Build a solid foundation in statistics and probability. Learn to describe data, calculate probabilities, understand distributions, perform hypothesis tests, and build regression models. Includes Python computation exercises for hands-on practice with real data analysis.
Summarize and describe datasets with measures of center, spread, and position
Understand probability theory including sample spaces, conditional probability, and Bayes' theorem
Study binomial, normal, and Poisson distributions along with expected value and the central limit theorem
Learn sampling methods, confidence intervals, hypothesis testing, p-values, and t-tests
Study scatter plots, correlation coefficients, linear regression, R-squared, and residual analysis
Apply all statistics and probability concepts in a comprehensive analysis project
A 20-question assessment covering descriptive statistics, probability, distributions, inference, and regression
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