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An interview prep path for data and business analyst loops. SQL-first, reinforced with the applied statistics and experimentation analysts are expected to reason about, plus data fundamentals and the communication-heavy behavioral themes analyst interviews screen for. Minimal algorithmic coding.
Analyst interviews are SQL interviews. Drill joins, aggregation, window functions, and query reasoning here, then practice writing real queries in the SQL Playground.
Analysts must read data correctly: distributions, significance, confidence intervals, and the correlation-vs-causation traps that trip people up in case rounds.
Reasoning about A/B tests and metrics is a common analyst case. Cover experiment design, picking metrics, and the standard pitfalls.
How data is modeled, moved, and warehoused - the pipeline context behind the tables you query.
Analyst loops screen hard for turning data into a clear, actionable story for non-technical stakeholders. Bring examples with concrete business impact.
The MCQs above test whether you can read SQL. These make you write it. Each one runs against a seeded database and is graded on the result set - start with the free filtering and aggregation problems, then work into joins and window functions, which is where analyst screens actually separate people.
Two references worth having in a second tab: the syntax you will forget under pressure, and the query-tuning vocabulary that turns 'it is slow' into a diagnosis an interviewer respects.
21 role-targeted paths are live, from new-grad and backend through SRE, security, data and engineering management. If you have a role you want covered, let us know.
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