Learning path
Statistics
Statistics and probability for data science: averages, Bayes, the normal curve, confidence intervals, p-values and correlation, with live animations. Part of the Data Scientist premium track.
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1
Mean, Median, Spread and the Five-Number Summary
Describe any column of numbers in five seconds: mean vs median, range, IQR, standard deviation and the five-number summary, with Python.
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2
Probability, Conditional Probability and Bayes’ Theorem
Probability from counting, "given that" questions, independence, and Bayes' theorem explained with a medical test and a spam filter, checked by simulation in Python.
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3
Distributions: Binomial, Normal and the Central Limit Theorem
The shapes data takes. Binomial for counts of successes, the normal bell curve and the 68-95-99.7 rule, z-scores, and why averages are always bell-shaped.
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4
Sampling and Confidence Intervals
Why a sample of 400 can describe a million people, what a 95% confidence interval really means, and how sample size controls your margin of error.
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5
Hypothesis Tests: z, t, Chi-Square and p-values
Decide with data instead of gut feeling. Null hypothesis, p-values, the t-test for A/B tests, chi-square for categories, and the mistakes to avoid.
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6
Correlation vs Causation
Measure how two variables move together with Pearson and Spearman correlation, read a correlation matrix, and learn why correlation alone never proves cause.