Statistical Methods and Data Analysis

Build strong statistical and data analysis skills to turn complex datasets into clear, actionable insights. This course is designed for learners interested in data analysis, statistics, research, economics, public policy, business, and related fields who want to strengthen their quantitative decision-making skills. Start with data visualization and learn how to identify different forms of data and choose appropriate charts and visualization techniques, including plots for univariate and bivariate data and approaches for big data. Develop a solid foundation in descriptive statistics, probability distributions, sampling, and statistical inference. Learn about measures of central tendency and dispersion, random variables, binomial and normal distributions, the Central Limit Theorem, and confidence intervals. Progress to hypothesis testing, experiments, correlation, and regression analysis. Gain knowledge of t-tests, ANOVA, OLS regression, measures of association, and the assumptions behind reliable statistical models. Take your skills further with advanced regression concepts, including reverse causality, heteroscedasticity, serial correlation, decision trees, cross-validation, random forests, variable selection, and penalized regression. Enroll now and develop the statistical thinking needed to analyze data, interpret evidence, and make informed decisions.