Not a puzzle with one clean answer. A messy dataset, a stakeholder with a theory, and a deadline.
Submit your analysis
A public GitHub repo, your answers to the objective questions, and a recommendation short enough for a board to read.
Get scored on both
40% on whether your numbers are right, 60% on whether your analysis would survive a real stakeholder.
The Stickiness Cliff
DAU/MAU fell from 35% to 28% the week the new release shipped, and growth has called an engagement emergency. Rebuild the metric from the raw events, find out who was in the numerator, and decide whether anything happened to real users at all.
The Pipeline Mirage
Win rate has fallen from about 31% to 10% since the new SDR team started, and the VP of Sales wants it disbanded. Check what the dashboard is dividing by, and whether the two teams are being compared at the same stage, before six people lose their jobs.
The Power User Myth
Users who create a Goal retain six times better, so growth wants to make it a required onboarding step. Work out whether the Goal is what keeps them — or whether staying is what lets them set one — then make the call.
The Last-Touch Trap
A brand's dashboard runs on last-touch attribution, and Paid Social barely registers — so growth wants to cut it. Check whether last-touch is hiding what Paid Social actually does, then make the call on the budget.
The Winning Variant
An A/B test on a new pricing screen shows the new variant converting far better, and growth wants to ship it to everyone. Check whether the win is real before it goes live — and give the call on shipping it.
The Churn Spike
Monthly churn nearly tripled in June at a subscription app, a week after a price rise. Product wants to roll the price back. Work out whether the price is actually what happened — and say whether to roll it back.
Diagnose the E-commerce Revenue Drop
The dashboard shows September revenue down about 15% at a home-goods retailer, and the team blames soft demand. Check whether that holds — and whether 15% is even the real number — then make the call on the paid social budget.
Why these are different
Every dataset here is generated with deliberate mess in it — duplicate rows from a retry bug, refunds recorded separately, timestamps in the wrong timezone, inconsistent codes. That is not to be annoying. It is because the gap between a junior analyst and a good one is almost never SQL syntax. It is noticing that the number looks wrong before the stakeholder does.
