Math Methods Data Analysis

Catalog description

This course prepares the student for data analysis. Topics discussed include probability axioms, counting methods, random variables, probability distributions and densities, expected value, variance, correlation, conditional distributions (mean and variance), special probability models, law of large numbers, central limit theorem, statistical estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, nonparametric methods, ANOVA, and least squares. Applications for data science problems are discussed.