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Computer science
Use descriptive statistics and uncertainty to interpret data responsibly.
values = [2, 4, 4, 6]
mean = sum(values) / len(values)COURSE CURRICULUM
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Learn how statistical questions, populations, and samples works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how variables, measurement scales, and study structure works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how parameters, estimates, and sampling variability works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how distributions, moments, categories, and missingness works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how probability rules, expectation, and transformations works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how conditional probability, bayes rules, and decisions works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how sampling distributions, simulation, and resampling works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how estimators, likelihood, and model functions works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how tables, grouped data, correlations, and covariance works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how summaries, visualization, and responsible communication works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how bias, confounding, uncertainty, and invalid assumptions works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how reproducible analyses, preregistration, and data provenance works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how regression, experimental design, and causal limits works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how files, survey data, time series, and data collection works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.
Learn how hypothesis tests, intervals, and diagnostic checks works in Statistics, why the underlying model matters, and how to apply it in a small program without hiding important trade-offs.