Statistical Thinking & Predictive Modeling

Build the analytical skills that turn raw data into decisions leaders can act on. In this course, you will move through a complete decision-intelligence workflow — from exploring and summarizing data to running rigorous statistical tests, building production-ready predictive models, and communicating results to non-technical stakeholders. You will learn to generate descriptive statistics and visual summaries that reveal data quality issues before they distort your analysis. You will design and execute hypothesis tests, interpret p-values in business terms, and balance Type I and Type II error trade-offs with confidence. In the modeling track, you will build and cross-validate classification models using scikit-learn, handle class imbalance with techniques like SMOTE and class weights, and apply feature-selection methods — including RFE and LASSO — to balance accuracy with interpretability. The course culminates in an end-to-end customer lifetime value prediction project that integrates every skill into a portfolio-ready deliverable. Whether you are moving into a data analyst, business intelligence, or machine learning role, this course gives you the technical depth and communication skills to stand out.