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RED SAGSkills

Hard Data Scientist questions

32 questions · 20 points each for a correct first answer.

  1. 1.Checking results every dayhard
  2. 2.Coaching the worst storeshard
  3. 3.Unexpected group sizeshard
  4. 4.Using pre-experiment datahard
  5. 5.Difference-in-differences assumptionhard
  6. 6.Coefficients that keep flippinghard
  7. 7.Feature users retain betterhard
  8. 8.Click-through rate standard errorshard
  9. 9.Propensity scores near 0 and 1hard
  10. 10.Key assumption of an instrumenthard
  11. 11.A link that only appears in a subsethard
  12. 12.Error spread grows with the predictionhard
  13. 13.Direction of omitted variable biashard
  14. 14.AUC after squaring the scoreshard
  15. 15.Precision when prevalence dropshard
  16. 16.Oversampling before cross-validationhard
  17. 17.Target encoding without leakagehard
  18. 18.NDCG at 3 for a rankinghard
  19. 19.Treated drivers take control rideshard
  20. 20.Benjamini-Hochberg discoverieshard
  21. 21.Noisy feature, flatter slopehard
  22. 22.Back-transforming log predictionshard
  23. 23.Effect with an interaction termhard
  24. 24.From log-odds to probabilityhard
  25. 25.Shape after NumPy broadcastinghard
  26. 26.Rolling mean at the edgeshard
  27. 27.LEFT JOIN that acts like INNER JOINhard
  28. 28.Average lifetime with active customershard
  29. 29.Great backtest, poor live forecasthard
  30. 30.When the bootstrap breaks downhard
  31. 31.Optimising time on sitehard
  32. 32.Big effect in one segmenthard