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Statistics

Describe data honestly, measure uncertainty, run fair experiments and fit regressions you can explain. The reasoning layer under every analyst and data scientist role.

You do not need a statistics degree for analyst and data science roles. You need to know which number summarises your data honestly, how sure you can be about it, and when a difference is real.

Every lesson runs in Python with pandas, SciPy and statsmodels on real public datasets from the UCI Machine Learning Repository and New York City.

  1. 1Describing data honestlyMean vs median: central tendency and outliers12 min read
  2. 2Describing data honestlyVariance and standard deviation explained11 min read
  3. 3Describing data honestlyCorrelation vs causation: Pearson and Spearman11 min read
  4. 4Probability and distributionsProbability basics: rules and conditional probability11 min read
  5. 5Probability and distributionsBayes theorem explained with real examples11 min read
  6. 6Probability and distributionsBinomial and Poisson distributions with examples11 min read
  7. 7Probability and distributionsNormal distribution and z-scores12 min read

Statistics lessons

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