Introduces foundational data types (nominal, ordinal, interval, ratio) and descriptive statistics (mean, variance). Covers discrete/continuous probability, conditional probability, and Bayes’ theorem. Explores key distributions (normal, binomial, uniform) and their parameters.
Applied Learning Project
By completing this specialization, learners will be able to:
Classify data types (nominal, ordinal, interval, ratio) and identify their use cases.
Calculate descriptive statistics and create visualizations.
Define probability axioms, conditional probability, and Bayes’ theorem.
Calculate probabilities for discrete and continuous events using combinatorial rules.
Describe properties of key distributions (normal, binomial, uniform) and their parameters.
