Foundations of Quantitative Analysis

Catalog description

This course emphasizes statistical ideas that inform the data life cycle, from generation of data through analysis and interpretation. The course begins with the science of how sampled data are used to study populations and the importance of understanding sources of bias. It addresses various aspects of statistical thinking such as multivariate data, descriptive statistics, and data visualization. It covers basic statistical inferential ideas of hypothesis testing and confidence intervals via a simulation-based approach that is particularly well-suited to data science. It lays the groundwork for later exploration of more advanced analytic techniques, e.g., prediction and classification models and algorithms, by introducing fundamental concepts of conditioning and Bayes' Theorem, basics of likelihood, and statistical optimization.