Applied Statistics: Categorical Data, Non-Parametric Tests, and Dimensionality Reduction

Building a solid foundation in statistics requires moving beyond simple summary metrics into flexible observational techniques. Understanding how to evaluate categorical variables and handle complex, high-dimensional datasets is critical for modern data analysis. This written course guides you through core statistical methods, from contingency tables to feature reduction algorithms. You will gain a clear conceptual understanding of how to select the right test for different data types and interpret the results effectively. What you will learn: Understand foundational statistical terms, measurement scales, and hypothesis testing principles; Analyze nominal and categorical data using contingency tables and chi-square tests; Apply non-parametric testing methods when standard distributional assumptions fail; Practice dimensionality reduction concepts such as Principal Component Analysis to streamline complex features; Evaluate non-parametric correlation measures for non-linear relationships; Interpret analytical findings to guide data-driven decision-making. The course begins with essential definitions and foundational theory before guiding you through structured text lessons and practical written scenarios. It is tailored for beginners and data enthusiasts who want to build confidence in statistical analysis with no advanced math prerequisites required. Begin reading today to expand your analytical toolkit with essential statistical methods.