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An interview prep path for data science loops. Built on statistics and probability, machine-learning concepts, and experimentation (A/B testing), grounded in SQL and Python, with the system-design and behavioral rounds product data scientists face. Lighter on algorithmic coding than a SWE path, heavier on inference and experiment design.
The backbone of every data science loop: distributions, hypothesis testing, p-values, confidence intervals, and the Bayesian reasoning interviewers probe. Anchor these first.
Bias-variance, regularization, the right evaluation metric for the problem, and the intuition behind the core algorithms - the conceptual ML screen most DS loops include.
Product data science runs on experiments. Master experiment design, sizing and power, and the pitfalls (peeking, novelty, SRM, network effects) interviewers love to test.
The daily tools. SQL for pulling and shaping data, Python for analysis. Pair these MCQs with the SQL Playground for hands-on query practice.
Senior DS loops add applied design (serving models, analytics pipelines) and behavioral rounds that screen for going deep and acting under ambiguity.
Every DS loop has a SQL screen, and it is the round candidates most often underestimate. Write these against real seeded data - aggregation and joins first, then the window functions that show up in cohort and retention questions.
SQL syntax and the statistical-vocabulary end of query tuning - useful to skim the morning of an interview.
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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